From be1b10c98bf5c4401476da51d7ec34496f2ebb79 Mon Sep 17 00:00:00 2001 From: GuopingWin <731061720@qq.com> Date: Sun, 12 Mar 2023 14:20:58 +0800 Subject: [PATCH 1/4] add homework --- my_homework/.gitignore | 6 + my_homework/README.md | 15 + my_homework/ch2/homework2.ipynb | 307 ++++++++++++ my_homework/ch2/homework2_3.py | 56 +++ my_homework/ch3/homework3.ipynb | 459 ++++++++++++++++++ my_homework/ch3/homework3_2.py | 68 +++ my_homework/ch4/home_analyse.ipynb | 113 +++++ my_homework/ch4/homework4.py | 55 +++ my_homework/ch4/ppof_ch4_code_p1.py | 326 +++++++++++++ .../files/conda-environment.yaml | 0 .../files/config.yaml | 29 ++ .../files/output.log | 4 + .../files/requirements.txt | 179 +++++++ .../files/wandb-metadata.json | 50 ++ .../files/wandb-summary.json | 1 + .../logs/debug-internal.log | 341 +++++++++++++ .../logs/debug.log | 27 ++ 17 files changed, 2036 insertions(+) create mode 100644 my_homework/.gitignore create mode 100644 my_homework/README.md create mode 100644 my_homework/ch2/homework2.ipynb create mode 100644 my_homework/ch2/homework2_3.py create mode 100644 my_homework/ch3/homework3.ipynb create mode 100644 my_homework/ch3/homework3_2.py create mode 100644 my_homework/ch4/home_analyse.ipynb create mode 100644 my_homework/ch4/homework4.py create mode 100644 my_homework/ch4/ppof_ch4_code_p1.py create mode 100644 my_homework/ch4/run-20230312_134251-g1s73ewz/files/conda-environment.yaml create mode 100644 my_homework/ch4/run-20230312_134251-g1s73ewz/files/config.yaml create mode 100644 my_homework/ch4/run-20230312_134251-g1s73ewz/files/output.log create mode 100644 my_homework/ch4/run-20230312_134251-g1s73ewz/files/requirements.txt create mode 100644 my_homework/ch4/run-20230312_134251-g1s73ewz/files/wandb-metadata.json create mode 100644 my_homework/ch4/run-20230312_134251-g1s73ewz/files/wandb-summary.json create mode 100644 my_homework/ch4/run-20230312_134251-g1s73ewz/logs/debug-internal.log create mode 100644 my_homework/ch4/run-20230312_134251-g1s73ewz/logs/debug.log diff --git a/my_homework/.gitignore b/my_homework/.gitignore new file mode 100644 index 0000000..571a1f5 --- /dev/null +++ b/my_homework/.gitignore @@ -0,0 +1,6 @@ +.idea +DI-engine +ding_study +wandb +output +.data \ No newline at end of file diff --git a/my_homework/README.md b/my_homework/README.md new file mode 100644 index 0000000..1cd000f --- /dev/null +++ b/my_homework/README.md @@ -0,0 +1,15 @@ +# PPOxFamily +this repository is to learn family algorithms of PPO. + +more details in: [https://github.com/opendilab/PPOxFamily](https://github.com/opendilab/PPOxFamily) + +
+ +
+ +## TODO +- [x] finish all the code in class one to four +- [x] correct notes +- [x] theorical inference processes +- [ ] 🆕class five + diff --git a/my_homework/ch2/homework2.ipynb b/my_homework/ch2/homework2.ipynb new file mode 100644 index 0000000..1ddb8ea --- /dev/null +++ b/my_homework/ch2/homework2.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. 重参数化技巧" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "一开始写错了,以为是对`eps`求导,变成了\n", + "\n", + "![](demo/reparam_gradient.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "naive grad variance: [5.74129343 1.00604956 0.17195452 0.08618732 0.01622461]\n", + "reparameterization grad variance: [0.39380306 0.03859908 0.00890374 0.00394094 0.00082136]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "def naive_grad(x, mu):\n", + " # the naive gradient to mu\n", + " # \\nabla_\\mu \\mathbb{E}_q[x^2] = \\mathbb{E}_q[x^2(x-\\mu)]\n", + " return np.mean(x ** 2 * (x - mu))\n", + " \n", + "def reparam_grad(eps, mu):\n", + " #### You need to finish the reparameterization gradient to mu here ####\n", + " # \\nabla_\\mu \\mathbb{E}_q[x^2] = \\mathbb{E}_q[2*(\\varepsilon+\\mu)]\n", + "\t# return np.mean((2-eps)*(mu+eps))\n", + " return np.mean(2*(mu+eps))\n", + " \n", + "def main():\n", + " data_size_list = [10, 100, 500, 1000, 5000]\n", + " sample_num = 100\n", + " mu, sigma = 2.0, 1.0\n", + " # variance of the gradient to mu\n", + " var1 = np.zeros(len(data_size_list))\n", + " var2 = np.zeros(len(data_size_list))\n", + "\n", + " for i, data_size in enumerate(data_size_list):\n", + " estimation1 = np.zeros(sample_num)\n", + " estimation2 = np.zeros(sample_num)\n", + " \n", + " for n in range(sample_num):\n", + " # 1.naive method\n", + " x = np.random.normal(mu, sigma, size=(data_size, ))\n", + " estimation1[n] = naive_grad(x, mu)\n", + " \n", + " # 2.reparameterization method\n", + " eps = np.random.normal(0.0, 1.0, size=(data_size, ))\n", + " x = eps * sigma + mu\n", + " estimation2[n] = reparam_grad(eps, mu)\n", + " var1[i] = np.var(estimation1)\n", + " var2[i] = np.var(estimation2)\n", + " \n", + " print('naive grad variance: {}'.format(var1))\n", + " print('reparameterization grad variance: {}'.format(var2))\n", + " # plot figure\n", + " index = [_ for _ in range(len(data_size_list))]\n", + " plt.plot(index, var1)\n", + " plt.plot(index, var2)\n", + " plt.xticks(index, data_size_list)\n", + " plt.legend(['naive', 'reparam'])\n", + " plt.savefig('demo/reparam.png')\n", + " plt.show()\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. 连续动作空间(小人越悬崖)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch.distributions import MultivariateNormal\n", + "import torch.nn.functional as F " + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "class LittleHuman(nn.Module):\n", + "\n", + " def __init__(self, lr = 0.01) -> None:\n", + " super().__init__()\n", + "\n", + " self.lr = lr\n", + "\n", + " self.param1 = nn.Parameter(torch.zeros(1), requires_grad=True)\n", + " self.param2 = nn.Parameter(torch.zeros(1), requires_grad=True)\n", + " # sigma_theta = \n", + " # sigma_v = \n", + "\n", + " # 只有 torch 本身的方法才会计算计算图\n", + " self.sigma = torch.concat([torch.exp(self.param1) * torch.pi / 2., torch.exp(self.param2) * 10.])\n", + " self.hidden = nn.Sequential(\n", + " nn.Linear(2, 2),\n", + " nn.Tanh()\n", + " )\n", + "\n", + "\n", + " def _get_mu(self, x):\n", + " x = 0.5 * (x + 1)\n", + " x[..., 0] *= 0.5 * torch.pi\n", + " x[..., 1] *= 10.\n", + " \n", + " return x\n", + "\n", + " def policy(self, d):\n", + " x = self.hidden(d)\n", + " mu = self._get_mu(x)\n", + " dist = MultivariateNormal(mu, scale_tril=torch.diag(self.sigma))\n", + "\n", + " return dist\n", + " \n", + " def success(self, a, d) -> bool:\n", + " theta, v = torch.split(a, 1, -1)\n", + " vx = v * torch.cos(theta)\n", + " vy = v * torch.cos(theta)\n", + "\n", + " d1, d2 = torch.split(a, 1, -1)\n", + " t = d1 / vx\n", + " return vy*t - 0.5*9.8*t*t >= d2\n", + "\n", + " def reward(self, action, success):\n", + " v = action[...,-1]\n", + " return success * (100 - v**2)\n", + "\n", + " def update(self, reward, prob):\n", + " \n", + " # print(reward * prob)\n", + " (reward * prob).mean().backward()\n", + " # print(self.param1.grad)\n", + "\n", + " with torch.no_grad():\n", + " self.param1 += self.lr * self.param1.grad\n", + " self.param2 += self.lr * self.param2.grad\n", + "\n", + " print(self.hidden[0].weight.grad, self.hidden[0].bias.grad)\n", + " print(self.param1.grad, self.param2.grad)\n", + "\n", + " def run(self, d):\n", + " dist = self.policy(d)\n", + " action = dist.sample()\n", + " success = self.success(action, d)\n", + " with torch.no_grad():\n", + " reward = self.reward(action, success)\n", + " prob = dist.log_prob(action)\n", + " grad = self.update(reward, prob)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[0., 0.],\n", + " [0., 0.]]) tensor([0., 0.])\n", + "tensor([0.]) tensor([0.])\n" + ] + } + ], + "source": [ + "lm = LittleHuman()\n", + "lm.train()\n", + "lm.run(torch.tensor([[[0.2, 0.2]]]))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 题⽬3(应⽤实践)\n", + "在课程第⼆讲(解构复杂动作空间)⼏个应⽤中任选⼀个\n", + "\n", + "- ⽕箭回收(离散动作空间)\n", + "- ⽆⼈机姿态控制(连续动作空间)\n", + "- 导航控制(混合动作空间)\n", + "\n", + "根据课程组给出的⽰例代码,训练得到相应的智能体。最终提交需要上传相关训练代码、⽇志截图或\n", + "最终所得的智能体效果视频(replay),具体样式可以参考第⼆讲的⽰例ISSUE。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Please install latest DI-engine's main branch first\n", + "# And we will release DI-engine v0.4.6 version with stable and tuned configuration of these demos.\n", + "from ding.bonus import PPOF\n", + "\n", + "\n", + "def lunarlander_discrete():\n", + " # Please install lunarlander env first, `pip3 install box2d`\n", + " agent = PPOF(env='lunarlander_discrete', exp_name='./lunarlander_discrete_demo')\n", + " agent.train(step=int(1e5))\n", + " # Classic RL interaction loop and save replay video\n", + " agent.deploy(enable_save_replay=True)\n", + "\n", + "\n", + "def lunarlander_continuous():\n", + " # Please install lunarlander env first, `pip3 install box2d`\n", + " agent = PPOF(env='lunarlander_continuous', exp_name='./lunarlander_continuous_demo', seed=314)\n", + " agent.train(step=int(1e5))\n", + " # Batch (Vectorized) evaluation\n", + " agent.batch_evaluate(env_num=4, n_evaluator_episode=8)\n", + "\n", + "\n", + "def rocket_landing():\n", + " # Please install rocket env first, `pip3 install git+https://github.com/nighood/rocket-recycling@master#egg=rocket_recycling`\n", + " agent = PPOF(env='rocket_landing', exp_name='./rocket_landing_demo')\n", + " agent.train(step=int(5e6), context='spawn')\n", + "\n", + "\n", + "def drone_fly():\n", + " # Please install gym_pybullet_drones env first, `pip3 install git+https://github.com/zjowowen/gym-pybullet-drones@master`\n", + " agent = PPOF(env='drone_fly', exp_name='./drone_fly_demo')\n", + " agent.train(step=int(5e6))\n", + "\n", + "\n", + "def hybrid_moving():\n", + " # Please install gym_hybrid env first, refer to the doc `https://di-engine-docs.readthedocs.io/zh_CN/latest/13_envs/gym_hybrid_zh.html`\n", + " agent = PPOF(env='hybrid_moving', exp_name='./hybrid_moving_demo')\n", + " agent.train(step=int(5e6))\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " # You can select and run your favorite demo\n", + " # lunarlander_discrete()\n", + " # lunarlander_continuous()\n", + " # rocket_landing()\n", + " # drone_fly()\n", + " hybrid_moving()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "torch11", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/my_homework/ch2/homework2_3.py b/my_homework/ch2/homework2_3.py new file mode 100644 index 0000000..a9c1187 --- /dev/null +++ b/my_homework/ch2/homework2_3.py @@ -0,0 +1,56 @@ +# Please install latest DI-engine's main branch first +# And we will release DI-engine v0.4.6 version with stable and tuned configuration of these demos. +from ding.bonus import PPOF + + +def lunarlander_discrete(): + # Please install lunarlander env first, `pip3 install box2d` + agent = PPOF(env='lunarlander_discrete', exp_name='./lunarlander_discrete_demo') + agent.train(step=int(1e5)) + # Classic RL interaction loop and save replay video + agent.deploy(enable_save_replay=True) + + +def lunarlander_continuous(): + # Please install lunarlander env first, `pip3 install box2d` + agent = PPOF(env='lunarlander_continuous', exp_name='./lunarlander_continuous_demo', seed=314) + agent.train(step=int(1e5)) + # Batch (Vectorized) evaluation + agent.batch_evaluate(env_num=4, n_evaluator_episode=8) + + +def rocket_landing(): + # Please install rocket env first, `pip3 install git+https://github.com/nighood/rocket-recycling@master#egg=rocket_recycling` + agent = PPOF(env='rocket_landing', exp_name='./rocket_landing_demo') + agent.train(step=int(5e6), context='spawn') + +def rocket_landing_deploy(): + # Please install rocket env first, `pip3 install git+https://github.com/nighood/rocket-recycling@master#egg=rocket_recycling` + agent = PPOF(env='rocket_landing', exp_name='./rocket_landing_demo') + agent.deploy(ckpt_path="rocket_landing_demo\ckpt\iteration_38400.pth.tar", enable_save_replay=True) + + +def drone_fly(): + # Please install gym_pybullet_drones env first, `pip3 install git+https://github.com/zjowowen/gym-pybullet-drones@master` + agent = PPOF(env='drone_fly', exp_name='./drone_fly_demo') + agent.train(step=int(5e6)) + + +def hybrid_moving(): + # Please install gym_hybrid env first, refer to the doc `https://di-engine-docs.readthedocs.io/zh_CN/latest/13_envs/gym_hybrid_zh.html` + agent = PPOF(env='hybrid_moving', exp_name='./hybrid_moving_demo') + agent.train(step=int(5e6)) + +def hybrid_moving_deploy(): + agent = PPOF(env='hybrid_moving', exp_name='./hybrid_moving_demo') + agent.deploy(enable_save_replay=True) + +if __name__ == "__main__": + # You can select and run your favorite demo + # lunarlander_discrete() + # lunarlander_continuous() + # rocket_landing() + rocket_landing_deploy() + # drone_fly() + # hybrid_moving() + # hybrid_moving_deploy() diff --git a/my_homework/ch3/homework3.ipynb b/my_homework/ch3/homework3.ipynb new file mode 100644 index 0000000..866dfbf --- /dev/null +++ b/my_homework/ch3/homework3.ipynb @@ -0,0 +1,459 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 代码实践题目" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 题目1(奇偶数预测问题)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "class OddAndEven(nn.Module):\n", + " def __init__(self) -> None:\n", + " super().__init__()\n", + "\n", + " # directly input data\n", + " self.net_direct = nn.Sequential(\n", + " nn.Linear(10, 128),\n", + " nn.ReLU(True),\n", + " nn.Linear(128, 2),\n", + " )\n", + "\n", + " # turn input to binary format,binary encoding length = 9\n", + " self.net_binary = nn.Sequential(\n", + " nn.Linear(10, 128),\n", + " nn.ReLU(True),\n", + " nn.Linear(128, 2),\n", + " )\n", + "\n", + " # use trigonometric function to encode,length = 9\n", + " self.net_trigo = nn.Sequential(\n", + " nn.Linear(10, 128),\n", + " nn.ReLU(True),\n", + " nn.Linear(128, 2),\n", + " )\n", + "\n", + " self.sigmoid = nn.Sigmoid()\n", + "\n", + "\n", + " def _binary_encode(self, x):\n", + " \n", + " def num2binary(x):\n", + " if x == 0:\n", + " return []\n", + " else:\n", + " temp = x % 2\n", + " l = num2binary(x//2)\n", + " l.append(temp)\n", + " return l\n", + "\n", + " binary_encode = []\n", + " for i in range(x.size(0)):\n", + " sub_1 = num2binary(x[i])\n", + " while len(sub_1) < 10: sub_1.insert(0, 0)\n", + " binary_encode.append(sub_1)\n", + "\n", + " return torch.tensor(binary_encode, dtype=torch.float)\n", + " \n", + " \n", + " def _trigo_encode(self, x):\n", + " # div_term = torch.exp(torch.arange(0, 10, 2) *\n", + " # -(math.log(10000.0) / 10)).to(x.device)\n", + " trigo_encode = torch.zeros(x.size(0), 10).to(x.device)\n", + " # trigo_encode[..., 0::2] = torch.sin(x * div_term)\n", + " # trigo_encode[..., 1::2] = torch.cos(x * div_term)\n", + " trigo_encode[..., 0::2] = torch.sin(x).repeat(1, 5)\n", + " trigo_encode[..., 1::2] = torch.cos(x).repeat(1, 5)\n", + "\n", + "\n", + " return trigo_encode.to(x.device)\n", + " \n", + " def forward(self, x):\n", + " binary_encode = self._binary_encode(x).to(x.device)\n", + " trigo_encode = self._trigo_encode(x).to(x.device)\n", + " origin_encode = x.repeat(1, 10).to(x.device)\n", + "\n", + " # binary_result = torch.argmax(self.sigmoid(self.net_direct(binary_encode)), dim=-1)\n", + " # trigo_result = torch.argmax(self.sigmoid(self.net_direct(trigo_encode)), dim=-1)\n", + " # origin_result = torch.argmax(self.sigmoid(self.net_direct(origin_encode)), dim=-1)\n", + " binary_result = self.net_binary(binary_encode)\n", + " trigo_result = self.net_trigo(trigo_encode)\n", + " origin_result = self.net_direct(origin_encode)\n", + "\n", + " return binary_result, trigo_result, origin_result\n" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " batch_size = 64\n", + " epochs = 200\n", + " net = OddAndEven()\n", + " net.to(device='cuda')\n", + " net.train()\n", + " loss_fn = nn.CrossEntropyLoss()\n", + "\n", + " optimizer = torch.optim.Adam(net.parameters(),lr=1e-3)\n", + " \n", + " for epoch in range(epochs):\n", + " data = torch.randint(0, 1000, size=(batch_size, 1), dtype=torch.float).cuda()\n", + " label = (data % 2 == 0).long().cuda().squeeze()\n", + " b, t, o = net(data)\n", + " l1 = loss_fn(b, label)\n", + " l2 = loss_fn(t, label)\n", + " l3 = loss_fn(o, label)\n", + " \n", + " l1.backward()\n", + " l2.backward()\n", + " l3.backward()\n", + " optimizer.step()\n", + " optimizer.zero_grad()\n", + "\n", + " print(f\"epoch:[{epoch:>3d}/{epochs:>3d}] binary_loss:{l1} triangle_loss:{l2} origin_loss:{l3}\")\n", + " \n", + " print('----------------------------- Test -----------------------------' )\n", + " data = torch.randint(0, 10, size=(10, 1), dtype=torch.float).cuda()\n", + " label = (data % 2 == 0).long().cuda().squeeze()\n", + " b, t, o = net(data)\n", + " print('binary_result: ', torch.argmax(b, dim=-1))\n", + " print('triangle_result: ', torch.argmax(t, dim=-1))\n", + " print('origin_result: ', torch.argmax(o, dim=-1))\n", + " print('label: ', label)" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\73106\\AppData\\Local\\Temp\\ipykernel_51568\\3798698966.py:36: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " l = num2binary(x//2)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:[ 0/200] binary_loss:0.7349872589111328 triangle_loss:0.6947663426399231 origin_loss:52.24180221557617\n", + "epoch:[ 1/200] binary_loss:0.683316171169281 triangle_loss:0.6998624801635742 origin_loss:28.34395408630371\n", + "epoch:[ 2/200] binary_loss:0.6706985235214233 triangle_loss:0.6971087455749512 origin_loss:9.654135704040527\n", + "epoch:[ 3/200] binary_loss:0.6902305483818054 triangle_loss:0.7240961790084839 origin_loss:13.732337951660156\n", + "epoch:[ 4/200] binary_loss:0.6652613878250122 triangle_loss:0.7089925408363342 origin_loss:22.04283905029297\n", + "epoch:[ 5/200] binary_loss:0.6852871775627136 triangle_loss:0.6977459788322449 origin_loss:23.781091690063477\n", + "epoch:[ 6/200] binary_loss:0.6469615697860718 triangle_loss:0.7086113691329956 origin_loss:18.387901306152344\n", + "epoch:[ 7/200] binary_loss:0.6761390566825867 triangle_loss:0.6878255009651184 origin_loss:11.120370864868164\n", + "epoch:[ 8/200] binary_loss:0.6498953700065613 triangle_loss:0.6898926496505737 origin_loss:3.8951528072357178\n", + "epoch:[ 9/200] binary_loss:0.6452895998954773 triangle_loss:0.6870980858802795 origin_loss:7.197264671325684\n", + "epoch:[ 10/200] binary_loss:0.6411842107772827 triangle_loss:0.6955468058586121 origin_loss:15.669432640075684\n", + "epoch:[ 11/200] binary_loss:0.6168311834335327 triangle_loss:0.691303014755249 origin_loss:17.784526824951172\n", + "epoch:[ 12/200] binary_loss:0.6193327903747559 triangle_loss:0.6875953674316406 origin_loss:15.500490188598633\n", + "epoch:[ 13/200] binary_loss:0.6365929841995239 triangle_loss:0.7006270885467529 origin_loss:15.674912452697754\n", + "epoch:[ 14/200] binary_loss:0.6062679886817932 triangle_loss:0.6948075294494629 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origin_loss:2.4676527976989746\n", + "epoch:[190/200] binary_loss:0.022400056943297386 triangle_loss:0.6969032883644104 origin_loss:1.6492072343826294\n", + "epoch:[191/200] binary_loss:0.02470509707927704 triangle_loss:0.6954671144485474 origin_loss:3.0599381923675537\n", + "epoch:[192/200] binary_loss:0.0231486689299345 triangle_loss:0.6977156400680542 origin_loss:4.135528564453125\n", + "epoch:[193/200] binary_loss:0.024425290524959564 triangle_loss:0.6975620985031128 origin_loss:3.454710006713867\n", + "epoch:[194/200] binary_loss:0.023887356743216515 triangle_loss:0.6981842517852783 origin_loss:1.3243358135223389\n", + "epoch:[195/200] binary_loss:0.018893973901867867 triangle_loss:0.6980413198471069 origin_loss:2.4780077934265137\n", + "epoch:[196/200] binary_loss:0.02293287217617035 triangle_loss:0.6993746757507324 origin_loss:2.5406863689422607\n", + "epoch:[197/200] binary_loss:0.020013099536299706 triangle_loss:0.6974712610244751 origin_loss:0.8364153504371643\n", + "epoch:[198/200] binary_loss:0.01902010291814804 triangle_loss:0.6927831172943115 origin_loss:3.662505626678467\n", + "epoch:[199/200] binary_loss:0.02204802818596363 triangle_loss:0.6838313937187195 origin_loss:3.501659870147705\n", + "----------------------------- Test -----------------------------\n", + "binary_result: tensor([1, 0, 1, 1, 1, 0, 0, 0, 0, 1], device='cuda:0')\n", + "triangle_result: tensor([0, 0, 1, 0, 1, 0, 0, 1, 1, 0], device='cuda:0')\n", + "origin_result: tensor([1, 0, 1, 1, 1, 1, 0, 0, 0, 1], device='cuda:0')\n", + "label: tensor([1, 0, 1, 1, 1, 0, 0, 0, 0, 1], device='cuda:0')\n" + ] + } + ], + "source": [ + "main()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "\n", + "### 结果\n", + "\n", + "![](demo/homework3_2_result.png)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 设计\n", + "\n", + "这里为了网络参数公平,另编码长度保证相同,三种方式如下\n", + "\n", + "- 二进制方法:对 [0,999] 范围的数转变成 10 位二进制数\n", + "- 三角函数法:对 sin、cos 进行交替编码,分别尝试了 postion_encoding、直接对原来的数进行 cos、sin\n", + "- 直接数字法:将输入数字重复 10 次" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 分析\n", + "针对三种方案,其中 `二进制` 的方式是可行的\n", + "\n", + "- `二进制`方法:因为二进制最后一位数与数的奇偶性具有强相关性,故经过迭代基本能够无误实现判断\n", + "\n", + "- `三角函数`方法:按道理来说,三角函数具有周期性,只要能够学到周期为1,那应该是能够收敛的,但是我在测试的时候并没有收敛,可能是我设计的编码他的周期不为1,导致出现了奇偶模糊,因为 position_encoding 中有 $PE_{t+\\Delta t} = T_{\\Delta t} PE_{t}$ 的变换关系\n", + "\n", + "- `直接数字`方法:完全不收敛\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 题目2(应用实践" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](demo/homework3_2.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "torch11", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/my_homework/ch3/homework3_2.py b/my_homework/ch3/homework3_2.py new file mode 100644 index 0000000..336c790 --- /dev/null +++ b/my_homework/ch3/homework3_2.py @@ -0,0 +1,68 @@ +# Please install latest DI-engine's main branch first +from ding.bonus import PPOF + + +def bipedalwalker(): + # Please install bipedalwalker env first, `pip3 install box2d` + # You can refer to the env doc (https://di-engine-docs.readthedocs.io/zh_CN/latest/13_envs/bipedalwalker_zh.html) for more details + agent = PPOF(env='bipedalwalker', exp_name='./bipedalwalker_demo') + agent.train(step=int(1e6)) + # Classic RL interaction loop and save replay video +def bipedalwalker_deploy(): + agent = PPOF(env='bipedalwalker', exp_name='./bipedalwalker_demo') + agent.deploy(ckpt_path="bipedalwalker_demo\ckpt\iteration_155680.pth.tar",enable_save_replay=True) + + +def evogym_carrier(): + # Please install evogym env first, refer to its doc (https://github.com/EvolutionGym/evogym#installation) + # Or you can use our provided docker (opendilab/ding:nightly-evogym) + # You can refer to the env doc (https://di-engine-docs.readthedocs.io/zh_CN/latest/13_envs/Evogym_zh.html) for more details + agent = PPOF(env='evogym_carrier', exp_name='./evogym_carrier_demo') + agent.train(step=int(1e6)) + + +def mario(): + # Please install mario env first, `pip install gym-super-mario-bros` + # You can refer to the env doc (https://di-engine-docs.readthedocs.io/zh_CN/latest/13_envs/gym_super_mario_bros_zh.html) for more details + agent = PPOF(env='mario', exp_name='./mario_demo') + agent.train(step=int(3e6)) + +def mario_deploy(): + agent = PPOF(env='mario', exp_name='./mario_demo') + agent.deploy(enable_save_replay=True) + + +def di_sheep(): + # Please prepare di_sheep env and modelfirst, you can copy the env and model file to to current directory, + # which are placed in https://github.com/opendilab/DI-sheep/blob/master/service + from sheep_env import SheepEnv + from sheep_model import SheepModel + env = SheepEnv(level=9) + obs_space = env.observation_space + model = SheepModel( + item_obs_size=obs_space['item_obs'].shape[1], + item_num=obs_space['item_obs'].shape[0], + item_encoder_type='TF', + bucket_obs_size=obs_space['bucket_obs'].shape[0], + global_obs_size=obs_space['global_obs'].shape[0], + ttorch_return=True, + ) + agent = PPOF(env='di_sheep', exp_name='./di_sheep_demo', model=model) + agent.train(step=int(1e6)) + + +def procgen_bigfish(): + # Please install procgen env first, `pip install procgen` + # You can refer to the env doc (https://di-engine-docs.readthedocs.io/zh_CN/latest/13_envs/procgen_zh.html) for more details + agent = PPOF(env='procgen_bigfish', exp_name='./procgen_bigfish_demo') + agent.train(step=int(1e7)) + + +if __name__ == "__main__": + # You can select and run your favorite demo + # bipedalwalker() + bipedalwalker_deploy() + # evogym_carrier() + # mario() + # di_sheep() + # procgen_bigfish() \ No newline at end of file diff --git a/my_homework/ch4/home_analyse.ipynb b/my_homework/ch4/home_analyse.ipynb new file mode 100644 index 0000000..40d14ae --- /dev/null +++ b/my_homework/ch4/home_analyse.ipynb @@ -0,0 +1,113 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CH4 " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## homework 1" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](demo/image1.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Result" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### small\n", + "![](demo/small.png)\n", + "#### little\n", + "![](demo/little.png)\n", + "#### standard\n", + "![](demo/standard.png)\n", + "#### large\n", + "![](demo/large.png)\n", + "#### very large\n", + "![](demo/verylarge.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analyse" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "从上面不同模型大小的输出图像分析模型阐述 RND 网络的过拟合和欠拟合问题\n", + "\n", + "可以看到在模型参数较小的时候,整体的 RND 网络的 reward 是非常大的,说明模型随机性很大,出现欠拟合\n", + "\n", + "随着模型参数量的增加 mse 和 reward 都在往下降,但是当模型逐渐转变到 large 和 very large 的时候 reward_min 无法收敛,发生了过拟合" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## homework 2 " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](demo/image2.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "由于没法制作视频,好像是 metadrive 需要的是 gym=0.19.0 的环境,我安装之后发现运行 deploy 成功但是没法记录视频,因此只能够上传 log" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "log_path = [run-20230312_134251-g1s73ewz]()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/my_homework/ch4/homework4.py b/my_homework/ch4/homework4.py new file mode 100644 index 0000000..cbac48a --- /dev/null +++ b/my_homework/ch4/homework4.py @@ -0,0 +1,55 @@ +# Please install latest DI-engine's main branch first +from ding.bonus import PPOF + + +def acrobot(): + # Please install acrobot env first, `pip3 install gym` + # You can refer to the env doc (https://di-engine-docs.readthedocs.io/zh_CN/latest/13_envs/acrobot_zh.html) for more details + agent = PPOF(env='acrobot', exp_name='output/ch4/acrobot_demo') + agent.train(step=int(1e5)) + +def acrobot_deploy(): + # Please install acrobot env first, `pip3 install gym` + # You can refer to the env doc (https://di-engine-docs.readthedocs.io/zh_CN/latest/13_envs/acrobot_zh.html) for more details + agent = PPOF(env='acrobot', exp_name='output/ch4/acrobot_demo') + agent.deploy(enable_save_replay=True) + + +def metadrive(): + # Please install metadrive env first, `pip install metadrive-simulator` + # You can refer to the env doc (https://di-engine-docs.readthedocs.io/zh_CN/latest/13_envs/metadrive_zh.html) for more details + agent = PPOF(env='metadrive', exp_name='output/ch4/metadrive_demo') + agent.train(step=int(1e6), context='spawn') + +def metadrive_deploy(): + agent = PPOF(env='metadrive', exp_name='output/ch4/metadrive_demo') + agent.deploy(enable_save_replay=True) + +def metadrive_install_test(): + from metadrive import MetaDriveEnv + env = MetaDriveEnv() + obs = env.reset() + print(obs.shape) # 输出 (259,) + +def minigrid_fourroom(): + # Please install minigrid env first, `pip install gym-minigrid` + # Note: minigrid env doesn't support Windows platform + # You can refer to the env doc (https://di-engine-docs.readthedocs.io/zh_CN/latest/13_envs/minigrid_zh.html) for more details + agent = PPOF(env='minigrid_fourroom', exp_name='output/ch4/minigrid_fourroom') + agent.train(step=int(3e6)) + + +def minigrid_fourroom_deploy(): + agent = PPOF(env='minigrid_fourroom', exp_name='output/ch4/minigrid_fourroom') + agent.deploy(enable_save_replay=True) + + + + +if __name__ == "__main__": + # acrobot() + # acrobot_deploy() + # metadrive_install_test() + metadrive() + # metadrive_deploy() + # minigrid_fourroom() \ No newline at end of file diff --git a/my_homework/ch4/ppof_ch4_code_p1.py b/my_homework/ch4/ppof_ch4_code_p1.py new file mode 100644 index 0000000..57bc778 --- /dev/null +++ b/my_homework/ch4/ppof_ch4_code_p1.py @@ -0,0 +1,326 @@ +# pip install minigrid +import os.path +from typing import Union, Tuple, Dict, List, Optional +from multiprocessing import Process +import multiprocessing as mp +import random +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import minigrid +import gymnasium as gym +from tqdm import tqdm +from torch.optim.lr_scheduler import ExponentialLR, MultiStepLR +from tensorboardX import SummaryWriter +from minigrid.wrappers import FlatObsWrapper + +random.seed(0) +np.random.seed(0) +torch.manual_seed(0) +if torch.cuda.is_available(): + device = torch.device("cuda:0") +else: + device = torch.device("cpu") + +train_config = dict( + train_iter=1024, + train_data_count=128, + test_data_count=4096, +) + +little_RND_net_config = dict( + exp_name="output/ch4/little_rnd_network", + observation_shape=2835, + hidden_size_list=[32, 16], + learning_rate=1e-3, + batch_size=64, + update_per_collect=100, + obs_norm=True, + obs_norm_clamp_min=-1, + obs_norm_clamp_max=1, + reward_mse_ratio=1e5, +) + +small_RND_net_config = dict( + exp_name="output/ch4/small_rnd_network", + observation_shape=2835, + hidden_size_list=[64, 64], + learning_rate=1e-3, + batch_size=64, + update_per_collect=100, + obs_norm=True, + obs_norm_clamp_min=-1, + obs_norm_clamp_max=1, + reward_mse_ratio=1e5, +) + +standard_RND_net_config = dict( + exp_name="output/ch4/standard_rnd_network", + observation_shape=2835, + hidden_size_list=[128, 64], + learning_rate=1e-3, + batch_size=64, + update_per_collect=100, + obs_norm=True, + obs_norm_clamp_min=-1, + obs_norm_clamp_max=1, + reward_mse_ratio=1e5, +) + +large_RND_net_config = dict( + exp_name="output/ch4/large_RND_network", + observation_shape=2835, + hidden_size_list=[256, 256], + learning_rate=1e-3, + batch_size=64, + update_per_collect=100, + obs_norm=True, + obs_norm_clamp_min=-1, + obs_norm_clamp_max=1, + reward_mse_ratio=1e5, +) + +very_large_RND_net_config = dict( + exp_name="output/ch4/very_large_RND_network", + observation_shape=2835, + hidden_size_list=[512, 512], + learning_rate=1e-3, + batch_size=64, + update_per_collect=100, + obs_norm=True, + obs_norm_clamp_min=-1, + obs_norm_clamp_max=1, + reward_mse_ratio=1e5, +) + +class FCEncoder(nn.Module): + def __init__( + self, + obs_shape: int, + hidden_size_list, + activation: Optional[nn.Module] = nn.ReLU(), + ) -> None: + super(FCEncoder, self).__init__() + self.obs_shape = obs_shape + self.act = activation + self.init = nn.Linear(obs_shape, hidden_size_list[0]) + + layers = [] + for i in range(len(hidden_size_list) - 1): + layers.append(nn.Linear(hidden_size_list[i], hidden_size_list[i + 1])) + layers.append(self.act) + self.main = nn.Sequential(*layers) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.act(self.init(x)) + x = self.main(x) + return x + +class RndNetwork(nn.Module): + def __init__(self, obs_shape: Union[int, list], hidden_size_list: list) -> None: + super(RndNetwork, self).__init__() + self.target = FCEncoder(obs_shape, hidden_size_list) + self.predictor = FCEncoder(obs_shape, hidden_size_list) + + for param in self.target.parameters(): + param.requires_grad = False + + def forward(self, obs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + predict_feature = self.predictor(obs) + with torch.no_grad(): + target_feature = self.target(obs) + return predict_feature, target_feature + +class RunningMeanStd(object): + def __init__(self, epsilon=1e-4, shape=(), device=torch.device('cpu')): + self._epsilon = epsilon + self._shape = shape + self._device = device + self.reset() + + def update(self, x): + batch_mean = np.mean(x, axis=0) + batch_var = np.var(x, axis=0) + batch_count = x.shape[0] + + new_count = batch_count + self._count + mean_delta = batch_mean - self._mean + new_mean = self._mean + mean_delta * batch_count / new_count + # this method for calculating new variable might be numerically unstable + m_a = self._var * self._count + m_b = batch_var * batch_count + m2 = m_a + m_b + np.square(mean_delta) * self._count * batch_count / new_count + new_var = m2 / new_count + self._mean = new_mean + self._var = new_var + self._count = new_count + + def reset(self): + if len(self._shape) > 0: + self._mean = np.zeros(self._shape, 'float32') + self._var = np.ones(self._shape, 'float32') + else: + self._mean, self._var = 0., 1. + self._count = self._epsilon + + @property + def mean(self) -> np.ndarray: + if np.isscalar(self._mean): + return self._mean + else: + return torch.FloatTensor(self._mean).to(self._device) + + @property + def std(self) -> np.ndarray: + std = np.sqrt(self._var + 1e-8) + if np.isscalar(std): + return std + else: + return torch.FloatTensor(std).to(self._device) + +class RndRewardModel(): + + def __init__(self, config) -> None: # noqa + super(RndRewardModel, self).__init__() + self.cfg = config + + self.tb_logger = SummaryWriter(os.path.join('output/ch4', config["exp_name"])) + self.reward_model = RndNetwork( + obs_shape=config["observation_shape"], hidden_size_list=config["hidden_size_list"] + ).to(device) + + self.opt = optim.Adam(self.reward_model.predictor.parameters(), config["learning_rate"]) + self.scheduler = ExponentialLR(self.opt, gamma=0.997) + + self.estimate_cnt_rnd = 0 + if self.cfg["obs_norm"]: + self._running_mean_std_rnd_obs = RunningMeanStd(epsilon=1e-4, device=device) + + def __del__(self): + self.tb_logger.flush() + self.tb_logger.close() + + def train(self, data) -> None: + for _ in range(self.cfg["update_per_collect"]): + train_data: list = random.sample(data, self.cfg["batch_size"]) + train_data: torch.Tensor = torch.stack(train_data).to(device) + if self.cfg["obs_norm"]: + # Note: observation normalization: transform obs to mean 0, std 1 + self._running_mean_std_rnd_obs.update(train_data.cpu().numpy()) + train_data = (train_data - self._running_mean_std_rnd_obs.mean) / self._running_mean_std_rnd_obs.std + train_data = torch.clamp( + train_data, min=self.cfg["obs_norm_clamp_min"], max=self.cfg["obs_norm_clamp_max"] + ) + + predict_feature, target_feature = self.reward_model(train_data) + loss = F.mse_loss(predict_feature, target_feature.detach()) + self.opt.zero_grad() + loss.backward() + self.opt.step() + self.scheduler.step() + + def estimate(self, data: list) -> List[Dict]: + """ + estimate the rnd intrinsic reward + """ + + obs = torch.stack(data).to(device) + if self.cfg["obs_norm"]: + # Note: observation normalization: transform obs to mean 0, std 1 + obs = (obs - self._running_mean_std_rnd_obs.mean) / self._running_mean_std_rnd_obs.std + obs = torch.clamp(obs, min=self.cfg["obs_norm_clamp_min"], max=self.cfg["obs_norm_clamp_max"]) + + with torch.no_grad(): + self.estimate_cnt_rnd += 1 + predict_feature, target_feature = self.reward_model(obs) + mse = F.mse_loss(predict_feature, target_feature, reduction='none').mean(dim=1) + self.tb_logger.add_scalar('rnd_reward/mse', mse.cpu().numpy().mean(), self.estimate_cnt_rnd) + + # Note: according to the min-max normalization, transform rnd reward to [0,1] + rnd_reward = mse * self.cfg["reward_mse_ratio"] #(mse - mse.min()) / (mse.max() - mse.min() + 1e-11) + + self.tb_logger.add_scalar('rnd_reward/rnd_reward_max', rnd_reward.max(), self.estimate_cnt_rnd) + self.tb_logger.add_scalar('rnd_reward/rnd_reward_mean', rnd_reward.mean(), self.estimate_cnt_rnd) + self.tb_logger.add_scalar('rnd_reward/rnd_reward_min', rnd_reward.min(), self.estimate_cnt_rnd) + + rnd_reward = torch.chunk(rnd_reward, rnd_reward.shape[0], dim=0) + +def training(config, train_data, test_data): + rnd_reward_model = RndRewardModel(config=config) + for i in tqdm(range(train_config["train_iter"])): + rnd_reward_model.train([torch.Tensor(item["last_observation"]) for item in train_data[i]]) + rnd_reward_model.estimate([torch.Tensor(item["last_observation"]) for item in test_data]) + +def main(): + env = gym.make("MiniGrid-Empty-8x8-v0") + env_obs = FlatObsWrapper(env) + + train_data = [] + test_data = [] + + for i in range(train_config["train_iter"]): + + train_data_per_iter = [] + + while len(train_data_per_iter) < train_config["train_data_count"]: + last_observation, _ = env_obs.reset() + terminated = False + while terminated != True and len(train_data_per_iter) < train_config["train_data_count"]: + action = env_obs.action_space.sample() + observation, reward, terminated, truncated, info = env_obs.step(action) + train_data_per_iter.append( + { + "last_observation": last_observation, + "action": action, + "reward": reward, + "observation": observation + } + ) + last_observation = observation + env_obs.close() + + train_data.append(train_data_per_iter) + + while len(test_data) < train_config["test_data_count"]: + last_observation, _ = env_obs.reset() + terminated = False + while terminated != True and len(train_data_per_iter) < train_config["test_data_count"]: + action = env_obs.action_space.sample() + observation, reward, terminated, truncated, info = env_obs.step(action) + test_data.append( + { + "last_observation": last_observation, + "action": action, + "reward": reward, + "observation": observation + } + ) + last_observation = observation + env_obs.close() + + # p0 = Process(target=training, args=(little_RND_net_config, train_data, test_data)) + # p0.start() + + # p1 = Process(target=training, args=(small_RND_net_config, train_data, test_data)) + # p1.start() + + # p2 = Process(target=training, args=(standard_RND_net_config, train_data, test_data)) + # p2.start() + + p3 = Process(target=training, args=(large_RND_net_config, train_data, test_data)) + p3.start() + + p4 = Process(target=training, args=(very_large_RND_net_config, train_data, test_data)) + p4.start() + + # p0.join() + # p1.join() + # p2.join() + p3.join() + p4.join() + +if __name__ == "__main__": + mp.set_start_method('spawn') + main() diff --git a/my_homework/ch4/run-20230312_134251-g1s73ewz/files/conda-environment.yaml b/my_homework/ch4/run-20230312_134251-g1s73ewz/files/conda-environment.yaml new file mode 100644 index 0000000..e69de29 diff --git a/my_homework/ch4/run-20230312_134251-g1s73ewz/files/config.yaml b/my_homework/ch4/run-20230312_134251-g1s73ewz/files/config.yaml new file mode 100644 index 0000000..295b980 --- /dev/null +++ b/my_homework/ch4/run-20230312_134251-g1s73ewz/files/config.yaml @@ -0,0 +1,29 @@ +wandb_version: 1 + +_wandb: + desc: null + value: + cli_version: 0.13.10 + framework: torch + is_jupyter_run: false + is_kaggle_kernel: false + python_version: 3.9.16 + start_time: 1678599771.618358 + t: + 1: + - 1 + - 5 + - 53 + - 55 + 2: + - 1 + - 5 + - 53 + - 55 + 3: + - 23 + 4: 3.9.16 + 5: 0.13.10 + 8: + - 3 + - 5 diff --git a/my_homework/ch4/run-20230312_134251-g1s73ewz/files/output.log b/my_homework/ch4/run-20230312_134251-g1s73ewz/files/output.log new file mode 100644 index 0000000..5d28eb0 --- /dev/null +++ b/my_homework/ch4/run-20230312_134251-g1s73ewz/files/output.log @@ -0,0 +1,4 @@ +[03-12 13:42:54] WARNING  If you want to use wandb to visualize the gradient, please set ]8;id=271493;file://E:\miniconda\envs\torch11\lib\site-packages\ding\utils\default_helper.py\default_helper.py]8;;\:]8;id=536110;file://E:\miniconda\envs\torch11\lib\site-packages\ding\utils\default_helper.py#410\410]8;;\ + gradient_logger = True in the config. +[03-12 13:44:15] INFO  Evaluation: Train Iter(0) Env Step(0) Mean Episode Return(0.062) ]8;id=499748;file://E:\miniconda\envs\torch11\lib\site-packages\ding\framework\middleware\functional\evaluator.py\evaluator.py]8;;\:]8;id=375441;file://E:\miniconda\envs\torch11\lib\site-packages\ding\framework\middleware\functional\evaluator.py#370\370]8;;\ +[03-12 13:44:51] INFO  Exceeded maximum number of env_step(3003), program is terminated ]8;id=88754;file://E:\miniconda\envs\torch11\lib\site-packages\ding\framework\middleware\functional\termination_checker.py\termination_checker.py]8;;\:]8;id=541618;file://E:\miniconda\envs\torch11\lib\site-packages\ding\framework\middleware\functional\termination_checker.py#22\22]8;;\ \ No newline at end of file diff --git a/my_homework/ch4/run-20230312_134251-g1s73ewz/files/requirements.txt b/my_homework/ch4/run-20230312_134251-g1s73ewz/files/requirements.txt new file mode 100644 index 0000000..7e4ab09 --- /dev/null +++ b/my_homework/ch4/run-20230312_134251-g1s73ewz/files/requirements.txt @@ -0,0 +1,179 @@ +absl-py==1.4.0 +aiohttp==3.8.4 +aiosignal==1.3.1 +appdirs==1.4.4 +asttokens==2.2.1 +async-timeout==4.0.2 +attrs==22.2.0 +backcall==0.2.0 +backports.functools-lru-cache==1.6.4 +bitmath==1.3.3.1 +box2d==2.3.10 +brotlipy==0.7.0 +cachetools==5.3.0 +certifi==2022.12.7 +cffi==1.15.1 +chardet==4.0.0 +charset-normalizer==2.0.4 +click==7.1.2 +cloudpickle==1.6.0 +cmake==3.25.2 +colorama==0.4.6 +contourpy==1.0.7 +cryptography==39.0.1 +cycler==0.11.0 +debugpy==1.5.1 +decorator==5.1.1 +di-engine==0.4.6 +di-toolkit==0.1.0 +di-treetensor==0.4.0 +dill==0.3.6 +docker-pycreds==0.4.0 +easydict==1.9 +enum-tools==0.9.0.post1 +exceptiongroup==1.1.0 +executing==1.2.0 +flask==1.1.4 +fonttools==4.38.0 +frozenlist==1.3.3 +gitdb==4.0.10 +gitpython==3.1.31 +glfw==2.5.6 +google-auth-oauthlib==0.4.6 +google-auth==2.16.2 +graphviz==0.20.1 +grpcio==1.51.3 +gym-notices==0.0.8 +gym-super-mario-bros==7.4.0 +gym==0.19.0 +gymnasium-notices==0.0.1 +gymnasium==0.27.1 +h5py==3.8.0 +hbutils==0.8.1 +hickle==5.0.2 +idna==3.4 +imageio-ffmpeg==0.4.8 +imageio==2.26.0 +importlib-metadata==6.0.0 +importlib-resources==5.12.0 +iniconfig==2.0.0 +ipykernel==6.15.0 +ipython==8.11.0 +itsdangerous==1.1.0 +jax-jumpy==0.2.0 +jedi==0.18.2 +jinja2==2.11.3 +joblib==1.2.0 +jupyter-client==8.0.3 +jupyter-core==5.2.0 +kiwisolver==1.4.4 +lazy-loader==0.1 +libtorrent==2.0.7 +llvmlite==0.39.1 +lxml==4.9.2 +lz4==4.3.2 +markdown-it-py==2.2.0 +markdown==3.4.1 +markupsafe==2.0.1 +matplotlib-inline==0.1.6 +matplotlib==3.7.0 +mdurl==0.1.2 +metadrive-simulator==0.2.6.0 +minigrid==2.1.1 +mkl-fft==1.3.1 +mkl-random==1.2.2 +mkl-service==2.4.0 +mpire==2.6.0 +multidict==6.0.4 +nes-py==8.2.1 +nest-asyncio==1.5.6 +networkx==3.0 +numba==0.56.4 +numpy==1.23.5 +oauthlib==3.2.2 +openai==0.27.0 +opencv-python-headless==4.7.0.72 +opencv-python==4.7.0.72 +packaging==23.0 +panda3d-gltf==0.13 +panda3d-simplepbr==0.10 +panda3d==1.10.8 +pandas==1.5.3 +parso==0.8.3 +pathtools==0.1.2 +pettingzoo==1.22.3 +pickleshare==0.7.5 +pillow==9.4.0 +pip==23.0.1 +platformdirs==3.0.0 +pluggy==1.0.0 +prompt-toolkit==3.0.38 +protobuf==3.20.1 +psutil==5.9.0 +pure-eval==0.2.2 +pyasn1-modules==0.2.8 +pyasn1==0.4.8 +pycparser==2.21 +pygame==2.2.0 +pyglet==1.5.21 +pygments==2.14.0 +pynng==0.7.2 +pyopengl==3.1.6 +pyopenssl==23.0.0 +pyparsing==3.0.9 +pysocks==1.7.1 +pytest==7.2.2 +python-dateutil==2.8.2 +pytimeparse==1.1.8 +pytz==2022.7.1 +pywavelets==1.4.1 +pywin32==305 +pyyaml==6.0 +pyzmq==23.2.0 +readerwriterlock==1.0.9 +redis==4.5.1 +requests-oauthlib==1.3.1 +requests==2.28.1 +responses==0.12.1 +rich==13.3.1 +rocket-recycling==0.1 +rsa==4.9 +scikit-image==0.20.0 +scikit-learn==1.2.1 +scipy==1.9.1 +seaborn==0.12.2 +sentry-sdk==1.16.0 +setproctitle==1.3.2 +setuptools==65.6.3 +six==1.16.0 +smmap==5.0.0 +sniffio==1.3.0 +stack-data==0.6.2 +tabulate==0.9.0 +tensorboard-data-server==0.7.0 +tensorboard-plugin-wit==1.8.1 +tensorboard==2.12.0 +tensorboardx==2.6 +threadpoolctl==3.1.0 +tifffile==2023.2.28 +tomli==2.0.1 +torch==1.11.0 +torchaudio==0.11.0 +torchvision==0.12.0 +tornado==6.2 +tqdm==4.64.1 +traitlets==5.9.0 +treevalue==1.4.7 +trueskill==0.4.5 +typing-extensions==4.4.0 +urllib3==1.26.14 +urlobject==2.4.3 +wandb==0.13.10 +wcwidth==0.2.6 +werkzeug==1.0.1 +wheel==0.38.4 +win-inet-pton==1.1.0 +wincertstore==0.2 +yapf==0.29.0 +yarl==1.8.2 +zipp==3.15.0 \ No newline at end of file diff --git a/my_homework/ch4/run-20230312_134251-g1s73ewz/files/wandb-metadata.json b/my_homework/ch4/run-20230312_134251-g1s73ewz/files/wandb-metadata.json new file mode 100644 index 0000000..52f22e5 --- /dev/null +++ b/my_homework/ch4/run-20230312_134251-g1s73ewz/files/wandb-metadata.json @@ -0,0 +1,50 @@ +{ + "os": "Windows-10-10.0.19044-SP0", + "python": "3.9.16", + "heartbeatAt": "2023-03-12T05:42:54.179314", + "startedAt": "2023-03-12T05:42:51.588439", + "docker": null, + "cuda": null, + "args": [], + "state": "running", + "program": "e:\\workspace\\ANM\\PPOxFamily\\ch4\\homework4.py", + "codePath": "ch4\\homework4.py", + "git": { + "remote": "https://github.com/GuoPingPan/PPOxFamily.git", + "commit": "5d0b49a096f580bb94d0f3e0b6c7870b19f462ae" + }, + "email": "731061720@qq.com", + "root": "E:/workspace/ANM/PPOxFamily", + "host": "Kindel-PG", + "username": "73106", + "executable": "E:\\miniconda\\envs\\torch11\\python.exe", + "cpu_count": 4, + "cpu_count_logical": 8, + "cpu_freq": { + "current": 2400.0, + "min": 0.0, + "max": 2400.0 + }, + "cpu_freq_per_core": [ + { + "current": 2400.0, + "min": 0.0, + "max": 2400.0 + } + ], + "disk": { + "total": 195.31151962280273, + "used": 132.6543083190918 + }, + "gpu": "NVIDIA GeForce GTX 1650", + "gpu_count": 1, + "gpu_devices": [ + { + "name": "NVIDIA GeForce GTX 1650", + "memory_total": 4294967296 + } + ], + "memory": { + "total": 15.922107696533203 + } +} diff --git a/my_homework/ch4/run-20230312_134251-g1s73ewz/files/wandb-summary.json b/my_homework/ch4/run-20230312_134251-g1s73ewz/files/wandb-summary.json new file mode 100644 index 0000000..008260e --- /dev/null +++ b/my_homework/ch4/run-20230312_134251-g1s73ewz/files/wandb-summary.json @@ -0,0 +1 @@ +{"cur_lr": 0.00010000000000000003, "_timestamp": 1678599891.7756824, "_runtime": 120.15732455253601, "_step": 10, "policy_loss": -0.006951711806671127, "value_loss": 4.781930442316376, "entropy_loss": 1.8287189849044965, "adv_max": 1.6704593328030213, "adv_mean": 5.895676820174507e-09, "approx_kl": 0.005069196849818463, "clipfrac": 0.057676630434782605, "value_max": 0.40170730336356664, "value_mean": -0.08156897273299325, "reward": 0.06240813036887266, "train iter": 460, "env step": 3003, "_wandb": {"runtime": 117}} \ No newline at end of file diff --git a/my_homework/ch4/run-20230312_134251-g1s73ewz/logs/debug-internal.log b/my_homework/ch4/run-20230312_134251-g1s73ewz/logs/debug-internal.log new file mode 100644 index 0000000..b881d8c --- /dev/null +++ b/my_homework/ch4/run-20230312_134251-g1s73ewz/logs/debug-internal.log @@ -0,0 +1,341 @@ +2023-03-12 13:42:51,617 INFO StreamThr :32820 [internal.py:wandb_internal():87] W&B internal server running at pid: 32820, started at: 2023-03-12 13:42:51.616363 +2023-03-12 13:42:51,618 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status +2023-03-12 13:42:51,621 INFO WriterThread:32820 [datastore.py:open_for_write():85] open: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\run-g1s73ewz.wandb +2023-03-12 13:42:51,624 DEBUG SenderThread:32820 [sender.py:send():336] send: header +2023-03-12 13:42:51,692 DEBUG SenderThread:32820 [sender.py:send():336] send: run +2023-03-12 13:42:53,334 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: check_version +2023-03-12 13:42:53,334 INFO SenderThread:32820 [dir_watcher.py:__init__():216] watching files in: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files +2023-03-12 13:42:53,349 INFO SenderThread:32820 [sender.py:_start_run_threads():1067] run started: g1s73ewz with start time 1678599771.618358 +2023-03-12 13:42:53,350 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: summary_record +2023-03-12 13:42:53,350 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:42:53,351 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: check_version +2023-03-12 13:42:54,082 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: run_start +2023-03-12 13:42:54,131 DEBUG HandlerThread:32820 [system_info.py:__init__():31] System info init +2023-03-12 13:42:54,131 DEBUG HandlerThread:32820 [system_info.py:__init__():46] System info init done +2023-03-12 13:42:54,131 INFO HandlerThread:32820 [system_monitor.py:start():151] Starting system monitor +2023-03-12 13:42:54,132 INFO SystemMonitor:32820 [system_monitor.py:_start():116] Starting system asset monitoring threads +2023-03-12 13:42:54,132 INFO HandlerThread:32820 [system_monitor.py:probe():172] Collecting system info +2023-03-12 13:42:54,137 INFO SystemMonitor:32820 [interfaces.py:start():168] Started cpu +2023-03-12 13:42:54,137 INFO SystemMonitor:32820 [interfaces.py:start():168] Started disk +2023-03-12 13:42:54,137 INFO SystemMonitor:32820 [interfaces.py:start():168] Started gpu +2023-03-12 13:42:54,138 INFO SystemMonitor:32820 [interfaces.py:start():168] Started memory +2023-03-12 13:42:54,155 INFO SystemMonitor:32820 [interfaces.py:start():168] Started network +2023-03-12 13:42:54,179 DEBUG HandlerThread:32820 [system_info.py:probe():195] Probing system +2023-03-12 13:42:54,181 DEBUG HandlerThread:32820 [system_info.py:_probe_git():180] Probing git +2023-03-12 13:42:54,184 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:42:54,310 DEBUG HandlerThread:32820 [system_info.py:_probe_git():188] Probing git done +2023-03-12 13:42:54,310 DEBUG HandlerThread:32820 [system_info.py:probe():241] Probing system done +2023-03-12 13:42:54,310 DEBUG HandlerThread:32820 [system_monitor.py:probe():181] {'os': 'Windows-10-10.0.19044-SP0', 'python': '3.9.16', 'heartbeatAt': '2023-03-12T05:42:54.179314', 'startedAt': '2023-03-12T05:42:51.588439', 'docker': None, 'cuda': None, 'args': (), 'state': 'running', 'program': 'e:\\workspace\\ANM\\PPOxFamily\\ch4\\homework4.py', 'codePath': 'ch4\\homework4.py', 'git': {'remote': 'https://github.com/GuoPingPan/PPOxFamily.git', 'commit': '5d0b49a096f580bb94d0f3e0b6c7870b19f462ae'}, 'email': '731061720@qq.com', 'root': 'E:/workspace/ANM/PPOxFamily', 'host': 'Kindel-PG', 'username': '73106', 'executable': 'E:\\miniconda\\envs\\torch11\\python.exe', 'cpu_count': 4, 'cpu_count_logical': 8, 'cpu_freq': {'current': 2400.0, 'min': 0.0, 'max': 2400.0}, 'cpu_freq_per_core': [{'current': 2400.0, 'min': 0.0, 'max': 2400.0}], 'disk': {'total': 195.31151962280273, 'used': 132.6543083190918}, 'gpu': 'NVIDIA GeForce GTX 1650', 'gpu_count': 1, 'gpu_devices': [{'name': 'NVIDIA GeForce GTX 1650', 'memory_total': 4294967296}], 'memory': {'total': 15.922107696533203}} +2023-03-12 13:42:54,310 INFO HandlerThread:32820 [system_monitor.py:probe():182] Finished collecting system info +2023-03-12 13:42:54,310 INFO HandlerThread:32820 [system_monitor.py:probe():185] Publishing system info +2023-03-12 13:42:54,310 DEBUG HandlerThread:32820 [system_info.py:_save_pip():51] Saving list of pip packages installed into the current environment +2023-03-12 13:42:54,311 DEBUG HandlerThread:32820 [system_info.py:_save_pip():67] Saving pip packages done +2023-03-12 13:42:54,311 DEBUG HandlerThread:32820 [system_info.py:_save_conda():74] Saving list of conda packages installed into the current environment +2023-03-12 13:42:54,323 ERROR HandlerThread:32820 [system_info.py:_save_conda():85] Error saving conda packages: [WinError 2] ϵͳҲָļ +Traceback (most recent call last): + File "E:\miniconda\envs\torch11\lib\site-packages\wandb\sdk\internal\system\system_info.py", line 81, in _save_conda + subprocess.call( + File "E:\miniconda\envs\torch11\lib\subprocess.py", line 349, in call + with Popen(*popenargs, **kwargs) as p: + File "E:\miniconda\envs\torch11\lib\subprocess.py", line 951, in __init__ + self._execute_child(args, executable, preexec_fn, close_fds, + File "E:\miniconda\envs\torch11\lib\subprocess.py", line 1420, in _execute_child + hp, ht, pid, tid = _winapi.CreateProcess(executable, args, +FileNotFoundError: [WinError 2] ϵͳҲָļ +2023-03-12 13:42:54,324 DEBUG HandlerThread:32820 [system_info.py:_save_conda():86] Saving conda packages done +2023-03-12 13:42:54,325 INFO HandlerThread:32820 [system_monitor.py:probe():187] Finished publishing system info +2023-03-12 13:42:54,337 INFO Thread-19 :32820 [dir_watcher.py:_on_file_created():275] file/dir created: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\wandb-metadata.json +2023-03-12 13:42:54,338 INFO Thread-19 :32820 [dir_watcher.py:_on_file_created():275] file/dir created: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\conda-environment.yaml +2023-03-12 13:42:54,338 INFO Thread-19 :32820 [dir_watcher.py:_on_file_created():275] file/dir created: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\wandb-summary.json +2023-03-12 13:42:54,338 INFO Thread-19 :32820 [dir_watcher.py:_on_file_created():275] file/dir created: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\requirements.txt +2023-03-12 13:42:54,342 DEBUG SenderThread:32820 [sender.py:send():336] send: files +2023-03-12 13:42:54,342 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-metadata.json with policy now +2023-03-12 13:42:54,356 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: stop_status +2023-03-12 13:42:54,357 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: stop_status +2023-03-12 13:42:55,075 DEBUG SenderThread:32820 [sender.py:send():336] send: telemetry +2023-03-12 13:42:55,339 INFO Thread-19 :32820 [dir_watcher.py:_on_file_created():275] file/dir created: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\output.log +2023-03-12 13:42:56,029 INFO wandb-upload_0:32820 [upload_job.py:push():138] Uploaded file C:\Users\73106\AppData\Local\Temp\tmpx1i8pn77wandb\9jhqbby0-wandb-metadata.json +2023-03-12 13:42:56,239 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:42:57,096 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:42:57,350 INFO Thread-19 :32820 [dir_watcher.py:_on_file_modified():292] file/dir modified: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\output.log +2023-03-12 13:42:58,289 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:00,350 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:02,122 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:43:02,404 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:04,438 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:06,487 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:07,153 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:43:08,527 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:09,372 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:43:09,373 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:43:10,575 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:12,204 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:43:12,626 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:14,419 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:43:14,668 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:16,704 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:17,241 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:43:18,748 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:19,436 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:43:20,794 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:22,306 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:43:22,833 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:23,840 INFO Thread-19 :32820 [dir_watcher.py:_on_file_modified():292] file/dir modified: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\config.yaml +2023-03-12 13:43:24,483 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:43:24,945 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:26,959 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:28,359 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:43:28,986 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:29,517 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:43:31,030 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:33,076 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:33,391 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:43:34,556 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:43:35,119 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:37,173 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:38,413 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:43:39,215 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:39,590 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:43:41,264 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:43,319 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:43,452 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:43:44,639 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:43:45,353 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:47,408 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:48,526 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:43:49,447 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:49,696 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:43:51,494 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:53,545 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:53,555 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:43:54,162 DEBUG SystemMonitor:32820 [system_monitor.py:_start():130] Starting system metrics aggregation loop +2023-03-12 13:43:54,163 DEBUG SenderThread:32820 [sender.py:send():336] send: stats +2023-03-12 13:43:54,751 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:43:55,674 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:57,695 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:59,228 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:43:59,710 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:43:59,810 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:44:01,748 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:03,802 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:04,255 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:44:04,851 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:44:05,854 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:07,898 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:09,275 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:44:09,907 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:44:09,932 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:11,973 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:14,026 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:14,335 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:44:14,922 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:44:16,102 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:18,138 INFO Thread-19 :32820 [dir_watcher.py:_on_file_modified():292] file/dir modified: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\output.log +2023-03-12 13:44:18,155 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:19,510 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:44:19,972 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:44:20,197 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:22,261 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:24,165 DEBUG SenderThread:32820 [sender.py:send():336] send: stats +2023-03-12 13:44:24,308 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:25,206 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:44:25,211 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:44:26,466 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:28,492 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:30,246 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:44:30,247 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:44:30,511 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:32,537 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:34,582 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:35,293 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:44:35,293 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:44:36,640 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:38,683 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:40,318 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:44:40,318 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:44:40,729 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:42,773 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:44,822 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:45,367 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:44:45,368 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:44:46,864 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:48,905 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:50,421 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:44:50,421 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:44:50,949 ERROR gpu :32820 [interfaces.py:monitor():129] Failed to sample metric: Not Supported +2023-03-12 13:44:51,773 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: partial_history +2023-03-12 13:44:51,774 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: partial_history +2023-03-12 13:44:51,775 DEBUG SenderThread:32820 [sender.py:send():336] send: history +2023-03-12 13:44:51,775 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: summary_record +2023-03-12 13:44:51,776 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: partial_history +2023-03-12 13:44:51,776 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: partial_history +2023-03-12 13:44:51,779 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: partial_history +2023-03-12 13:44:51,779 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: partial_history +2023-03-12 13:44:51,779 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: partial_history +2023-03-12 13:44:51,780 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: partial_history +2023-03-12 13:44:51,780 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: partial_history +2023-03-12 13:44:51,781 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: partial_history +2023-03-12 13:44:51,781 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: partial_history +2023-03-12 13:44:51,782 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,783 DEBUG SenderThread:32820 [sender.py:send():336] send: history +2023-03-12 13:44:51,783 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: summary_record +2023-03-12 13:44:51,785 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,785 DEBUG SenderThread:32820 [sender.py:send():336] send: history +2023-03-12 13:44:51,785 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: summary_record +2023-03-12 13:44:51,786 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,786 DEBUG SenderThread:32820 [sender.py:send():336] send: history +2023-03-12 13:44:51,786 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: summary_record +2023-03-12 13:44:51,787 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,787 DEBUG SenderThread:32820 [sender.py:send():336] send: history +2023-03-12 13:44:51,787 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: summary_record +2023-03-12 13:44:51,788 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,788 DEBUG SenderThread:32820 [sender.py:send():336] send: history +2023-03-12 13:44:51,788 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: summary_record +2023-03-12 13:44:51,789 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,790 DEBUG SenderThread:32820 [sender.py:send():336] send: history +2023-03-12 13:44:51,790 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: summary_record +2023-03-12 13:44:51,791 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,791 DEBUG SenderThread:32820 [sender.py:send():336] send: history +2023-03-12 13:44:51,792 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: summary_record +2023-03-12 13:44:51,792 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,793 DEBUG SenderThread:32820 [sender.py:send():336] send: history +2023-03-12 13:44:51,793 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: summary_record +2023-03-12 13:44:51,794 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,794 DEBUG SenderThread:32820 [sender.py:send():336] send: history +2023-03-12 13:44:51,794 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: summary_record +2023-03-12 13:44:51,795 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,796 DEBUG SenderThread:32820 [sender.py:send():336] send: history +2023-03-12 13:44:51,796 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: summary_record +2023-03-12 13:44:51,797 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,853 DEBUG SenderThread:32820 [sender.py:send():336] send: exit +2023-03-12 13:44:51,853 INFO SenderThread:32820 [sender.py:send_exit():559] handling exit code: 0 +2023-03-12 13:44:51,854 INFO SenderThread:32820 [sender.py:send_exit():561] handling runtime: 117 +2023-03-12 13:44:51,856 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,857 INFO SenderThread:32820 [sender.py:send_exit():567] send defer +2023-03-12 13:44:51,858 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: defer +2023-03-12 13:44:51,858 INFO HandlerThread:32820 [handler.py:handle_request_defer():170] handle defer: 0 +2023-03-12 13:44:51,858 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: defer +2023-03-12 13:44:51,858 INFO SenderThread:32820 [sender.py:send_request_defer():583] handle sender defer: 0 +2023-03-12 13:44:51,859 INFO SenderThread:32820 [sender.py:transition_state():587] send defer: 1 +2023-03-12 13:44:51,859 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: defer +2023-03-12 13:44:51,859 INFO HandlerThread:32820 [handler.py:handle_request_defer():170] handle defer: 1 +2023-03-12 13:44:51,860 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: defer +2023-03-12 13:44:51,860 INFO SenderThread:32820 [sender.py:send_request_defer():583] handle sender defer: 1 +2023-03-12 13:44:51,860 INFO SenderThread:32820 [sender.py:transition_state():587] send defer: 2 +2023-03-12 13:44:51,860 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: defer +2023-03-12 13:44:51,861 INFO HandlerThread:32820 [handler.py:handle_request_defer():170] handle defer: 2 +2023-03-12 13:44:51,861 INFO HandlerThread:32820 [system_monitor.py:finish():161] Stopping system monitor +2023-03-12 13:44:51,875 DEBUG SystemMonitor:32820 [system_monitor.py:_start():137] Finished system metrics aggregation loop +2023-03-12 13:44:51,876 DEBUG SystemMonitor:32820 [system_monitor.py:_start():141] Publishing last batch of metrics +2023-03-12 13:44:51,879 INFO HandlerThread:32820 [interfaces.py:finish():175] Joined cpu +2023-03-12 13:44:51,880 INFO HandlerThread:32820 [interfaces.py:finish():175] Joined disk +2023-03-12 13:44:51,923 INFO HandlerThread:32820 [interfaces.py:finish():175] Joined gpu +2023-03-12 13:44:51,923 INFO HandlerThread:32820 [interfaces.py:finish():175] Joined memory +2023-03-12 13:44:51,923 INFO HandlerThread:32820 [interfaces.py:finish():175] Joined network +2023-03-12 13:44:51,924 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: defer +2023-03-12 13:44:51,924 INFO SenderThread:32820 [sender.py:send_request_defer():583] handle sender defer: 2 +2023-03-12 13:44:51,924 INFO SenderThread:32820 [sender.py:transition_state():587] send defer: 3 +2023-03-12 13:44:51,924 DEBUG SenderThread:32820 [sender.py:send():336] send: stats +2023-03-12 13:44:51,925 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: defer +2023-03-12 13:44:51,925 INFO HandlerThread:32820 [handler.py:handle_request_defer():170] handle defer: 3 +2023-03-12 13:44:51,925 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: defer +2023-03-12 13:44:51,925 INFO SenderThread:32820 [sender.py:send_request_defer():583] handle sender defer: 3 +2023-03-12 13:44:51,925 INFO SenderThread:32820 [sender.py:transition_state():587] send defer: 4 +2023-03-12 13:44:51,925 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: defer +2023-03-12 13:44:51,925 INFO HandlerThread:32820 [handler.py:handle_request_defer():170] handle defer: 4 +2023-03-12 13:44:51,926 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: defer +2023-03-12 13:44:51,926 INFO SenderThread:32820 [sender.py:send_request_defer():583] handle sender defer: 4 +2023-03-12 13:44:51,926 INFO SenderThread:32820 [sender.py:transition_state():587] send defer: 5 +2023-03-12 13:44:51,926 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: defer +2023-03-12 13:44:51,926 INFO HandlerThread:32820 [handler.py:handle_request_defer():170] handle defer: 5 +2023-03-12 13:44:51,926 DEBUG SenderThread:32820 [sender.py:send():336] send: summary +2023-03-12 13:44:51,927 INFO SenderThread:32820 [sender.py:_save_file():1321] saving file wandb-summary.json with policy end +2023-03-12 13:44:51,927 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: defer +2023-03-12 13:44:51,927 INFO SenderThread:32820 [sender.py:send_request_defer():583] handle sender defer: 5 +2023-03-12 13:44:51,927 INFO SenderThread:32820 [sender.py:transition_state():587] send defer: 6 +2023-03-12 13:44:51,928 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: defer +2023-03-12 13:44:51,928 INFO HandlerThread:32820 [handler.py:handle_request_defer():170] handle defer: 6 +2023-03-12 13:44:51,928 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: defer +2023-03-12 13:44:51,928 INFO SenderThread:32820 [sender.py:send_request_defer():583] handle sender defer: 6 +2023-03-12 13:44:51,928 INFO SenderThread:32820 [sender.py:transition_state():587] send defer: 7 +2023-03-12 13:44:51,928 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: status_report +2023-03-12 13:44:51,928 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: defer +2023-03-12 13:44:51,929 INFO HandlerThread:32820 [handler.py:handle_request_defer():170] handle defer: 7 +2023-03-12 13:44:51,929 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: defer +2023-03-12 13:44:51,929 INFO SenderThread:32820 [sender.py:send_request_defer():583] handle sender defer: 7 +2023-03-12 13:44:51,956 INFO Thread-19 :32820 [dir_watcher.py:_on_file_modified():292] file/dir modified: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\wandb-summary.json +2023-03-12 13:44:52,857 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: 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13:44:58,021 INFO SenderThread:32820 [dir_watcher.py:finish():406] scan save: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\output.log output.log +2023-03-12 13:44:58,028 INFO SenderThread:32820 [dir_watcher.py:finish():406] scan save: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\requirements.txt requirements.txt +2023-03-12 13:44:58,034 INFO SenderThread:32820 [dir_watcher.py:finish():406] scan save: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\wandb-metadata.json wandb-metadata.json +2023-03-12 13:44:58,036 INFO SenderThread:32820 [dir_watcher.py:finish():406] scan save: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\wandb-summary.json wandb-summary.json +2023-03-12 13:44:58,042 INFO SenderThread:32820 [sender.py:transition_state():587] send defer: 10 +2023-03-12 13:44:58,042 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: poll_exit +2023-03-12 13:44:58,042 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: defer +2023-03-12 13:44:58,043 INFO HandlerThread:32820 [handler.py:handle_request_defer():170] handle defer: 10 +2023-03-12 13:44:58,047 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: defer +2023-03-12 13:44:58,047 INFO SenderThread:32820 [sender.py:send_request_defer():583] handle sender defer: 10 +2023-03-12 13:44:58,047 INFO SenderThread:32820 [file_pusher.py:finish():162] shutting down file pusher +2023-03-12 13:44:58,943 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: keepalive +2023-03-12 13:44:59,218 INFO wandb-upload_3:32820 [upload_job.py:push():138] Uploaded file E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\wandb-summary.json +2023-03-12 13:44:59,688 INFO wandb-upload_0:32820 [upload_job.py:push():138] Uploaded file E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\config.yaml +2023-03-12 13:44:59,701 INFO wandb-upload_2:32820 [upload_job.py:push():138] Uploaded file E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\requirements.txt +2023-03-12 13:44:59,779 INFO wandb-upload_1:32820 [upload_job.py:push():138] Uploaded file E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\files\output.log +2023-03-12 13:44:59,991 INFO Thread-18 :32820 [sender.py:transition_state():587] send defer: 11 +2023-03-12 13:44:59,991 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: defer +2023-03-12 13:44:59,991 INFO HandlerThread:32820 [handler.py:handle_request_defer():170] handle defer: 11 +2023-03-12 13:44:59,991 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: defer +2023-03-12 13:44:59,991 INFO SenderThread:32820 [sender.py:send_request_defer():583] handle sender defer: 11 +2023-03-12 13:44:59,992 INFO SenderThread:32820 [file_pusher.py:join():167] waiting for file pusher +2023-03-12 13:44:59,992 INFO SenderThread:32820 [sender.py:transition_state():587] send defer: 12 +2023-03-12 13:44:59,992 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: defer +2023-03-12 13:44:59,992 INFO HandlerThread:32820 [handler.py:handle_request_defer():170] handle defer: 12 +2023-03-12 13:44:59,992 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: defer +2023-03-12 13:44:59,992 INFO SenderThread:32820 [sender.py:send_request_defer():583] handle sender defer: 12 +2023-03-12 13:45:00,962 INFO SenderThread:32820 [sender.py:transition_state():587] send defer: 13 +2023-03-12 13:45:00,962 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: defer +2023-03-12 13:45:00,962 INFO HandlerThread:32820 [handler.py:handle_request_defer():170] handle defer: 13 +2023-03-12 13:45:00,962 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: defer +2023-03-12 13:45:00,962 INFO SenderThread:32820 [sender.py:send_request_defer():583] handle sender defer: 13 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[handler.py:handle_request():144] handle_request: get_summary +2023-03-12 13:45:00,965 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: sampled_history +2023-03-12 13:45:00,966 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: poll_exit +2023-03-12 13:45:00,966 DEBUG SenderThread:32820 [sender.py:send_request():363] send_request: server_info +2023-03-12 13:45:01,721 INFO MainThread:32820 [wandb_run.py:_footer_history_summary_info():3442] rendering history +2023-03-12 13:45:01,723 INFO MainThread:32820 [wandb_run.py:_footer_history_summary_info():3474] rendering summary +2023-03-12 13:45:01,741 INFO MainThread:32820 [wandb_run.py:_footer_sync_info():3398] logging synced files +2023-03-12 13:45:01,743 DEBUG HandlerThread:32820 [handler.py:handle_request():144] handle_request: shutdown +2023-03-12 13:45:01,743 INFO HandlerThread:32820 [handler.py:finish():841] shutting down handler +2023-03-12 13:45:01,966 INFO WriterThread:32820 [datastore.py:close():299] close: E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\run-g1s73ewz.wandb +2023-03-12 13:45:02,730 INFO SenderThread:32820 [sender.py:finish():1493] shutting down sender +2023-03-12 13:45:02,731 INFO SenderThread:32820 [file_pusher.py:finish():162] shutting down file pusher +2023-03-12 13:45:02,731 INFO SenderThread:32820 [file_pusher.py:join():167] waiting for file pusher diff --git a/my_homework/ch4/run-20230312_134251-g1s73ewz/logs/debug.log b/my_homework/ch4/run-20230312_134251-g1s73ewz/logs/debug.log new file mode 100644 index 0000000..cbb0a65 --- /dev/null +++ b/my_homework/ch4/run-20230312_134251-g1s73ewz/logs/debug.log @@ -0,0 +1,27 @@ +2023-03-12 13:42:51,599 INFO MainThread:36176 [wandb_setup.py:_flush():68] Configure stats pid to 36176 +2023-03-12 13:42:51,599 INFO MainThread:36176 [wandb_setup.py:_flush():68] Loading settings from C:\Users\73106\.config\wandb\settings +2023-03-12 13:42:51,600 INFO MainThread:36176 [wandb_setup.py:_flush():68] Loading settings from E:\workspace\ANM\PPOxFamily\wandb\settings +2023-03-12 13:42:51,601 INFO MainThread:36176 [wandb_setup.py:_flush():68] Loading settings from environment variables: {'_require_service': 'True'} +2023-03-12 13:42:51,602 INFO MainThread:36176 [wandb_setup.py:_flush():68] Inferring run settings from compute environment: {'program_relpath': 'ch4\\homework4.py', 'program': 'e:\\workspace\\ANM\\PPOxFamily\\ch4\\homework4.py'} +2023-03-12 13:42:51,603 INFO MainThread:36176 [wandb_setup.py:_flush():68] Applying login settings: {'anonymous': 'must'} +2023-03-12 13:42:51,603 INFO MainThread:36176 [wandb_init.py:_log_setup():492] Logging user logs to E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\logs\debug.log +2023-03-12 13:42:51,604 INFO MainThread:36176 [wandb_init.py:_log_setup():493] Logging internal logs to E:\workspace\ANM\PPOxFamily\wandb\run-20230312_134251-g1s73ewz\logs\debug-internal.log +2023-03-12 13:42:51,604 INFO MainThread:36176 [wandb_init.py:init():532] calling init triggers +2023-03-12 13:42:51,605 INFO MainThread:36176 [wandb_init.py:init():538] wandb.init called with sweep_config: {} +config: {} +2023-03-12 13:42:51,605 INFO MainThread:36176 [wandb_init.py:init():588] starting backend +2023-03-12 13:42:51,605 INFO MainThread:36176 [wandb_init.py:init():592] setting up manager +2023-03-12 13:42:51,610 INFO MainThread:36176 [backend.py:_multiprocessing_setup():106] multiprocessing start_methods=spawn, using: spawn +2023-03-12 13:42:51,618 INFO MainThread:36176 [wandb_init.py:init():599] backend started and connected +2023-03-12 13:42:51,620 INFO MainThread:36176 [wandb_init.py:init():687] updated telemetry +2023-03-12 13:42:51,692 INFO MainThread:36176 [wandb_init.py:init():727] communicating run to backend with 60.0 second timeout +2023-03-12 13:42:53,333 INFO MainThread:36176 [wandb_run.py:_on_init():2134] communicating current version +2023-03-12 13:42:54,067 INFO MainThread:36176 [wandb_run.py:_on_init():2143] got version response upgrade_message: "wandb version 0.13.11 is available! To upgrade, please run:\n $ pip install wandb --upgrade" + +2023-03-12 13:42:54,067 INFO MainThread:36176 [wandb_init.py:init():775] starting run threads in backend +2023-03-12 13:42:54,357 INFO MainThread:36176 [wandb_run.py:_console_start():2114] atexit reg +2023-03-12 13:42:54,357 INFO MainThread:36176 [wandb_run.py:_redirect():1969] redirect: SettingsConsole.WRAP_RAW +2023-03-12 13:42:54,357 INFO MainThread:36176 [wandb_run.py:_redirect():2034] Wrapping output streams. +2023-03-12 13:42:54,357 INFO MainThread:36176 [wandb_run.py:_redirect():2059] Redirects installed. +2023-03-12 13:42:54,358 INFO MainThread:36176 [wandb_init.py:init():817] run started, returning control to user process +2023-03-12 13:45:02,818 WARNING MsgRouterThr:36176 [router.py:message_loop():77] message_loop has been closed From 062328f87eb6b31b64468815417eb68b20b1485d Mon Sep 17 00:00:00 2001 From: GuopingWin <731061720@qq.com> Date: Mon, 10 Apr 2023 16:05:46 +0800 Subject: [PATCH 2/4] [me] doing homework5 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b/my_homework/ch5/lstm.py new file mode 100644 index 0000000..b483882 --- /dev/null +++ b/my_homework/ch5/lstm.py @@ -0,0 +1,197 @@ +""" +Long Short Term Memory (LSTM) is a kind of recurrent neural network that can capture long-short term information. +This document mainly includes: +- Pytorch implementation for LSTM. +- An example to test LSTM. +For beginners, you can refer to to learn the basics about how LSTM works. +""" +from typing import Optional, Union, Tuple, List, Dict +import math +import torch +import torch.nn as nn +from ding.torch_utils import build_normalization + + +class LSTM(nn.Module): + """ + **Overview:** + Implementation of LSTM cell with layer norm. + """ + + def __init__( + self, + input_size: int, + hidden_size: int, + num_layers: int, + norm_type: Optional[str] = 'LN', + dropout: float = 0. + ) -> None: + # Initialize arguments. + super(LSTM, self).__init__() + self.input_size = input_size + self.hidden_size = hidden_size + self.num_layers = num_layers + # Initialize normalization functions. + norm_func = build_normalization(norm_type) + self.norm = nn.ModuleList([norm_func(hidden_size * 4) for _ in range(2 * num_layers)]) + # Initialize LSTM parameters. + self.wx = nn.ParameterList() + self.wh = nn.ParameterList() + dims = [input_size] + [hidden_size] * num_layers + for l in range(num_layers): + self.wx.append(nn.Parameter(torch.zeros(dims[l], dims[l + 1] * 4))) + self.wh.append(nn.Parameter(torch.zeros(hidden_size, hidden_size * 4))) + self.bias = nn.Parameter(torch.zeros(num_layers, hidden_size * 4)) + # Initialize the Dropout Layer. + self.use_dropout = dropout > 0. + if self.use_dropout: + self.dropout = nn.Dropout(dropout) + self._init() + + # Dealing with different types of input and return preprocessed prev_state. + def _before_forward(self, inputs: torch.Tensor, prev_state: Union[None, List[Dict]]) -> torch.Tensor: + seq_len, batch_size = inputs.shape[:2] + # If prev_state is None, it indicates that this is the beginning of a sequence. In this case, prev_state will be initialized as zero. + if prev_state is None: + zeros = torch.zeros(self.num_layers, batch_size, self.hidden_size, dtype=inputs.dtype, device=inputs.device) + prev_state = (zeros, zeros) + # If prev_state is not None, then preprocess it into one batch. + else: + assert len(prev_state) == batch_size + state = [[v for v in prev.values()] for prev in prev_state] + state = list(zip(*state)) + prev_state = [torch.cat(t, dim=1) for t in state] + + return prev_state + + def _init(self): + # Initialize parameters. Each parameter is initialized using a uniform distribution of: $$U(-\sqrt {\frac 1 {HiddenSize}}, -\sqrt {\frac 1 {HiddenSize}})$$ + gain = math.sqrt(1. / self.hidden_size) + for l in range(self.num_layers): + torch.nn.init.uniform_(self.wx[l], -gain, gain) + torch.nn.init.uniform_(self.wh[l], -gain, gain) + if self.bias is not None: + torch.nn.init.uniform_(self.bias[l], -gain, gain) + + def forward( + self, + inputs: torch.Tensor, + prev_state: torch.Tensor, + ) -> Tuple[torch.Tensor, Union[torch.Tensor, list]]: + # The shape of input is: [sequence length, batch size, input size] + seq_len, batch_size = inputs.shape[:2] + prev_state = self._before_forward(inputs, prev_state) + + H, C = prev_state + x = inputs + next_state = [] + for l in range(self.num_layers): + h, c = H[l], C[l] + new_x = [] + for s in range(seq_len): + # Calculate $$z, z^i, z^f, z^o$$ simultaneously. + gate = self.norm[l * 2](torch.matmul(x[s], self.wx[l]) + ) + self.norm[l * 2 + 1](torch.matmul(h, self.wh[l])) + if self.bias is not None: + gate += self.bias[l] + gate = list(torch.chunk(gate, 4, dim=1)) + i, f, o, z = gate + # $$z^i = \sigma (Wx^ix^t + Wh^ih^{t-1})$$ + i = torch.sigmoid(i) + # $$z^f = \sigma (Wx^fx^t + Wh^fh^{t-1})$$ + f = torch.sigmoid(f) + # $$z^o = \sigma (Wx^ox^t + Wh^oh^{t-1})$$ + o = torch.sigmoid(o) + # $$z = tanh(Wxx^t + Whh^{t-1})$$ + z = torch.tanh(z) + # $$c^t = z^f \odot c^{t-1}+z^i \odot z$$ + c = f * c + i * z + # $$h^t = z^o \odot tanh(c^t)$$ + h = o * torch.tanh(c) + new_x.append(h) + next_state.append((h, c)) + x = torch.stack(new_x, dim=0) + # Dropout layer. + if self.use_dropout and l != self.num_layers - 1: + x = self.dropout(x) + next_state = [torch.stack(t, dim=0) for t in zip(*next_state)] + # Return list type, split the next_state . + h, c = next_state + batch_size = h.shape[1] + # Split h with shape [num_layers, batch_size, hidden_size] to a list with length batch_size and each element is a tensor with shape [num_layers, 1, hidden_size]. The same operation is performed on c. + next_state = [torch.chunk(h, batch_size, dim=1), torch.chunk(c, batch_size, dim=1)] + next_state = list(zip(*next_state)) + next_state = [{k: v for k, v in zip(['h', 'c'], item)} for item in next_state] + return x, next_state + + +def pack_data(data: List[torch.Tensor], traj_len: int) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Overview: + You need to pack variable-length data to regular tensor, return tensor and corresponding mask. + If len(data_i) < traj_len, use `null_padding`, + else split the whole sequences info different trajectories. + Returns: + - tensor (:obj:`torch.Tensor`): dtype (torch.float32), shape (traj_len, B, N) + - mask (:obj:`torch.Tensor`): dtype (torch.float32), shape (traj_len, B) + """ + + traj_list = [] + mask_list = [] + + for traj in data: + length, num = traj.shape + # less than traj_len use null padding + if length < traj_len: + pad_traj = torch.concat([traj, torch.zeros(traj_len - length, num)]) + pad_mask = torch.ones(traj_len) + pad_mask[length:] = 0.0 + traj_list.append(pad_traj) + mask_list.append(pad_mask) + # greater than traj_len use split and forward padding + else: + for i in range(0, length, traj_len): + if i+traj_len <= length: + pad_traj = traj[i:i+traj_len] + else: + pad_traj = traj[-traj_len:] + traj_list.append(pad_traj) + mask_list.append(torch.ones(traj_len)) + + traj_pad_and_split = torch.stack(traj_list, dim=1) + mask_pad_and_split = torch.stack(mask_list, dim=1) + + return traj_pad_and_split, mask_pad_and_split + + +def test_lstm(): + seq_len_list = [32, 49, 24, 78, 45] + traj_len = 32 + N = 10 + hidden_size = 32 + num_layers = 2 + + variable_len_data = [torch.rand(s, N) for s in seq_len_list] + input_, mask = pack_data(variable_len_data, traj_len) + assert isinstance(input_, torch.Tensor), type(input_) + batch_size = input_.shape[1] + assert batch_size == 9, "packed data must have 9 trajectories" + lstm = LSTM(N, hidden_size=hidden_size, num_layers=num_layers, norm_type='LN', dropout=0.1) + + prev_state = None + for s in range(traj_len): + input_step = input_[s:s + 1] + output, prev_state = lstm(input_step, prev_state) + + assert output.shape == (1, batch_size, hidden_size) + assert len(prev_state) == batch_size + assert prev_state[0]['h'].shape == (num_layers, 1, hidden_size) + loss = (output * mask.unsqueeze(-1)).mean() + loss.backward() + for _, m in lstm.named_parameters(): + assert isinstance(m.grad, torch.Tensor) + print('finished') + + +if __name__ == '__main__': + test_lstm() From 65ecebf9b7e87012f67d672c631d3b8d3df427b2 Mon Sep 17 00:00:00 2001 From: GuopingWin <731061720@qq.com> Date: Sat, 22 Apr 2023 16:07:26 +0800 Subject: [PATCH 3/4] add ch6 --- my_homework/ch6/ppof_ch6_code_p1.py | 95 +++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 my_homework/ch6/ppof_ch6_code_p1.py diff --git a/my_homework/ch6/ppof_ch6_code_p1.py b/my_homework/ch6/ppof_ch6_code_p1.py new file mode 100644 index 0000000..80f6132 --- /dev/null +++ b/my_homework/ch6/ppof_ch6_code_p1.py @@ -0,0 +1,95 @@ +import numpy as np +import torch + + +def get_agent_id_feature(agent_id, agent_num): + agent_id_feature = torch.zeros(agent_num) + agent_id_feature[agent_id] = 1 + return agent_id_feature + + +def get_movement_feature(): + # for simplicity, we use random movement feature here + movement_feature = torch.randint(0, 2, (8, )) + return movement_feature + + +def get_own_feature(): + # for simplicity, we use random own feature here + return torch.randn(10) + + +def get_ally_visible_feature(): + # this function only return the visible feature of one ally + # for simplicity, we use random tensor as ally visible feature while zero tensor as ally invisible feature + if np.random.random() > 0.5: + ally_visible_feature = torch.randn(4) + else: + ally_visible_feature = torch.zeros(4) + return ally_visible_feature + + +def get_enemy_visible_feature(): + # this function only return the visible feature of one enemy + # for simplicity, we use random tensor as enemy visible feature while zero tensor as enemy invisible feature + if np.random.random() > 0.8: + enemy_visible_feature = torch.randn(4) + else: + enemy_visible_feature = torch.zeros(4) + return enemy_visible_feature + + +def get_ind_global_state(agent_id, ally_agent_num, enemy_agent_num): + # You need to implement this function + + agent_id_feature = get_agent_id_feature(agent_id, ally_agent_num+enemy_agent_num) + own_feature = get_own_feature() + ally_visible_features = [] + for i in range(ally_agent_num): + if i != agent_id: + ally_visible_features.append(get_ally_visible_feature()) + + enemy_visible_feature = [] + for i in range(enemy_agent_num): + enemy_visible_feature.append(get_enemy_visible_feature()) + + + return torch.cat([agent_id_feature, own_feature, *ally_visible_features, *enemy_visible_feature]) + + +def get_ep_global_state(agent_id, ally_agent_num, enemy_agent_num): + # In many multi-agent environments such as SMAC, the global state is the simplified version of the combination + # of all the agent's independent state, and the concrete implementation depends on the characteris of environment. + # For simplicity, we use random feature here. + ally_center_feature = torch.randn(8) + enemy_center_feature = torch.randn(8) + return torch.cat([ally_center_feature, enemy_center_feature]) + + +def get_as_global_state(agent_id, ally_agent_num, enemy_agent_num): + # You need to implement this function + ind_global_state = get_ind_global_state(agent_id, ally_agent_num, enemy_agent_num) + ep_global_state = get_ep_global_state(agent_id, ally_agent_num, enemy_agent_num) + + return torch.cat([ind_global_state, ep_global_state]) + + +def test_global_state(): + ally_agent_num = 3 + enemy_agent_num = 5 + # get independent global state, which usually used in decentralized training + for agent_id in range(ally_agent_num): + ind_global_state = get_ind_global_state(agent_id, ally_agent_num, enemy_agent_num) + assert isinstance(ind_global_state, torch.Tensor) + # get environment provide global state, which is the same for all agents, used in centralized training + for agent_id in range(ally_agent_num): + ep_global_state = get_ep_global_state(agent_id, ally_agent_num, enemy_agent_num) + assert isinstance(ep_global_state, torch.Tensor) + # get naive agent-specific global state, which is the specific for each agent, used in centralized training + for agent_id in range(ally_agent_num): + as_global_state = get_as_global_state(agent_id, ally_agent_num, enemy_agent_num) + assert isinstance(as_global_state, torch.Tensor) + + +if __name__ == "__main__": + test_global_state() From 208a6dd727cdfa4a34b959193c233c3eb8bf2978 Mon Sep 17 00:00:00 2001 From: GuopingWin <731061720@qq.com> Date: Sat, 29 Apr 2023 23:08:16 +0800 Subject: [PATCH 4/4] ch6-h2 --- my_homework/ch6/h2_mpe/ckpt/ckpt_best.pth.tar | Bin 0 -> 5130685 bytes .../ch6/h2_mpe/ckpt/iteration_0.pth.tar | Bin 0 -> 5130685 bytes 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