From 355c237a04447113972626bd5433e6c0b36124e2 Mon Sep 17 00:00:00 2001 From: umutucak Date: Tue, 5 Dec 2023 09:54:54 +0100 Subject: [PATCH 01/13] init --- .../continuous_ppo/momappo_crazyrl.py | 552 ++++++++++++++++++ pyproject.toml | 1 + 2 files changed, 553 insertions(+) create mode 100644 momaland/learning/continuous_ppo/momappo_crazyrl.py diff --git a/momaland/learning/continuous_ppo/momappo_crazyrl.py b/momaland/learning/continuous_ppo/momappo_crazyrl.py new file mode 100644 index 00000000..cfc59851 --- /dev/null +++ b/momaland/learning/continuous_ppo/momappo_crazyrl.py @@ -0,0 +1,552 @@ +"""This is an MAPPO implementation which runs the environment on CPU (PettingZoo) and the learning on jax compiled functions. + +Should be way faster than the original MAPPO implementation based on torch. Main changes compared to full jax version: +- CPU environment (PZ) instead of GPU environment +- Relies on Supersuit wrappers +- No vectorized environment (no autoreset) +- Step function, and by extension train function are not jittable or vmappable due to env step not being jittable +The last two points are probably the main reason why this implementation is slower than the full jax version. +""" +import argparse +import os +import time +from distutils.util import strtobool +from typing import List, NamedTuple, Sequence, Tuple + +import chex +import distrax +import flax.linen as nn +import jax +import jax.numpy as jnp +import numpy as np +import optax +import orbax.checkpoint +from crazy_rl.utils.experiments_and_plots import save_results +from distrax import MultivariateNormalDiag +from etils import epath +from flax.linen.initializers import constant, orthogonal +from flax.training.train_state import TrainState +from jax import vmap +from supersuit import agent_indicator_v0, clip_actions_v0, normalize_obs_v0 +from tqdm import tqdm + +from momaland.envs.crazyrl.catch import catch_v0 as Catch # noqa +from momaland.envs.crazyrl.escort import escort_v0 as Escort # noqa +from momaland.envs.crazyrl.surround import surround_v0 as Surround # noqa +from momaland.utils.env import ParallelEnv +from momaland.utils.wrappers import NormalizeReward # waiting PR #19 + + +def parse_args(): + # fmt: off + parser = argparse.ArgumentParser() + parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"), + help="the name of this experiment") + parser.add_argument("--seed", type=int, default=1, + help="seed of the experiment") + parser.add_argument("--debug", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True, help="run in debug mode") + + # Algorithm specific arguments + parser.add_argument("--num-steps", type=int, default=1280, help="the number of steps per epoch (higher batch size should be better)") + parser.add_argument("--total-timesteps", type=int, default=1e5, + help="total timesteps of the experiments") + parser.add_argument("--update-epochs", type=int, default=2, help="the number epochs to update the policy") + parser.add_argument("--num-minibatches", type=int, default=2, help="the number of minibatches (keep small in MARL)") + parser.add_argument("--gamma", type=float, default=0.99, + help="the discount factor gamma") + parser.add_argument("--lr", type=float, default=1e-3, + help="the learning rate of the policy network optimizer") + parser.add_argument("--gae-lambda", type=float, default=0.99, + help="the lambda for the generalized advantage estimation") + parser.add_argument("--clip-eps", type=float, default=0.2, + help="the epsilon for clipping in the policy objective") + parser.add_argument("--ent-coef", type=float, default=0.0, + help="the coefficient for the entropy bonus") + parser.add_argument("--vf-coef", type=float, default=0.8, + help="the coefficient for the value function loss") + parser.add_argument("--max-grad-norm", type=float, default=0.5, + help="the maximum norm for the gradient clipping") + parser.add_argument("--activation", type=str, default="tanh", + help="the activation function for the neural networks") + parser.add_argument("--anneal-lr", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True, + help="whether to anneal the learning rate linearly") + + args = parser.parse_args() + # fmt: on + return args + + +class Actor(nn.Module): + """Actor class for the agent.""" + + action_dim: Sequence[int] + activation: str = "tanh" + + @nn.compact + def __call__(self, local_obs_and_id: jnp.ndarray): + if self.activation == "relu": + activation = nn.relu + else: + activation = nn.tanh + actor_mean = nn.Dense(256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(local_obs_and_id) + actor_mean = activation(actor_mean) + actor_mean = nn.Dense(256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(actor_mean) + actor_mean = activation(actor_mean) + actor_mean = nn.Dense(self.action_dim, kernel_init=orthogonal(0.01), bias_init=constant(0.0))(actor_mean) + actor_logtstd = self.param("log_std", nn.initializers.zeros, (self.action_dim,)) + pi: MultivariateNormalDiag = distrax.MultivariateNormalDiag(actor_mean, jnp.exp(actor_logtstd)) + return pi + + +class Critic(nn.Module): + """Critic class for the agent.""" + + activation: str = "tanh" + + @nn.compact + def __call__(self, global_obs: jnp.ndarray): + if self.activation == "relu": + activation = nn.relu + else: + activation = nn.tanh + + critic = nn.Dense(256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(global_obs) + critic = activation(critic) + critic = nn.Dense(256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(critic) + critic = activation(critic) + critic = nn.Dense(1, kernel_init=orthogonal(1.0), bias_init=constant(0.0))(critic) + + return jnp.squeeze(critic, axis=-1) + + +class Transition(NamedTuple): + terminated: jnp.ndarray + joint_actions: jnp.ndarray # shape is (num_agents, action_dim) + value: jnp.ndarray + reward: jnp.ndarray + log_prob: jnp.ndarray + obs: jnp.ndarray # shape is (num_agents, obs_dim) + global_obs: jnp.ndarray # shape is (global_obs_dim) + info: jnp.ndarray + + +class Buffer: + """A numpy buffer to accumulate the samples, normally faster than jax based because mutable.""" + + def __init__(self, batch_size: int, joint_actions_shape, obs_shape, global_obs_shape, num_agents): + self.batch_size = batch_size + self.joint_actions = np.zeros((batch_size, *joint_actions_shape)) + self.obs = np.zeros((batch_size, *obs_shape)) + self.global_obs = np.zeros((batch_size, *global_obs_shape)) + self.value = np.zeros(batch_size) + self.reward = np.zeros(batch_size) + self.log_prob = np.zeros((batch_size, num_agents)) + self.terminated = np.zeros(batch_size) + self.info = np.zeros(batch_size) # TODO + self.idx = 0 + + def add( + self, + terminated: bool, + joint_actions: np.ndarray, + obs: np.ndarray, + global_obs: np.ndarray, + value: float, + reward: float, + log_prob: np.ndarray, + info: dict, + ): + self.terminated[self.idx] = terminated + self.joint_actions[self.idx] = joint_actions + self.obs[self.idx] = obs + self.global_obs[self.idx] = global_obs + self.value[self.idx] = value + self.reward[self.idx] = reward + self.log_prob[self.idx] = log_prob + # TODO self.info[self.idx] = info + self.idx += 1 + + def flush(self): + self.idx = 0 + + def to_transition(self): + return Transition( + terminated=jnp.array(self.terminated), + joint_actions=jnp.array(self.joint_actions), + obs=jnp.array(self.obs), + global_obs=jnp.array(self.global_obs), + value=jnp.array(self.value), + reward=jnp.array(self.reward), + log_prob=jnp.array(self.log_prob), + info=jnp.array(self.info), + ) + + +def train(args, key: chex.PRNGKey): + num_updates = int(args.total_timesteps // args.num_steps) + minibatch_size = int(args.num_steps // args.num_minibatches) + + def linear_schedule(count): + frac = 1.0 - (count // (args.num_minibatches * args.update_epochs)) / num_updates + return args.lr * frac + + num_drones = 3 + # env = Surround( + # drone_ids=np.arange(num_drones), + # init_flying_pos=np.array( + # [ + # [0.0, 0.0, 1.0], + # [0.0, 1.0, 1.0], + # # [1.0, 0.0, 1.0], + # # [1.0, 2.0, 2.0], + # # [2.0, 0.5, 1.0], + # # [2.0, 2.5, 2.0], + # # [2.0, 1.0, 2.5], + # # [0.5, 0.5, 0.5], + # ] + # ), + # target_location=np.array([1.0, 1.0, 2.0]), + # multi_obj=False, + # size=5, + # # target_speed=0.15, + # # final_target_location=jnp.array([-2.0, -2.0, 1.0]), + # ) + + env: ParallelEnv = Catch( + drone_ids=np.arange(num_drones), + init_flying_pos=np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 1.0], [1.0, 0.0, 1.0]]), + ) + + env = clip_actions_v0(env) + env = normalize_obs_v0(env, env_min=-1.0, env_max=1.0) + env = agent_indicator_v0(env) + env = NormalizeReward(env, args.gamma) + + # Initial reset to have correct dimensions in the observations + env.reset(seed=args.seed) + + # INIT NETWORKS + single_action_space = env.action_space(env.possible_agents[0]) + single_obs_space = env.observation_space(env.possible_agents[0]) + + actor = Actor(single_action_space.shape[0], activation=args.activation) + critic = Critic(activation=args.activation) + key, actor_key, critic_key = jax.random.split(key, 3) + dummy_local_obs_and_id = jnp.zeros(single_obs_space.shape) + dummy_global_obs = jnp.zeros(env.state().shape) + actor_params = actor.init(actor_key, dummy_local_obs_and_id) + critic_params = critic.init(critic_key, dummy_global_obs) + if args.anneal_lr: + tx = optax.chain( + optax.clip_by_global_norm(args.max_grad_norm), + optax.adam(learning_rate=linear_schedule, eps=1e-5), + ) + else: + tx = optax.chain( + optax.clip_by_global_norm(args.max_grad_norm), + optax.adam(args.lr, eps=1e-5), + ) + + actor_train_state = TrainState.create( + apply_fn=actor.apply, + params=actor_params, + tx=tx, + ) + + critic_train_state = TrainState.create( + apply_fn=critic.apply, + params=critic_params, + tx=tx, + ) + + # BUFFER + buffer = Buffer( + batch_size=args.num_steps, + joint_actions_shape=(num_drones, single_action_space.shape[0]), + obs_shape=(num_drones, single_obs_space.shape[0]), + global_obs_shape=env.state().shape, + num_agents=num_drones, + ) + + def _to_array_obs(obs: dict): + """Converts a dict of observations to a numpy array of shape (num_agents, obs_dim)""" + return np.stack([obs[agent] for agent in env.possible_agents]) + + @jax.jit + def _ma_get_pi(params, obs: jnp.ndarray): + """Gets the actions for all agents at once. This is done with a for loop because distrax does not like vmapping.""" + return [actor.apply(params, obs[i]) for i in range(num_drones)] + + def _batched_ma_get_pi(params, obs: jnp.ndarray): + """Gets the actions for all agents in all the envs at once. This is done with a for loop because distrax does not like vmapping.""" + return [actor.apply(params, obs[:, i, :]) for i in range(num_drones)] + + @jax.jit + def _ma_sample_and_log_prob_from_pi(pi: List[MultivariateNormalDiag], key: chex.PRNGKey): + """Samples actions for all agents in all the envs at once. This is done with a for loop because distrax does not like vmapping. + + Args: + pi (List[MultivariateNormalDiag]): List of distrax distributions for agent actions (batched over envs) + key (chex.PRNGKey): PRNGKey to use for sampling: size should be (num_agents, 2) + """ + return [pi[i].sample_and_log_prob(seed=key[i]) for i in range(num_drones)] + + # Batch get value + vmapped_get_value = vmap(critic.apply, in_axes=(None, 0)) + + critic.apply = jax.jit(critic.apply) + + @jax.jit + def _calculate_gae(traj_batch, last_val): + def _get_advantages(gae_and_next_value, transition): + gae, next_value = gae_and_next_value + done, value, reward = ( + transition.terminated, + transition.value, + transition.reward, + ) + delta = reward + args.gamma * next_value * (1 - done) - value + gae = delta + args.gamma * args.gae_lambda * (1 - done) * gae + return (gae, value), gae + + _, advantages = jax.lax.scan( + _get_advantages, + (jnp.zeros_like(last_val), last_val), + traj_batch, + reverse=True, + unroll=16, + ) + return advantages, advantages + traj_batch.value + + @jax.jit + def _update_minbatch(actor_critic_train_state, batch_info): + actor_train_state, critic_train_state = actor_critic_train_state + traj_batch, advantages, targets = batch_info + + def _loss_fn(actor_params, critic_params, traj_batch, gae, targets): + # Batch values are in shape (batch_size, num_drones, ...) + + # RERUN NETWORK + pi = _batched_ma_get_pi( + actor_params, traj_batch.obs + ) # this is a list of distributions with batch_shape of minibatch_size and event shape of action_dim + new_value = vmapped_get_value(critic_params, traj_batch.global_obs) + # MA Log Prob: shape (num_drones, minibatch_size) + new_log_probs = jnp.array([pi[i].log_prob(traj_batch.joint_actions[:, i, :]) for i in range(num_drones)]) + new_log_probs = new_log_probs.transpose() # (minibatch_size, num_drones) + + # Normalizes advantage (trick) + gae = (gae - gae.mean()) / (gae.std() + 1e-8) + gae = gae.reshape((-1, 1)) # (minibatch_size, 1) + + # CALCULATE VALUE LOSS + value_pred_clipped = traj_batch.value + (new_value - traj_batch.value).clip(-args.clip_eps, args.clip_eps) + value_losses = jnp.square(new_value - targets) + value_losses_clipped = jnp.square(value_pred_clipped - targets) + value_loss = 0.5 * jnp.maximum(value_losses, value_losses_clipped).mean() + + # CALCULATE ACTOR LOSS FOR ALL AGENTS, AGGREGATE LOSS (sum) + logratio = new_log_probs - traj_batch.log_prob + ratio = jnp.exp(logratio) + approx_kl = ((ratio - 1) - logratio).mean() + loss_actor1 = -ratio * gae + loss_actor2 = -jnp.clip(ratio, 1.0 - args.clip_eps, 1.0 + args.clip_eps) * gae + loss_per_agent = jnp.maximum(loss_actor1, loss_actor2).mean(0) # mean across minibatch + loss_actors = jnp.sum(loss_per_agent) # sum across agents + + entropies = jnp.array([p.entropy().mean() for p in pi]) + entropy = entropies.mean() # TODO check how to aggregate entropies + + total_loss = loss_actors + args.vf_coef * value_loss - args.ent_coef * entropy + return total_loss, (value_loss, loss_actors, entropy, approx_kl) + + grad_fn = jax.value_and_grad(_loss_fn, argnums=(0, 1), has_aux=True) + total_loss_and_debug, grads = grad_fn( + actor_train_state.params, critic_train_state.params, traj_batch, advantages, targets + ) + actor_train_state = actor_train_state.apply_gradients(grads=grads[0]) + critic_train_state = critic_train_state.apply_gradients(grads=grads[1]) + return (actor_train_state, critic_train_state), total_loss_and_debug + + @jax.jit + def _update_epoch(update_state, unused): + actor_train_state, critic_train_state, traj_batch, advantages, targets, key = update_state + key, subkey = jax.random.split(key) + batch_size = minibatch_size * args.num_minibatches + permutation = jax.random.permutation(subkey, batch_size) + batch = (traj_batch, advantages, targets) + # flattens the num_steps dimensions into batch_size; keeps the other dimensions untouched (num_drones, obs_dim, ...) + batch = jax.tree_util.tree_map(lambda x: x.reshape((batch_size,) + x.shape[1:]), batch) + # shuffles the full batch using permutations + shuffled_batch = jax.tree_util.tree_map(lambda x: jnp.take(x, permutation, axis=0), batch) + # Slices the shuffled batch into num_minibatches + minibatches = jax.tree_util.tree_map( + lambda x: jnp.reshape(x, [args.num_minibatches, -1] + list(x.shape[1:])), + shuffled_batch, + ) + actor_critic_state, total_loss_and_debug = jax.lax.scan( + _update_minbatch, (actor_train_state, critic_train_state), minibatches + ) + update_state = (actor_critic_state[0], actor_critic_state[1], traj_batch, advantages, targets, key) + return update_state, total_loss_and_debug + + # INIT ENV + key, subkeys = jax.random.split(key) + obs, info = env.reset(seed=args.seed) + episode_returns = [] + current_timestep = 0 + + # TRAIN LOOP + def _update_step(runner_state: Tuple[TrainState, TrainState, dict, chex.PRNGKey]): + # COLLECT TRAJECTORIES + def _env_step(runner_state): + actor_state, critic_state, obs, key = runner_state + + # SELECT ACTION + key, subkey = jax.random.split(key) + # pi contains the normal distributions for each drone (num_drones x Distribution(action_dim)) + np_obs = _to_array_obs(obs) + pi = _ma_get_pi(actor_state.params, jnp.array(np_obs)) + action_keys = jax.random.split(subkey, num_drones) + + # for each agent, sample an action + actions, log_probs = zip(*_ma_sample_and_log_prob_from_pi(pi, action_keys)) + actions_dict = dict() + for i, agent in enumerate(env.possible_agents): + actions_dict[agent] = np.array(actions[i]) + actions = np.array(actions) + log_probs = np.array(log_probs) + + # CRITIC STEP + global_obs = env.state() + value = critic.apply(critic_state.params, global_obs) + + # STEP ENV + key, subkey = jax.random.split(key) + obs, rewards, terminateds, truncateds, info = env.step(actions_dict) + nonlocal current_timestep + current_timestep += 1 + + reward = np.array(list(rewards.values())).sum(axis=-1) # team reward + terminated = np.logical_or( + np.any(np.array(list(terminateds.values())), axis=-1), np.any(np.array(list(truncateds.values())), axis=-1) + ) # TODO handle truncations + + buffer.add( + terminated=terminated, + joint_actions=actions, + obs=np_obs, + global_obs=global_obs, + value=value, + reward=reward, + log_prob=log_probs, + info=info, + ) + + if terminated: + team_return = sum(list(info["episode"]["r"].values())) + if args.debug: + print(f"Episode return: ${team_return}, length: ${info['episode']['l']}") + episode_returns.append((current_timestep, time.time() - start_time, team_return)) + obs, info = env.reset() + + runner_state = (actor_state, critic_state, obs, key) + return runner_state + + for _ in range(args.num_steps): + runner_state = _env_step(runner_state) + + # CALCULATE ADVANTAGE + actor_train_state, critic_train_state, obs, key = runner_state + global_obs = env.state() + # TODO global_obs should be based on last obs, not current obs if truncated + last_val = critic.apply(critic_train_state.params, global_obs) + traj_batch = buffer.to_transition() + + advantages, targets = _calculate_gae(traj_batch, last_val) + + # UPDATE NETWORK + update_state = (actor_train_state, critic_train_state, traj_batch, advantages, targets, key) + update_state, loss_info = jax.lax.scan(_update_epoch, update_state, None, args.update_epochs) + + # Updates the train states (don't forget) + actor_train_state = update_state[0] + critic_train_state = update_state[1] + key = update_state[-1] + buffer.flush() + + metric = traj_batch.info + + # Careful, metric has a shape of (num_steps) and loss_info has a shape of (update_epochs, num_minibatches) + # losses = ( + # loss_info[0], + # loss_info[1][0], + # loss_info[1][1], + # loss_info[1][2], + # loss_info[1][3], + # ) + # metric["total_loss"] = losses[0] + # metric["value_loss"] = losses[1] + # metric["actor_loss"] = losses[2] + # metric["entropy"] = losses[3] + # metric["approx_kl"] = losses[4] + + # if args.debug: + # + # def callback(info, loss): + # print(f"total loss: {loss[0].mean()}") + # print(f"value loss: {loss[1].mean()}") + # print(f"actor loss: {loss[2].mean()}") + # print(f"entropy: {loss[3].mean()}") + # print(f"approx kl: {loss[4].mean()}") + # + # jax.debug.callback(callback, metric, losses) + + runner_state = (actor_train_state, critic_train_state, obs, key) + return runner_state, metric + + key, subkey = jax.random.split(key) + runner_state = (actor_train_state, critic_train_state, obs, subkey) + for _ in tqdm(range(num_updates), desc="Updates"): + runner_state, metric = _update_step(runner_state) + + metric = {"returned_episode_returns": np.array(episode_returns)} + return {"runner_state": runner_state, "metrics": metric} + + +def save_actor(actor_state): + directory = epath.Path("../trained_model") + actor_dir = directory / "actor_cpu" + print("Saving actor to ", actor_dir) + ckptr = orbax.checkpoint.PyTreeCheckpointer() + ckptr.save(actor_dir, actor_state, force=True) + + +if __name__ == "__main__": + args = parse_args() + rng = jax.random.PRNGKey(args.seed) + np.random.seed(args.seed) + + start_time = time.time() + out = train(args, rng) + print(f"total time: {time.time() - start_time}") + print(f"SPS: {args.total_timesteps / (time.time() - start_time)}") + + # actor_state = out["runner_state"][0] + # save_actor(actor_state) + + # import matplotlib.pyplot as plt + # + returns = out["metrics"]["returned_episode_returns"] + save_results(returns, "MAPPO_CPU_Circle_(1env)", args.seed) + # plt.plot(returns[:, 0], returns[:, 2], label="episode return") + + # plt.plot(out["metrics"]["total_loss"].mean(-1).reshape(-1), label="total loss") + # plt.plot(out["metrics"]["actor_loss"].mean(-1).reshape(-1), label="actor loss") + # plt.plot(out["metrics"]["value_loss"].mean(-1).reshape(-1), label="value loss") + # plt.plot(out["metrics"]["entropy"].mean(-1).reshape(-1), label="entropy") + # plt.plot(out["metrics"]["approx_kl"].mean(-1).reshape(-1), label="approx kl") + # plt.xlabel("Timestep") + # plt.ylabel("Team return") + # plt.legend() + # plt.show() diff --git a/pyproject.toml b/pyproject.toml index 84b80530..e965eacc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,6 +36,7 @@ dependencies = [ "PyOpenGL ==3.1.6", "PyOpenGL-accelerate >=3.1.1", "pillow >=8.3.1", + "jax >= 0.4.20", ] dynamic = ["version"] From f1e6b93c5cf55a86a40765bbb49b99660fb69880 Mon Sep 17 00:00:00 2001 From: umutucak Date: Tue, 5 Dec 2023 10:27:54 +0100 Subject: [PATCH 02/13] learning depend available in new optional set --- pyproject.toml | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index e965eacc..d982de30 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,12 +36,22 @@ dependencies = [ "PyOpenGL ==3.1.6", "PyOpenGL-accelerate >=3.1.1", "pillow >=8.3.1", - "jax >= 0.4.20", ] dynamic = ["version"] [project.optional-dependencies] # Update dependencies in `all` if any are added or removed +learning = [ + "jax >=0.4", + "chex >=0.1", + "distrax@git+https://github.com/google-deepmind/distrax", + "flax >=0.6", + "optax >=0.1", + "orbax-checkpoint >=0.3", + "etils >=1.5", # not sure, wasn't in crazyrl/pyproject.toml + "supersuit >=3.9", + "tqdm >=4.66.1", +] all = [ # All dependencies above except accept-rom-license # NOTE: No need to manually remove the duplicates, setuptools automatically does that From e25f5fb6f69ed086cb219e5ef07ff83817b323c9 Mon Sep 17 00:00:00 2001 From: umutucak Date: Tue, 5 Dec 2023 11:05:05 +0100 Subject: [PATCH 03/13] dependency update --- pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index d982de30..41c44104 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,9 +42,9 @@ dynamic = ["version"] [project.optional-dependencies] # Update dependencies in `all` if any are added or removed learning = [ - "jax >=0.4", + "jax >=0.4.21", "chex >=0.1", - "distrax@git+https://github.com/google-deepmind/distrax", + "distrax >= 0.15", "flax >=0.6", "optax >=0.1", "orbax-checkpoint >=0.3", From 8e9dd186644e4a6cd3c1d79fdc9517220723ab3b Mon Sep 17 00:00:00 2001 From: umutucak Date: Wed, 6 Dec 2023 13:15:11 +0100 Subject: [PATCH 04/13] doc typo --- momaland/utils/parallel_wrappers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/momaland/utils/parallel_wrappers.py b/momaland/utils/parallel_wrappers.py index 0bc5ff08..d5f245a5 100644 --- a/momaland/utils/parallel_wrappers.py +++ b/momaland/utils/parallel_wrappers.py @@ -11,7 +11,7 @@ class LinearizeReward(BaseParallelWrapper): `weights` represents the weights of each objective in the reward vector space for each agent. Example: - >>> weights = {"agent_0": np.array([0.1, 0.9]), "agent_1": np.array([0.2, 0.8]} + >>> weights = {"agent_0": np.array([0.1, 0.9]), "agent_1": np.array([0.2, 0.8])} >>> env = LinearizeReward(env, weights) """ From c1e51e075359bfa981715f0fcbc53b837b025528 Mon Sep 17 00:00:00 2001 From: umutucak Date: Wed, 13 Dec 2023 17:00:33 +0300 Subject: [PATCH 05/13] new learning dependencies --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 41c44104..314b0dc8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,8 @@ learning = [ "etils >=1.5", # not sure, wasn't in crazyrl/pyproject.toml "supersuit >=3.9", "tqdm >=4.66.1", + "pandas >=2.1.0", + "matplotlib >=3.8.0", ] all = [ # All dependencies above except accept-rom-license From bad536088a64616631973a60f226196d0093c7ba Mon Sep 17 00:00:00 2001 From: umutucak Date: Wed, 13 Dec 2023 17:36:11 +0300 Subject: [PATCH 06/13] MOMAPPO on CrazyRL with SO collapse --- .../continuous_ppo/momappo_crazyrl.py | 110 +++++++++--------- momaland/learning/continuous_ppo/wrappers.py | 65 +++++++++++ 2 files changed, 121 insertions(+), 54 deletions(-) create mode 100644 momaland/learning/continuous_ppo/wrappers.py diff --git a/momaland/learning/continuous_ppo/momappo_crazyrl.py b/momaland/learning/continuous_ppo/momappo_crazyrl.py index cfc59851..35f1c7b8 100644 --- a/momaland/learning/continuous_ppo/momappo_crazyrl.py +++ b/momaland/learning/continuous_ppo/momappo_crazyrl.py @@ -1,12 +1,5 @@ -"""This is an MAPPO implementation which runs the environment on CPU (PettingZoo) and the learning on jax compiled functions. - -Should be way faster than the original MAPPO implementation based on torch. Main changes compared to full jax version: -- CPU environment (PZ) instead of GPU environment -- Relies on Supersuit wrappers -- No vectorized environment (no autoreset) -- Step function, and by extension train function are not jittable or vmappable due to env step not being jittable -The last two points are probably the main reason why this implementation is slower than the full jax version. -""" +"""This is an MOMAPPO implementation which runs the environment on CPU (MOMAland) and the learning on jax compiled functions.""" + import argparse import os import time @@ -21,7 +14,8 @@ import numpy as np import optax import orbax.checkpoint -from crazy_rl.utils.experiments_and_plots import save_results + +# from crazy_rl.utils.experiments_and_plots import save_results # TODO from distrax import MultivariateNormalDiag from etils import epath from flax.linen.initializers import constant, orthogonal @@ -31,13 +25,19 @@ from tqdm import tqdm from momaland.envs.crazyrl.catch import catch_v0 as Catch # noqa -from momaland.envs.crazyrl.escort import escort_v0 as Escort # noqa -from momaland.envs.crazyrl.surround import surround_v0 as Surround # noqa +from momaland.learning.continuous_ppo.wrappers import ( + RecordEpisodeStatistics, + save_results, +) + +# from momaland.envs.crazyrl.escort import escort_v0 as Escort # noqa +# from momaland.envs.crazyrl.surround import surround_v0 as Surround # noqa from momaland.utils.env import ParallelEnv -from momaland.utils.wrappers import NormalizeReward # waiting PR #19 +from momaland.utils.parallel_wrappers import LinearizeReward, NormalizeReward def parse_args(): + """Argument parsing for hyperparameter optimization.""" # fmt: off parser = argparse.ArgumentParser() parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"), @@ -84,6 +84,7 @@ class Actor(nn.Module): @nn.compact def __call__(self, local_obs_and_id: jnp.ndarray): + """Actor network initialization.""" if self.activation == "relu": activation = nn.relu else: @@ -105,6 +106,7 @@ class Critic(nn.Module): @nn.compact def __call__(self, global_obs: jnp.ndarray): + """Actor network initialization.""" if self.activation == "relu": activation = nn.relu else: @@ -120,6 +122,8 @@ def __call__(self, global_obs: jnp.ndarray): class Transition(NamedTuple): + """Transition info for buffer.""" + terminated: jnp.ndarray joint_actions: jnp.ndarray # shape is (num_agents, action_dim) value: jnp.ndarray @@ -134,6 +138,7 @@ class Buffer: """A numpy buffer to accumulate the samples, normally faster than jax based because mutable.""" def __init__(self, batch_size: int, joint_actions_shape, obs_shape, global_obs_shape, num_agents): + """Buffer initialization to keep track of data between episodes.""" self.batch_size = batch_size self.joint_actions = np.zeros((batch_size, *joint_actions_shape)) self.obs = np.zeros((batch_size, *obs_shape)) @@ -156,6 +161,7 @@ def add( log_prob: np.ndarray, info: dict, ): + """Appending new data to the buffer.""" self.terminated[self.idx] = terminated self.joint_actions[self.idx] = joint_actions self.obs[self.idx] = obs @@ -167,9 +173,11 @@ def add( self.idx += 1 def flush(self): + """Resetting the idx.""" self.idx = 0 def to_transition(self): + """Cast to type Transition.""" return Transition( terminated=jnp.array(self.terminated), joint_actions=jnp.array(self.joint_actions), @@ -182,7 +190,8 @@ def to_transition(self): ) -def train(args, key: chex.PRNGKey): +def train(args, weights: np.ndarray, key: chex.PRNGKey): + """Main training loop for MOMAPPO with SO collapse.""" num_updates = int(args.total_timesteps // args.num_steps) minibatch_size = int(args.num_steps // args.num_minibatches) @@ -190,37 +199,21 @@ def linear_schedule(count): frac = 1.0 - (count // (args.num_minibatches * args.update_epochs)) / num_updates return args.lr * frac - num_drones = 3 - # env = Surround( - # drone_ids=np.arange(num_drones), - # init_flying_pos=np.array( - # [ - # [0.0, 0.0, 1.0], - # [0.0, 1.0, 1.0], - # # [1.0, 0.0, 1.0], - # # [1.0, 2.0, 2.0], - # # [2.0, 0.5, 1.0], - # # [2.0, 2.5, 2.0], - # # [2.0, 1.0, 2.5], - # # [0.5, 0.5, 0.5], - # ] - # ), - # target_location=np.array([1.0, 1.0, 2.0]), - # multi_obj=False, - # size=5, - # # target_speed=0.15, - # # final_target_location=jnp.array([-2.0, -2.0, 1.0]), - # ) - - env: ParallelEnv = Catch( - drone_ids=np.arange(num_drones), - init_flying_pos=np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 1.0], [1.0, 0.0, 1.0]]), - ) - + env: ParallelEnv = Catch.parallel_env() env = clip_actions_v0(env) env = normalize_obs_v0(env, env_min=-1.0, env_max=1.0) env = agent_indicator_v0(env) - env = NormalizeReward(env, args.gamma) + for agent in env.possible_agents: + for idx in range(env.unwrapped.reward_space(agent).shape[0]): # reward space still not accessible? @ffelten + env = NormalizeReward(env, agent, idx) + weights = { + env.possible_agents[0]: weights, + env.possible_agents[1]: weights, + env.possible_agents[2]: weights, + env.possible_agents[3]: weights, + } + env = LinearizeReward(env, weights) + env = RecordEpisodeStatistics(env) # Initial reset to have correct dimensions in the observations env.reset(seed=args.seed) @@ -262,34 +255,34 @@ def linear_schedule(count): # BUFFER buffer = Buffer( batch_size=args.num_steps, - joint_actions_shape=(num_drones, single_action_space.shape[0]), - obs_shape=(num_drones, single_obs_space.shape[0]), + joint_actions_shape=(len(env.possible_agents), single_action_space.shape[0]), + obs_shape=(len(env.possible_agents), single_obs_space.shape[0]), global_obs_shape=env.state().shape, - num_agents=num_drones, + num_agents=len(env.possible_agents), ) def _to_array_obs(obs: dict): - """Converts a dict of observations to a numpy array of shape (num_agents, obs_dim)""" + """Converts a dict of observations to a numpy array of shape (num_agents, obs_dim).""" return np.stack([obs[agent] for agent in env.possible_agents]) @jax.jit def _ma_get_pi(params, obs: jnp.ndarray): """Gets the actions for all agents at once. This is done with a for loop because distrax does not like vmapping.""" - return [actor.apply(params, obs[i]) for i in range(num_drones)] + return [actor.apply(params, obs[i]) for i in range(len(env.possible_agents))] def _batched_ma_get_pi(params, obs: jnp.ndarray): """Gets the actions for all agents in all the envs at once. This is done with a for loop because distrax does not like vmapping.""" - return [actor.apply(params, obs[:, i, :]) for i in range(num_drones)] + return [actor.apply(params, obs[:, i, :]) for i in range(len(env.possible_agents))] @jax.jit def _ma_sample_and_log_prob_from_pi(pi: List[MultivariateNormalDiag], key: chex.PRNGKey): """Samples actions for all agents in all the envs at once. This is done with a for loop because distrax does not like vmapping. Args: - pi (List[MultivariateNormalDiag]): List of distrax distributions for agent actions (batched over envs) - key (chex.PRNGKey): PRNGKey to use for sampling: size should be (num_agents, 2) + pi (List[MultivariateNormalDiag]): List of distrax distributions for agent actions (batched over envs). + key (chex.PRNGKey): PRNGKey to use for sampling: size should be (num_agents, 2). """ - return [pi[i].sample_and_log_prob(seed=key[i]) for i in range(num_drones)] + return [pi[i].sample_and_log_prob(seed=key[i]) for i in range(len(env.possible_agents))] # Batch get value vmapped_get_value = vmap(critic.apply, in_axes=(None, 0)) @@ -332,7 +325,9 @@ def _loss_fn(actor_params, critic_params, traj_batch, gae, targets): ) # this is a list of distributions with batch_shape of minibatch_size and event shape of action_dim new_value = vmapped_get_value(critic_params, traj_batch.global_obs) # MA Log Prob: shape (num_drones, minibatch_size) - new_log_probs = jnp.array([pi[i].log_prob(traj_batch.joint_actions[:, i, :]) for i in range(num_drones)]) + new_log_probs = jnp.array( + [pi[i].log_prob(traj_batch.joint_actions[:, i, :]) for i in range(len(env.possible_agents))] + ) new_log_probs = new_log_probs.transpose() # (minibatch_size, num_drones) # Normalizes advantage (trick) @@ -407,7 +402,7 @@ def _env_step(runner_state): # pi contains the normal distributions for each drone (num_drones x Distribution(action_dim)) np_obs = _to_array_obs(obs) pi = _ma_get_pi(actor_state.params, jnp.array(np_obs)) - action_keys = jax.random.split(subkey, num_drones) + action_keys = jax.random.split(subkey, len(env.possible_agents)) # for each agent, sample an action actions, log_probs = zip(*_ma_sample_and_log_prob_from_pi(pi, action_keys)) @@ -510,11 +505,15 @@ def _env_step(runner_state): for _ in tqdm(range(num_updates), desc="Updates"): runner_state, metric = _update_step(runner_state) + for e in episode_returns: + print(e) + print() metric = {"returned_episode_returns": np.array(episode_returns)} return {"runner_state": runner_state, "metrics": metric} def save_actor(actor_state): + """Saves trained actor.""" directory = epath.Path("../trained_model") actor_dir = directory / "actor_cpu" print("Saving actor to ", actor_dir) @@ -528,7 +527,10 @@ def save_actor(actor_state): np.random.seed(args.seed) start_time = time.time() - out = train(args, rng) + # for i in range(1, 10): TODO outer weight loop + # weights = np.array([1 - i/10, i/10]) + # out = train(args, weights, rng) + out = train(args, [0.5, 0.5], rng) print(f"total time: {time.time() - start_time}") print(f"SPS: {args.total_timesteps / (time.time() - start_time)}") diff --git a/momaland/learning/continuous_ppo/wrappers.py b/momaland/learning/continuous_ppo/wrappers.py new file mode 100644 index 00000000..37b5ad1e --- /dev/null +++ b/momaland/learning/continuous_ppo/wrappers.py @@ -0,0 +1,65 @@ +"""Wrappers for training. + +Parallel only. + +TODO AEC. +""" + +import os +from typing import Optional + +import pandas as pd +import pettingzoo +from pettingzoo.utils.wrappers.base_parallel import BaseParallelWrapper + + +class RecordEpisodeStatistics(BaseParallelWrapper): + """This wrapper will record episode statistics and print them at the end of each episode.""" + + def __init__(self, env: pettingzoo.ParallelEnv): + """This wrapper will record episode statistics and print them at the end of each episode. + + Args: + env (env): The environment to apply the wrapper + """ + BaseParallelWrapper.__init__(self, env) + self.episode_rewards = {agent: 0 for agent in self.possible_agents} + self.episode_lengths = {agent: 0 for agent in self.possible_agents} + + def step(self, actions): + """Steps through the environment, recording episode statistics.""" + obs, rews, terminateds, truncateds, infos = super().step(actions) + for agent in self.env.possible_agents: + self.episode_rewards[agent] += rews[agent] + self.episode_lengths[agent] += 1 + if all(terminateds.values()) or all(truncateds.values()): + infos["episode"] = { + "r": self.episode_rewards, + "l": self.episode_lengths, + } + return obs, rews, terminateds, truncateds, infos + + def reset(self, seed: Optional[int] = None, options: Optional[dict] = None): + """Resets the environment, recording episode statistics.""" + obs, info = super().reset(seed, options) + for agent in self.env.possible_agents: + self.episode_rewards[agent] = 0 + self.episode_lengths[agent] = 0 + return obs, info + + +def save_results(returns, exp_name, seed): + """Saves the results of an experiment to a csv file. + + Args: + returns: a list of triples (timesteps, time, episodic_return) + exp_name: experiment name + seed: seed of the experiment + """ + if not os.path.exists("results"): + os.makedirs("results") + filename = f"results/results_{exp_name}_{seed}.csv" + print(f"Saving results to {filename}") + df = pd.DataFrame(returns) + df.columns = ["Total timesteps", "Time", "Episodic return"] + df.to_csv(filename, index=False) From aa434e805a7c0dd140a390f485b79a0762a1956a Mon Sep 17 00:00:00 2001 From: umutucak Date: Thu, 14 Dec 2023 20:19:16 +0300 Subject: [PATCH 07/13] iterating over different weights + reward linearization with weight distribution --- .../continuous_ppo/momappo_crazyrl.py | 18 +- momaland/learning/continuous_ppo/wrappers.py | 2 +- results/MOMAPPO_Catch_0.1-0.9_1.csv | 607 +++++++++++++++++ results/MOMAPPO_Catch_0.2-0.8_1.csv | 622 +++++++++++++++++ results/MOMAPPO_Catch_0.3-0.7_1.csv | 638 ++++++++++++++++++ results/MOMAPPO_Catch_0.4-0.6_1.csv | 603 +++++++++++++++++ results/MOMAPPO_Catch_0.5-0.5_1.csv | 619 +++++++++++++++++ results/MOMAPPO_Catch_0.6-0.4_1.csv | 619 +++++++++++++++++ results/MOMAPPO_Catch_0.7-0.3_1.csv | 599 ++++++++++++++++ results/MOMAPPO_Catch_0.8-0.2_1.csv | 580 ++++++++++++++++ results/MOMAPPO_Catch_0.9-0.1_1.csv | 616 +++++++++++++++++ 11 files changed, 5511 insertions(+), 12 deletions(-) create mode 100644 results/MOMAPPO_Catch_0.1-0.9_1.csv create mode 100644 results/MOMAPPO_Catch_0.2-0.8_1.csv create mode 100644 results/MOMAPPO_Catch_0.3-0.7_1.csv create mode 100644 results/MOMAPPO_Catch_0.4-0.6_1.csv create mode 100644 results/MOMAPPO_Catch_0.5-0.5_1.csv create mode 100644 results/MOMAPPO_Catch_0.6-0.4_1.csv create mode 100644 results/MOMAPPO_Catch_0.7-0.3_1.csv create mode 100644 results/MOMAPPO_Catch_0.8-0.2_1.csv create mode 100644 results/MOMAPPO_Catch_0.9-0.1_1.csv diff --git a/momaland/learning/continuous_ppo/momappo_crazyrl.py b/momaland/learning/continuous_ppo/momappo_crazyrl.py index 35f1c7b8..029ba490 100644 --- a/momaland/learning/continuous_ppo/momappo_crazyrl.py +++ b/momaland/learning/continuous_ppo/momappo_crazyrl.py @@ -212,7 +212,7 @@ def linear_schedule(count): env.possible_agents[2]: weights, env.possible_agents[3]: weights, } - env = LinearizeReward(env, weights) + env = LinearizeReward(env, weights) # linearizing the rewards given the weight distribution env = RecordEpisodeStatistics(env) # Initial reset to have correct dimensions in the observations @@ -505,9 +505,6 @@ def _env_step(runner_state): for _ in tqdm(range(num_updates), desc="Updates"): runner_state, metric = _update_step(runner_state) - for e in episode_returns: - print(e) - print() metric = {"returned_episode_returns": np.array(episode_returns)} return {"runner_state": runner_state, "metrics": metric} @@ -527,10 +524,12 @@ def save_actor(actor_state): np.random.seed(args.seed) start_time = time.time() - # for i in range(1, 10): TODO outer weight loop - # weights = np.array([1 - i/10, i/10]) - # out = train(args, weights, rng) - out = train(args, [0.5, 0.5], rng) + out = [] + for i in range(1, 10): # iterating over the different weights + weights = np.array([round(1 - i / 10, 1), round(i / 10, 1)]) + out.append(train(args, weights, rng)) + returns = out[-1]["metrics"]["returned_episode_returns"] + save_results(returns, f"MOMAPPO_Catch_{weights[0], 1}-{weights[1]}", args.seed) print(f"total time: {time.time() - start_time}") print(f"SPS: {args.total_timesteps / (time.time() - start_time)}") @@ -538,9 +537,6 @@ def save_actor(actor_state): # save_actor(actor_state) # import matplotlib.pyplot as plt - # - returns = out["metrics"]["returned_episode_returns"] - save_results(returns, "MAPPO_CPU_Circle_(1env)", args.seed) # plt.plot(returns[:, 0], returns[:, 2], label="episode return") # plt.plot(out["metrics"]["total_loss"].mean(-1).reshape(-1), label="total loss") diff --git a/momaland/learning/continuous_ppo/wrappers.py b/momaland/learning/continuous_ppo/wrappers.py index 37b5ad1e..7194f84d 100644 --- a/momaland/learning/continuous_ppo/wrappers.py +++ b/momaland/learning/continuous_ppo/wrappers.py @@ -58,7 +58,7 @@ def save_results(returns, exp_name, seed): """ if not os.path.exists("results"): os.makedirs("results") - filename = f"results/results_{exp_name}_{seed}.csv" + filename = f"results/{exp_name}_{seed}.csv" print(f"Saving results to {filename}") df = pd.DataFrame(returns) df.columns = ["Total timesteps", "Time", "Episodic return"] diff --git a/results/MOMAPPO_Catch_0.1-0.9_1.csv b/results/MOMAPPO_Catch_0.1-0.9_1.csv new file mode 100644 index 00000000..15119446 --- /dev/null +++ b/results/MOMAPPO_Catch_0.1-0.9_1.csv @@ -0,0 +1,607 @@ +Total timesteps,Time,Episodic return +96.0,2.182108163833618,-8.263704854319803 +108.0,2.1995673179626465,-11.568583349033725 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f4bc22c3ea326f0c0a6bc465b7ec4071d68ebf47 Mon Sep 17 00:00:00 2001 From: Umut Ucak Date: Fri, 15 Dec 2023 15:47:33 +0300 Subject: [PATCH 08/13] Delete results directory --- results/MOMAPPO_Catch_0.1-0.9_1.csv | 607 -------------------------- results/MOMAPPO_Catch_0.2-0.8_1.csv | 622 --------------------------- results/MOMAPPO_Catch_0.3-0.7_1.csv | 638 ---------------------------- results/MOMAPPO_Catch_0.4-0.6_1.csv | 603 -------------------------- results/MOMAPPO_Catch_0.5-0.5_1.csv | 619 --------------------------- results/MOMAPPO_Catch_0.6-0.4_1.csv | 619 --------------------------- results/MOMAPPO_Catch_0.7-0.3_1.csv | 599 -------------------------- results/MOMAPPO_Catch_0.8-0.2_1.csv | 580 ------------------------- results/MOMAPPO_Catch_0.9-0.1_1.csv | 616 --------------------------- 9 files changed, 5503 deletions(-) delete mode 100644 results/MOMAPPO_Catch_0.1-0.9_1.csv delete mode 100644 results/MOMAPPO_Catch_0.2-0.8_1.csv delete mode 100644 results/MOMAPPO_Catch_0.3-0.7_1.csv delete mode 100644 results/MOMAPPO_Catch_0.4-0.6_1.csv delete mode 100644 results/MOMAPPO_Catch_0.5-0.5_1.csv delete mode 100644 results/MOMAPPO_Catch_0.6-0.4_1.csv delete mode 100644 results/MOMAPPO_Catch_0.7-0.3_1.csv delete mode 100644 results/MOMAPPO_Catch_0.8-0.2_1.csv delete mode 100644 results/MOMAPPO_Catch_0.9-0.1_1.csv diff --git a/results/MOMAPPO_Catch_0.1-0.9_1.csv b/results/MOMAPPO_Catch_0.1-0.9_1.csv deleted file mode 100644 index 15119446..00000000 --- a/results/MOMAPPO_Catch_0.1-0.9_1.csv +++ /dev/null @@ -1,607 +0,0 @@ -Total timesteps,Time,Episodic return -96.0,2.182108163833618,-8.263704854319803 -108.0,2.1995673179626465,-11.568583349033725 -150.0,2.2599871158599854,-0.6216778673742738 -162.0,2.2777023315429688,-11.954153857857456 -252.0,2.40671706199646,0.5020741809770697 -287.0,2.457087993621826,-3.933972424233798 -303.0,2.480236053466797,-5.817966239119414 -312.0,2.4933900833129883,-2.479108788643498 -320.0,2.5051581859588623,-3.02400393455755 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-98355.0,2641.1336481571198,24.37716750931161 -98555.0,2641.417585372925,23.868242972881127 -98755.0,2641.7467782497406,23.058452035725352 -98955.0,2642.032058238983,23.676089170431847 -99068.0,2642.1921961307526,13.030072269415244 -99268.0,2642.4714601039886,24.142842354427913 -99410.0,2642.671536207199,15.778285134572801 -99610.0,2642.951708316803,24.516580699606582 -99783.0,2643.1927411556244,20.374094428771024 From 5a9e76620f634bfe782b1384c84fb3d3293deac7 Mon Sep 17 00:00:00 2001 From: umutucak Date: Fri, 15 Dec 2023 15:50:09 +0300 Subject: [PATCH 09/13] gitignore update for training data --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 0693c0bd..d85a4843 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +results + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] From a5257043858e2b6b50b15fa2e3656be051ee8970 Mon Sep 17 00:00:00 2001 From: umutucak Date: Fri, 15 Dec 2023 15:55:39 +0300 Subject: [PATCH 10/13] feedback fixes --- .../continuous_ppo/momappo_crazyrl.py | 27 +++----- momaland/learning/continuous_ppo/utils.py | 27 ++++++++ momaland/learning/continuous_ppo/wrappers.py | 65 ------------------- momaland/utils/parallel_wrappers.py | 36 ++++++++++ 4 files changed, 72 insertions(+), 83 deletions(-) create mode 100644 momaland/learning/continuous_ppo/utils.py delete mode 100644 momaland/learning/continuous_ppo/wrappers.py diff --git a/momaland/learning/continuous_ppo/momappo_crazyrl.py b/momaland/learning/continuous_ppo/momappo_crazyrl.py index 029ba490..dd45269c 100644 --- a/momaland/learning/continuous_ppo/momappo_crazyrl.py +++ b/momaland/learning/continuous_ppo/momappo_crazyrl.py @@ -1,4 +1,4 @@ -"""This is an MOMAPPO implementation which runs the environment on CPU (MOMAland) and the learning on jax compiled functions.""" +"""Implementation of MAPPO with parameter sharing on the CPU. Learning on jax compiled functions. Works for cooperative settings.""" import argparse import os @@ -14,8 +14,6 @@ import numpy as np import optax import orbax.checkpoint - -# from crazy_rl.utils.experiments_and_plots import save_results # TODO from distrax import MultivariateNormalDiag from etils import epath from flax.linen.initializers import constant, orthogonal @@ -24,17 +22,15 @@ from supersuit import agent_indicator_v0, clip_actions_v0, normalize_obs_v0 from tqdm import tqdm -from momaland.envs.crazyrl.catch import catch_v0 as Catch # noqa -from momaland.learning.continuous_ppo.wrappers import ( +from momaland.envs.crazyrl.catch import catch_v0 as Catch +from momaland.learning.continuous_ppo.utils import save_results +from momaland.utils.env import ParallelEnv +from momaland.utils.parallel_wrappers import ( + LinearizeReward, + NormalizeReward, RecordEpisodeStatistics, - save_results, ) -# from momaland.envs.crazyrl.escort import escort_v0 as Escort # noqa -# from momaland.envs.crazyrl.surround import surround_v0 as Surround # noqa -from momaland.utils.env import ParallelEnv -from momaland.utils.parallel_wrappers import LinearizeReward, NormalizeReward - def parse_args(): """Argument parsing for hyperparameter optimization.""" @@ -206,13 +202,8 @@ def linear_schedule(count): for agent in env.possible_agents: for idx in range(env.unwrapped.reward_space(agent).shape[0]): # reward space still not accessible? @ffelten env = NormalizeReward(env, agent, idx) - weights = { - env.possible_agents[0]: weights, - env.possible_agents[1]: weights, - env.possible_agents[2]: weights, - env.possible_agents[3]: weights, - } - env = LinearizeReward(env, weights) # linearizing the rewards given the weight distribution + _weights = {agent: weights for agent in env.possible_agents} + env = LinearizeReward(env, _weights) # linearizing the rewards given the weights env = RecordEpisodeStatistics(env) # Initial reset to have correct dimensions in the observations diff --git a/momaland/learning/continuous_ppo/utils.py b/momaland/learning/continuous_ppo/utils.py new file mode 100644 index 00000000..fc4fed64 --- /dev/null +++ b/momaland/learning/continuous_ppo/utils.py @@ -0,0 +1,27 @@ +"""Wrappers for training. + +Parallel only. + +TODO AEC. +""" + +import os + +import pandas as pd + + +def save_results(returns, exp_name, seed): + """Saves the results of an experiment to a csv file. + + Args: + returns: a list of triples (timesteps, time, episodic_return) + exp_name: experiment name + seed: seed of the experiment + """ + if not os.path.exists("results"): + os.makedirs("results") + filename = f"results/{exp_name}_{seed}.csv" + print(f"Saving results to {filename}") + df = pd.DataFrame(returns) + df.columns = ["Total timesteps", "Time", "Episodic return"] + df.to_csv(filename, index=False) diff --git a/momaland/learning/continuous_ppo/wrappers.py b/momaland/learning/continuous_ppo/wrappers.py deleted file mode 100644 index 7194f84d..00000000 --- a/momaland/learning/continuous_ppo/wrappers.py +++ /dev/null @@ -1,65 +0,0 @@ -"""Wrappers for training. - -Parallel only. - -TODO AEC. -""" - -import os -from typing import Optional - -import pandas as pd -import pettingzoo -from pettingzoo.utils.wrappers.base_parallel import BaseParallelWrapper - - -class RecordEpisodeStatistics(BaseParallelWrapper): - """This wrapper will record episode statistics and print them at the end of each episode.""" - - def __init__(self, env: pettingzoo.ParallelEnv): - """This wrapper will record episode statistics and print them at the end of each episode. - - Args: - env (env): The environment to apply the wrapper - """ - BaseParallelWrapper.__init__(self, env) - self.episode_rewards = {agent: 0 for agent in self.possible_agents} - self.episode_lengths = {agent: 0 for agent in self.possible_agents} - - def step(self, actions): - """Steps through the environment, recording episode statistics.""" - obs, rews, terminateds, truncateds, infos = super().step(actions) - for agent in self.env.possible_agents: - self.episode_rewards[agent] += rews[agent] - self.episode_lengths[agent] += 1 - if all(terminateds.values()) or all(truncateds.values()): - infos["episode"] = { - "r": self.episode_rewards, - "l": self.episode_lengths, - } - return obs, rews, terminateds, truncateds, infos - - def reset(self, seed: Optional[int] = None, options: Optional[dict] = None): - """Resets the environment, recording episode statistics.""" - obs, info = super().reset(seed, options) - for agent in self.env.possible_agents: - self.episode_rewards[agent] = 0 - self.episode_lengths[agent] = 0 - return obs, info - - -def save_results(returns, exp_name, seed): - """Saves the results of an experiment to a csv file. - - Args: - returns: a list of triples (timesteps, time, episodic_return) - exp_name: experiment name - seed: seed of the experiment - """ - if not os.path.exists("results"): - os.makedirs("results") - filename = f"results/{exp_name}_{seed}.csv" - print(f"Saving results to {filename}") - df = pd.DataFrame(returns) - df.columns = ["Total timesteps", "Time", "Episodic return"] - df.to_csv(filename, index=False) diff --git a/momaland/utils/parallel_wrappers.py b/momaland/utils/parallel_wrappers.py index d5f245a5..02d91b3c 100644 --- a/momaland/utils/parallel_wrappers.py +++ b/momaland/utils/parallel_wrappers.py @@ -1,10 +1,46 @@ """Various wrappers for Parallel MO environments.""" +from typing import Optional import numpy as np from gymnasium.wrappers.normalize import RunningMeanStd from pettingzoo.utils.wrappers.base_parallel import BaseParallelWrapper +class RecordEpisodeStatistics(BaseParallelWrapper): + """This wrapper will record episode statistics and print them at the end of each episode.""" + + def __init__(self, env): + """This wrapper will record episode statistics and print them at the end of each episode. + + Args: + env (env): The environment to apply the wrapper + """ + BaseParallelWrapper.__init__(self, env) + self.episode_rewards = {agent: 0 for agent in self.possible_agents} + self.episode_lengths = {agent: 0 for agent in self.possible_agents} + + def step(self, actions): + """Steps through the environment, recording episode statistics.""" + obs, rews, terminateds, truncateds, infos = super().step(actions) + for agent in self.env.possible_agents: + self.episode_rewards[agent] += rews[agent] + self.episode_lengths[agent] += 1 + if all(terminateds.values()) or all(truncateds.values()): + infos["episode"] = { + "r": self.episode_rewards, + "l": self.episode_lengths, + } + return obs, rews, terminateds, truncateds, infos + + def reset(self, seed: Optional[int] = None, options: Optional[dict] = None): + """Resets the environment, recording episode statistics.""" + obs, info = super().reset(seed, options) + for agent in self.env.possible_agents: + self.episode_rewards[agent] = 0 + self.episode_lengths[agent] = 0 + return obs, info + + class LinearizeReward(BaseParallelWrapper): """Convert MO reward vector into scalar SO reward value. From b38983f2bc71ad664789291426110c3306e0ac01 Mon Sep 17 00:00:00 2001 From: umutucak Date: Fri, 22 Dec 2023 10:16:11 +0300 Subject: [PATCH 11/13] @rradules review, configurable network arch --- .../continuous_ppo/momappo_crazyrl.py | 28 +++++++++++++------ 1 file changed, 19 insertions(+), 9 deletions(-) diff --git a/momaland/learning/continuous_ppo/momappo_crazyrl.py b/momaland/learning/continuous_ppo/momappo_crazyrl.py index dd45269c..36717365 100644 --- a/momaland/learning/continuous_ppo/momappo_crazyrl.py +++ b/momaland/learning/continuous_ppo/momappo_crazyrl.py @@ -62,6 +62,10 @@ def parse_args(): help="the coefficient for the value function loss") parser.add_argument("--max-grad-norm", type=float, default=0.5, help="the maximum norm for the gradient clipping") + parser.add_argument("--actor-net-arch", type=lambda x: list(map(int, x.split(','))), default=[256, 256], + help="actor network architecture excluding the output layer(size=action_space)") + parser.add_argument("--critic-net-arch", type=lambda x: list(map(int, x.split(','))), default=[256, 256], + help="critic network architecture excluding the output layer (size=1)") parser.add_argument("--activation", type=str, default="tanh", help="the activation function for the neural networks") parser.add_argument("--anneal-lr", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True, @@ -76,6 +80,7 @@ class Actor(nn.Module): """Actor class for the agent.""" action_dim: Sequence[int] + net_arch: np.ndarray activation: str = "tanh" @nn.compact @@ -85,19 +90,23 @@ def __call__(self, local_obs_and_id: jnp.ndarray): activation = nn.relu else: activation = nn.tanh - actor_mean = nn.Dense(256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(local_obs_and_id) - actor_mean = activation(actor_mean) - actor_mean = nn.Dense(256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(actor_mean) + + actor_mean = nn.Dense(self.net_arch[0], kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(local_obs_and_id) actor_mean = activation(actor_mean) + for i in range(1, len(self.net_arch)): + actor_mean = nn.Dense(self.net_arch[i], kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(actor_mean) + actor_mean = activation(actor_mean) actor_mean = nn.Dense(self.action_dim, kernel_init=orthogonal(0.01), bias_init=constant(0.0))(actor_mean) actor_logtstd = self.param("log_std", nn.initializers.zeros, (self.action_dim,)) pi: MultivariateNormalDiag = distrax.MultivariateNormalDiag(actor_mean, jnp.exp(actor_logtstd)) + return pi class Critic(nn.Module): """Critic class for the agent.""" + net_arch: np.ndarray activation: str = "tanh" @nn.compact @@ -108,10 +117,11 @@ def __call__(self, global_obs: jnp.ndarray): else: activation = nn.tanh - critic = nn.Dense(256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(global_obs) - critic = activation(critic) - critic = nn.Dense(256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(critic) + critic = nn.Dense(self.net_arch[0], kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(global_obs) critic = activation(critic) + for i in range(1, len(self.net_arch)): + critic = nn.Dense(self.net_arch[i], kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(critic) + critic = activation(critic) critic = nn.Dense(1, kernel_init=orthogonal(1.0), bias_init=constant(0.0))(critic) return jnp.squeeze(critic, axis=-1) @@ -200,7 +210,7 @@ def linear_schedule(count): env = normalize_obs_v0(env, env_min=-1.0, env_max=1.0) env = agent_indicator_v0(env) for agent in env.possible_agents: - for idx in range(env.unwrapped.reward_space(agent).shape[0]): # reward space still not accessible? @ffelten + for idx in range(env.unwrapped.reward_space(agent).shape[0]): # TODO env = NormalizeReward(env, agent, idx) _weights = {agent: weights for agent in env.possible_agents} env = LinearizeReward(env, _weights) # linearizing the rewards given the weights @@ -213,8 +223,8 @@ def linear_schedule(count): single_action_space = env.action_space(env.possible_agents[0]) single_obs_space = env.observation_space(env.possible_agents[0]) - actor = Actor(single_action_space.shape[0], activation=args.activation) - critic = Critic(activation=args.activation) + actor = Actor(single_action_space.shape[0], net_arch=args.actor_net_arch, activation=args.activation) + critic = Critic(net_arch=args.critic_net_arch, activation=args.activation) key, actor_key, critic_key = jax.random.split(key, 3) dummy_local_obs_and_id = jnp.zeros(single_obs_space.shape) dummy_global_obs = jnp.zeros(env.state().shape) From f1626241a2e20b6ea696cc9266b7a8ced9eba9d7 Mon Sep 17 00:00:00 2001 From: umutucak Date: Fri, 22 Dec 2023 10:20:22 +0300 Subject: [PATCH 12/13] restructuring --- .../momappo_crazyrl.py => continuous/cooperative_momappo.py} | 2 +- momaland/learning/{continuous_ppo => }/utils.py | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename momaland/learning/{continuous_ppo/momappo_crazyrl.py => continuous/cooperative_momappo.py} (99%) rename momaland/learning/{continuous_ppo => }/utils.py (100%) diff --git a/momaland/learning/continuous_ppo/momappo_crazyrl.py b/momaland/learning/continuous/cooperative_momappo.py similarity index 99% rename from momaland/learning/continuous_ppo/momappo_crazyrl.py rename to momaland/learning/continuous/cooperative_momappo.py index 36717365..291b49cf 100644 --- a/momaland/learning/continuous_ppo/momappo_crazyrl.py +++ b/momaland/learning/continuous/cooperative_momappo.py @@ -23,7 +23,7 @@ from tqdm import tqdm from momaland.envs.crazyrl.catch import catch_v0 as Catch -from momaland.learning.continuous_ppo.utils import save_results +from momaland.learning.utils import save_results from momaland.utils.env import ParallelEnv from momaland.utils.parallel_wrappers import ( LinearizeReward, diff --git a/momaland/learning/continuous_ppo/utils.py b/momaland/learning/utils.py similarity index 100% rename from momaland/learning/continuous_ppo/utils.py rename to momaland/learning/utils.py From c5fa10ff3cfeb93016481f7396d76aff5b02cd0b Mon Sep 17 00:00:00 2001 From: umutucak Date: Sun, 24 Dec 2023 15:34:39 +0300 Subject: [PATCH 13/13] feedback fixes --- .../learning/continuous/cooperative_momappo.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/momaland/learning/continuous/cooperative_momappo.py b/momaland/learning/continuous/cooperative_momappo.py index 291b49cf..6474544f 100644 --- a/momaland/learning/continuous/cooperative_momappo.py +++ b/momaland/learning/continuous/cooperative_momappo.py @@ -1,4 +1,4 @@ -"""Implementation of MAPPO with parameter sharing on the CPU. Learning on jax compiled functions. Works for cooperative settings.""" +"""Implementation of multi-objective MAPPO with parameter sharing on the CPU. Learning on jax compiled functions. Works for cooperative settings.""" import argparse import os @@ -197,7 +197,7 @@ def to_transition(self): def train(args, weights: np.ndarray, key: chex.PRNGKey): - """Main training loop for MOMAPPO with SO collapse.""" + """MAPPO scalarizing the vector reward using weights and weighted sum.""" num_updates = int(args.total_timesteps // args.num_steps) minibatch_size = int(args.num_steps // args.num_minibatches) @@ -210,7 +210,7 @@ def linear_schedule(count): env = normalize_obs_v0(env, env_min=-1.0, env_max=1.0) env = agent_indicator_v0(env) for agent in env.possible_agents: - for idx in range(env.unwrapped.reward_space(agent).shape[0]): # TODO + for idx in range(env.unwrapped.reward_space(agent).shape[0]): env = NormalizeReward(env, agent, idx) _weights = {agent: weights for agent in env.possible_agents} env = LinearizeReward(env, _weights) # linearizing the rewards given the weights @@ -318,18 +318,18 @@ def _update_minbatch(actor_critic_train_state, batch_info): traj_batch, advantages, targets = batch_info def _loss_fn(actor_params, critic_params, traj_batch, gae, targets): - # Batch values are in shape (batch_size, num_drones, ...) + # Batch values are in shape (batch_size, len(env.possible_agents), ...) # RERUN NETWORK pi = _batched_ma_get_pi( actor_params, traj_batch.obs ) # this is a list of distributions with batch_shape of minibatch_size and event shape of action_dim new_value = vmapped_get_value(critic_params, traj_batch.global_obs) - # MA Log Prob: shape (num_drones, minibatch_size) + # MA Log Prob: shape (len(env.possible_agents), minibatch_size) new_log_probs = jnp.array( [pi[i].log_prob(traj_batch.joint_actions[:, i, :]) for i in range(len(env.possible_agents))] ) - new_log_probs = new_log_probs.transpose() # (minibatch_size, num_drones) + new_log_probs = new_log_probs.transpose() # (minibatch_size, len(env.possible_agents)) # Normalizes advantage (trick) gae = (gae - gae.mean()) / (gae.std() + 1e-8) @@ -371,7 +371,7 @@ def _update_epoch(update_state, unused): batch_size = minibatch_size * args.num_minibatches permutation = jax.random.permutation(subkey, batch_size) batch = (traj_batch, advantages, targets) - # flattens the num_steps dimensions into batch_size; keeps the other dimensions untouched (num_drones, obs_dim, ...) + # flattens the num_steps dimensions into batch_size; keeps the other dimensions untouched (len(env.possible_agents), obs_dim, ...) batch = jax.tree_util.tree_map(lambda x: x.reshape((batch_size,) + x.shape[1:]), batch) # shuffles the full batch using permutations shuffled_batch = jax.tree_util.tree_map(lambda x: jnp.take(x, permutation, axis=0), batch) @@ -400,7 +400,7 @@ def _env_step(runner_state): # SELECT ACTION key, subkey = jax.random.split(key) - # pi contains the normal distributions for each drone (num_drones x Distribution(action_dim)) + # pi contains the normal distributions for each drone (len(env.possible_agents) x Distribution(action_dim)) np_obs = _to_array_obs(obs) pi = _ma_get_pi(actor_state.params, jnp.array(np_obs)) action_keys = jax.random.split(subkey, len(env.possible_agents)) @@ -531,8 +531,8 @@ def save_actor(actor_state): out.append(train(args, weights, rng)) returns = out[-1]["metrics"]["returned_episode_returns"] save_results(returns, f"MOMAPPO_Catch_{weights[0], 1}-{weights[1]}", args.seed) + print(f"SPS: {args.total_timesteps / (time.time() - start_time)}") print(f"total time: {time.time() - start_time}") - print(f"SPS: {args.total_timesteps / (time.time() - start_time)}") # actor_state = out["runner_state"][0] # save_actor(actor_state)