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SRDDPG_V5.py
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SRDDPG_V5.py
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# Useful Package
import matplotlib
matplotlib.use('TkAgg')
import tensorflow as tf
import numpy as np
import time
from cartpole_disturb import CartPoleEnv_adv as dreamer
import os
import math
# For GPU
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
##################### 备忘 ###################
#Lambda更新
#Disturb训练方式
##################### hyper parameters ####################
MAX_EPISODES = 50000
MAX_EP_STEPS =2500
LR_A = 0.0001 # learning rate for actor
LR_C = 0.0002 # learning rate for critic
LR_D = 0.0001 # learning rate for disturb
GAMMA = 0.99 # reward discount
TAU = 0.01 # soft replacement
MEMORY_CAPACITY = 10000
BATCH_SIZE = 128
labda=10.
tol = 0.001
# Function switch
RENDER = False
DISTURB = True
DREAMER = True
print("Dreamer = ",DREAMER,",DISTURB = " ,DISTURB,",RENDER = ",RENDER)
# For analyse
EWMA_p=0.95
EWMA_step=np.zeros((1,MAX_EPISODES+1))
EWMA_reward=np.zeros((1,MAX_EPISODES+1))
iteration=np.zeros((1,MAX_EPISODES+1))
# Training setting
var = 5 # control exploration
t1 = time.time()
max_reward=400000
max_ewma_reward=200000
############################### DDPG ####################################
class DDPG(object):
def __init__(self, a_dim, s_dim, a_bound,disturb_switch):
############################### Model parameters ####################################
self.memory = np.zeros((MEMORY_CAPACITY, s_dim * 2 + a_dim + 3), dtype=np.float32)
self.pointer = 0
self.sess = tf.Session()
self.a_dim, self.s_dim, self.a_bound = a_dim, s_dim, a_bound,
self.S = tf.placeholder(tf.float32, [None, s_dim], 's')
self.S_ = tf.placeholder(tf.float32, [None, s_dim], 's_')
self.R = tf.placeholder(tf.float32, [None, 1], 'r')
self.LR_A = tf.placeholder(tf.float32, None, 'LR_A')
self.LR_C = tf.placeholder(tf.float32, None, 'LR_C')
self.LR_D = tf.placeholder(tf.float32, None, 'LR_D')
self.labda = tf.placeholder(tf.float32, None, 'Lambda')
self.a = self._build_a(self.S, ) # 这个网络用于及时更新参数
self.d = self._build_d(self.S, ) # 这个网络用于及时更新参数
self.q = self._build_c(self.S, self.a, self.d) # 这个网络是用于及时更新参数
self.DISTURB=disturb_switch
a_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Actor')
c_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Critic')
d_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Disturber')
############################### Model Learning Setting ####################################
ema = tf.train.ExponentialMovingAverage(decay=1 - TAU) # soft replacement
def ema_getter(getter, name, *args, **kwargs):
return ema.average(getter(name, *args, **kwargs))
target_update = [ema.apply(a_params), ema.apply(c_params), ema.apply(d_params)] # soft update operation
beta = 0.01
a_ = self._build_a(self.S_, reuse=True, custom_getter=ema_getter) # replaced target parameters
a_cons = self._build_a(self.S_, reuse=True)
d_ = self._build_d(self.S_, reuse=True, custom_getter=ema_getter) # replaced target parameters
d_cons = self._build_d(self.S_, reuse=True)
q_ = self._build_c(self.S_, tf.stop_gradient(a_), tf.stop_gradient(d_), reuse=True, custom_getter=ema_getter)
self.q_cons = self._build_c(self.S_, a_cons, d_cons, reuse=True)
self.q_lambda =tf.reduce_mean(self.q - self.q_cons)
a_loss = - tf.reduce_mean(self.q) + self.labda * self.q_lambda # maximize the q
d_loss = tf.reduce_mean(self.q)
self.atrain = tf.train.AdamOptimizer(self.LR_A).minimize(a_loss, var_list=a_params)#以learning_rate去训练,方向是minimize loss,调整列表参数,用adam
self.dtrain = tf.train.AdamOptimizer(self.LR_D).minimize(d_loss,
var_list=d_params) # 以learning_rate去训练,方向是minimize loss,调整列表参数,用adam
with tf.control_dependencies(target_update): # soft replacement happened at here
q_target = self.R + GAMMA * q_+ beta*tf.matmul(self.d,tf.transpose(self.d))
td_error = tf.losses.mean_squared_error(labels=q_target, predictions=self.q)
self.ctrain = tf.train.AdamOptimizer(self.LR_C).minimize(td_error, var_list=c_params)
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver()
def choose_action(self, s):
return self.sess.run(self.a, {self.S: s[np.newaxis, :]})[0]
def choose_disturb(self, s):
return self.sess.run(self.d, {self.S: s[np.newaxis, :]})[0]
def learn(self,LR_A,LR_D,LR_C,labda):
indices = np.random.choice(MEMORY_CAPACITY, size=BATCH_SIZE)
bt = self.memory[indices, :]
bs = bt[:, :self.s_dim]
ba = bt[:, self.s_dim: self.s_dim + self.a_dim]
bd = bt[:, self.s_dim + self.a_dim: self.s_dim + self.a_dim+2]
br = bt[:, -self.s_dim - 1: -self.s_dim]
bs_ = bt[:, -self.s_dim:]
if self.DISTURB:
self.sess.run(self.dtrain, {self.S: bs, self.LR_D: LR_D})
self.sess.run(self.atrain, {self.S: bs, self.S_: bs_, self.LR_A: LR_A, self.labda: labda})
self.sess.run(self.ctrain,
{self.S: bs, self.a: ba, self.R: br, self.S_: bs_,self.LR_C: LR_C, self.d: bd})
return self.sess.run(self.q_lambda,{self.S: bs, self.a: ba, self.R: br, self.S_: bs_}),self.sess.run(self.R, {self.R: br})
def store_transition(self, s, a, d, r, s_):
transition = np.hstack((s, a, d,[r], s_))
index = self.pointer % MEMORY_CAPACITY # replace the old memory with new memory
self.memory[index, :] = transition
self.pointer += 1
#action 选择模块也是actor模块
def _build_a(self, s, reuse=None, custom_getter=None):
trainable = True
with tf.variable_scope('Actor', reuse=reuse, custom_getter=custom_getter):
net_0 = tf.layers.dense(s, 256, activation=tf.nn.relu, name='l1', trainable=trainable)#原始是30
net_1 = tf.layers.dense(net_0, 256, activation=tf.nn.relu, name='l2', trainable=trainable) # 原始是30
net_2 = tf.layers.dense(net_1, 256, activation=tf.nn.relu, name='l3', trainable=trainable) # 原始是30
net_3 = tf.layers.dense(net_2, 128, activation=tf.nn.relu, name='l4', trainable=trainable) # 原始是30
a = tf.layers.dense(net_3, self.a_dim, activation=tf.nn.tanh, name='a', trainable=trainable)
return tf.multiply(a, self.a_bound, name='scaled_a')
#critic模块
def _build_c(self, s, a,d,reuse=None, custom_getter=None):
trainable = True if reuse is None else False
with tf.variable_scope('Critic', reuse=reuse, custom_getter=custom_getter):
n_l1 = 512#30
w1_s = tf.get_variable('w1_s', [self.s_dim, n_l1], trainable=trainable)
w1_a = tf.get_variable('w1_a', [self.a_dim, n_l1], trainable=trainable)
w1_d = tf.get_variable('w1_d', [self.s_dim/2, n_l1], trainable=trainable)
b1 = tf.get_variable('b1', [1, n_l1], trainable=trainable)
net_0 = tf.nn.relu(tf.matmul(s, w1_s) + tf.matmul(a, w1_a)+tf.matmul(d, w1_d)+b1)
net_1 = tf.layers.dense(net_0, 512, activation=tf.nn.relu, name='l2', trainable=trainable)
net_2 = tf.layers.dense(net_1, 256, activation=tf.nn.relu, name='l3', trainable=trainable)
net_3 = tf.layers.dense(net_2, 128, activation=tf.nn.relu, name='l4', trainable=trainable)
return tf.layers.dense(net_3, 1, trainable=trainable) # Q(s,a)
def _build_d(self, s, reuse=None, custom_getter=None):
theta_threshold_radians = 20 * 2 * math.pi / 360
x_threshold = 5
trainable = True
with tf.variable_scope('Disturber', reuse=reuse, custom_getter=custom_getter):
net_0 = tf.layers.dense(s, 512, activation=tf.nn.relu, name='l1', trainable=trainable)
net_1 = tf.layers.dense(net_0, 512, activation=tf.nn.relu, name='l2', trainable=trainable)
net_2 = tf.layers.dense(net_1, 512, activation=tf.nn.relu, name='l3', trainable=trainable)
net_3 = tf.layers.dense(net_2, 256, activation=tf.nn.relu, name='l4', trainable=trainable)
d = tf.layers.dense(net_3, self.s_dim/2, activation=tf.nn.tanh, name='d', trainable=trainable)
return tf.multiply(d, [x_threshold/500,theta_threshold_radians/500], name='scaled_d')
def save_result(self):
save_path = self.saver.save(self.sess, "Model/SRDDPG_In_Dream_WITHOUT_D.ckpt")
print("Save to path: ", save_path)
############################### DREAMER ####################################
class Dreamer(object):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': 50
}
def __init__(self, a_dim, s_dim, a_bound,):
tf.reset_default_graph()
# Model parameter
self.memory = np.zeros((MEMORY_CAPACITY, s_dim * 3 + a_dim), dtype=np.float32)
self.pointer = 0
self.sess = tf.Session()
self.a_dim, self.s_dim, self.a_bound = a_dim, s_dim, a_bound,
self.S = tf.placeholder(tf.float32, [None, s_dim], 's')
self.S_ = tf.placeholder(tf.float32, [None, s_dim], 's_')
self.S_L = tf.placeholder(tf.float32, [None, s_dim], 's_l')
self.LR_D= tf.placeholder(tf.float32, None, 'LR_D')
self.A = tf.placeholder(tf.float32, [None, a_dim], 'a')
# Dynamics Parameter
self.gravity = 10
self.masscart = 1
self.masspole = 0.1
self.total_mass = (self.masspole + self.masscart)
self.length = 0.5 # actually half the pole's length
self.polemass_length = (self.masspole * self.length)
self.force_mag = 20
self.tau = 0.02 # seconds between state updates
#Render Part
self.viewer = None
self.state = None
self.x_threshold=5
#Learning Part
self.dreamer = self._build_dreamer(self.S, self.A,self.S_L) #S_=linear_model+DNN=S_L+DNN(S,A)
d_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Dreamer')
self.dreamer_loss_s = tf.reduce_mean(tf.squared_difference(self.S_ , self.dreamer))
self.dreamertrain_s = tf.train.AdamOptimizer(self.LR_D).minimize(self.dreamer_loss_s,var_list = d_params)
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver()
self.saver.restore(self.sess, "Model/SRDDPG_Dreamer_V1.ckpt") # 1 0.1 0.5 0.001
def dream(self, s,a,d):
# self.gravity = np.random.normal(10, 0.1)
# self.masscart = np.random.normal(1, 0.1)
# self.masspole = np.random.normal(0.1, 0.01)
x, x_dot, theta, theta_dot = s
force = a[0]
costheta = 1
sintheta = theta
temp = (force + self.polemass_length * theta_dot * theta_dot * sintheta) / self.total_mass
thetaacc = (self.gravity * sintheta - costheta * temp) / (
self.length * (4.0 / 3.0 - self.masspole * costheta * costheta / self.total_mass))
xacc = temp - self.polemass_length * thetaacc * costheta / self.total_mass
x_ = x + self.tau * x_dot+d[0]
x_dot_ = x_dot + self.tau * xacc
theta_ = theta + self.tau * theta_dot+d[1]
theta_dot_ = theta_dot + self.tau * thetaacc
s_linear = np.array([x_, x_dot_, theta_, theta_dot_])
s_=self.sess.run(self.dreamer, {self.S: s[np.newaxis, :],self.A: a[np.newaxis, :],self.S_L: s_linear[np.newaxis, :]})[0]
x, _, theta, _ = s_
r_1 = ((1 - abs(x)))
r_2 = (((20 * 2 * math.pi / 360) / 4) - abs(theta)) / ((20 * 2 * math.pi / 360) / 4)
reward = np.sign(r_2) * ((10 * r_2) ** 2) + np.sign(r_1) * ((10 * r_1) ** 2)
self.state = s_
return s_,reward
def learn(self,LR_D):
indices = np.random.choice(MEMORY_CAPACITY, size=BATCH_SIZE)
bt = self.memory[indices, :]
bs = bt[:, :self.s_dim]
ba = bt[:, self.s_dim: self.s_dim + self.a_dim]
bs_ = bt[:, -self.s_dim:]
bs_l=bt[:, -self.s_dim - 4: -self.s_dim]
self.sess.run(self.dreamertrain_s, {self.S: bs,self.A: ba, self.S_: bs_,self.S_L: bs_l, self.LR_D: LR_D})
return self.sess.run(self.dreamer_loss_s, {self.S: bs,self.A: ba, self.S_: bs_,self.S_L: bs_l, self.LR_D: LR_D})
def store_transition(self, s, a,s_):
x, x_dot, theta, theta_dot = s
force = a[0]
costheta = 1
sintheta = theta
temp = (force + self.polemass_length * theta_dot * theta_dot * sintheta) / self.total_mass
thetaacc = (self.gravity * sintheta - costheta * temp) / (
self.length * (4.0 / 3.0 - self.masspole * costheta * costheta / self.total_mass))
xacc = temp - self.polemass_length * thetaacc * costheta / self.total_mass
x_ = x + self.tau * x_dot
x_dot_ = x_dot + self.tau * xacc
theta_ = theta + self.tau * theta_dot
theta_dot_ = theta_dot + self.tau * thetaacc
s_linear = np.array([x_, x_dot_, theta_, theta_dot_])
transition = np.hstack((s, a,s_linear,s_))
index = self.pointer % MEMORY_CAPACITY # replace the old memory with new memory
self.memory[index, :] = transition
self.pointer += 1
def _build_dreamer(self, s, a,s_linear,reuse=None, custom_getter=None):
trainable = True if reuse is None else False
with tf.variable_scope('Dreamer', reuse=reuse, custom_getter=custom_getter):
n_l1 = 512#30
w1_s = tf.get_variable('w1_s', [self.s_dim, n_l1], trainable=trainable)
w1_a = tf.get_variable('w1_a', [self.a_dim, n_l1], trainable=trainable)
b1 = tf.get_variable('b1', [1, n_l1], trainable=trainable)
net_0 = tf.nn.relu(tf.matmul(s, w1_s) + tf.matmul(a, w1_a)+ b1)
net_1 = tf.layers.dense(net_0, 512, activation=tf.nn.relu, name='l2', trainable=trainable)
net_2 = tf.layers.dense(net_1, 256, activation=tf.nn.relu, name='l3', trainable=trainable)
return tf.layers.dense(net_2, self.s_dim, trainable=trainable)+s_linear
def save_result(self):
save_path = self.saver.save(self.sess, "Model/SRDDPG_Dreamer_V2.ckpt")
print("Save to path: ", save_path)
def render(self, mode='human'):
screen_width = 800
screen_height = 400
world_width = self.x_threshold * 2
scale = screen_width / world_width
carty = 100 # TOP OF CART
polewidth = 10.0
polelen = scale * 1.0
cartwidth = 50.0
cartheight = 30.0
if self.viewer is None:
from gym.envs.classic_control import rendering
self.viewer = rendering.Viewer(screen_width, screen_height)
l, r, t, b = -cartwidth / 2, cartwidth / 2, cartheight / 2, -cartheight / 2
axleoffset = cartheight / 4.0
cart = rendering.FilledPolygon([(l, b), (l, t), (r, t), (r, b)])
self.carttrans = rendering.Transform()
cart.add_attr(self.carttrans)
self.viewer.add_geom(cart)
l, r, t, b = -polewidth / 2, polewidth / 2, polelen - polewidth / 2, -polewidth / 2
pole = rendering.FilledPolygon([(l, b), (l, t), (r, t), (r, b)])
pole.set_color(.8, .6, .4)
self.poletrans = rendering.Transform(translation=(0, axleoffset))
pole.add_attr(self.poletrans)
pole.add_attr(self.carttrans)
self.viewer.add_geom(pole)
self.axle = rendering.make_circle(polewidth / 2)
self.axle.add_attr(self.poletrans)
self.axle.add_attr(self.carttrans)
self.axle.set_color(.5, .5, .8)
self.viewer.add_geom(self.axle)
self.track = rendering.Line((0, carty), (screen_width, carty))
self.track.set_color(0, 0, 0)
self.viewer.add_geom(self.track)
if self.state is None: return None
x = self.state
cartx = x[0] * scale + screen_width / 2.0 # MIDDLE OF CART
self.carttrans.set_translation(cartx, carty)
self.poletrans.set_rotation(-x[2])
return self.viewer.render(return_rgb_array=mode == 'rgb_array')
def close(self):
if self.viewer:
self.viewer.close()
self.viewer = None
############################### INITIALIZE ####################################
env = dreamer()
env = env.unwrapped
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.shape[0]
a_bound = env.action_space.high
env_dream=Dreamer(a_dim, s_dim, a_bound)
ddpg = DDPG(a_dim, s_dim, a_bound,DISTURB)
############################### TRAINING ####################################
# env.seed(1) # 普通的 Policy gradient 方法, 使得回合的 variance 比较大, 所以我们选了一个好点的随机种子
for i in range(MAX_EPISODES):
iteration[0,i+1]=i+1
s = env.reset()
REWARD = 0
for j in range(MAX_EP_STEPS):
#Visulization
if RENDER:
if DREAMER:
env_dream.render()
else:
env.render()
# Choose action
# Add exploration noise
a = ddpg.choose_action(s)
a = np.clip(np.random.normal(a, var), -a_bound, a_bound) # add randomness to action selection for exploration
if DISTURB:
# Choose disturb
# Add exploration noise
d = ddpg.choose_disturb(s)
d = np.random.normal(d, abs(d * 0.02 * var)) # add randomness to disturb selection for exploration
else:
d=[0,0]
# RUN IN REAL IN TO GET INFORMATION OF DIE OR NOT
# IF Dreamer_update=True, GET INFORMATION OF THE S,A,R1,S_
# 得到的是真实的 s,a->s_ 和 r
# 主要是判断是否游戏结束
s_, r, done, hit = env.step(a,d) # S_=ENV(S,A), R=REWARD(S_)
# RUN IN DREAM
# 得到的是梦境中的s,a->s_dream 和 r_dream
# 如果在梦境,那么s_next 和 reward 就被梦境值覆盖
s_next=s_
reward=r
if DREAMER:
s_next, reward = env_dream.dream(s,a,d)
#储存s,a和s_next,reward用于DDPG的学习
ddpg.store_transition(s, a, d,(reward / 10), s_next)
#DDPG LEARN
if ddpg.pointer > MEMORY_CAPACITY:
# Decay the action randomness
var *= .99999
l_q,l_r=ddpg.learn(LR_A,LR_C,LR_D,labda)
if l_q>tol:
if labda==0:
labda = 1e-8
labda = min(labda*2,11)
if labda==11:
labda = 1e-8
if l_q<-tol:
labda = labda/2
# 梦境状态更新
s = s_next
# 现实状态与梦境同步,用于进行下一次的现实STEP
env.state = s_next
# 计算总得分
REWARD += reward
# OUTPUT TRAINING INFORMATION AND LEARNING RATE DECAY
if j == MAX_EP_STEPS - 1:
EWMA_step[0,i+1]=EWMA_p*EWMA_step[0,i]+(1-EWMA_p)*j
EWMA_reward[0,i+1]=EWMA_p*EWMA_reward[0,i]+(1-EWMA_p)*REWARD
print('Episode:', i, ' Reward: %i' % int(REWARD),'Explore: %.2f' % var,"good","EWMA_step = ",EWMA_step[0,i+1],"EWMA_reward = ",EWMA_reward[0,i+1],"LR_A = ",LR_A,'lambda',labda,'LR_D :',LR_D)
if EWMA_reward[0,i+1]>max_ewma_reward:
max_ewma_reward=min(EWMA_reward[0,i+1]+1000,500000)
LR_A *= .8 # learning rate for actor
LR_D *= .8 # learning rate for disturb
LR_C *= .8 # learning rate for critic
ddpg.save_result()
if REWARD> max_reward:
max_reward = min(REWARD+5000,500000)
LR_A *= .8 # learning rate for actor
LR_D *= .8 # learning rate for disturb
LR_C *= .8 # learning rate for critic
ddpg.save_result()
print("max_reward : ",REWARD)
else:
LR_A *= .99
LR_D *= .99
LR_C *= .99
break
elif done:
EWMA_step[0,i+1]=EWMA_p*EWMA_step[0,i]+(1-EWMA_p)*j
EWMA_reward[0,i+1]=EWMA_p*EWMA_reward[0,i]+(1-EWMA_p)*REWARD
if hit==1:
print('Episode:', i, ' Reward: %i' % int(REWARD), 'Explore: %.2f' % var, "break in : ", j, "due to ",
"hit the wall", "EWMA_step = ", EWMA_step[0, i + 1], "EWMA_reward = ", EWMA_reward[0, i + 1],"LR_A = ",LR_A,'lambda',labda,'LR_D :',LR_D)
else:
print('Episode:', i, ' Reward: %i' % int(REWARD),'Explore: %.2f' % var, "break in : ", j, "due to",
"fall down","EWMA_step = ", EWMA_step[0, i + 1], "EWMA_reward = ", EWMA_reward[0, i + 1],"LR_A = ",LR_A,'lambda',labda,'LR_D :',LR_D)
break