分类： Python/Ruby
2022-01-13 17:25:06
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import gym
import time
##################### hyper parameters ####################
EPISODES = 200
EP_STEPS = 200
LR_ACTOR = 0.001
LR_CRITIC = 0.002
GAMMA = 0.9
TAU = 0.01
MEMORY_CAPACITY = 10000
BATCH_SIZE = 64
RENDER = False
ENV_NAME = 'Pendulum-v0'
########################## DDPG Framework ######################
class ActorNet(nn.Module): # define the network structure for actor and critic
def __init__(self, s_dim, a_dim):
super(ActorNet, self).__init__()
self.fc1 = nn.Linear(s_dim, 30)
self.fc1.weight.data.normal_(0, 0.1) # initialization of FC1
self.out = nn.Linear(30, a_dim)
self.out.weight.data.normal_(0, 0.1) # initilizaiton of OUT
def forward(self, x):
x = self.fc1(x)
x = F.relu(x)
x = self.out(x)
x = torch.tanh(x)
actions = x * 2 # for the game "Pendulum-v0", action range is [-2, 2]
return actions
class CriticNet(nn.Module):
def __init__(self, s_dim, a_dim):
super(CriticNet, self).__init__()
self.fcs = nn.Linear(s_dim, 30)
self.fcs.weight.data.normal_(0, 0.1)
self.fca = nn.Linear(a_dim, 30)
self.fca.weight.data.normal_(0, 0.1)
self.out = nn.Linear(30, 1)
self.out.weight.data.normal_(0, 0.1)
def forward(self, s, a):
x = self.fcs(s)
y = self.fca(a)
actions_value = self.out(F.relu(x + y))
return actions_value
class DDPG(object):
def __init__(self, a_dim, s_dim, a_bound):
self.a_dim, self.s_dim, self.a_bound = a_dim, s_dim, a_bound
self.memory = np.zeros((MEMORY_CAPACITY, s_dim * 2 + a_dim + 1), dtype=np.float32)
self.pointer = 0 # serves as updating the memory data
# Create the 4 network objects
self.actor_eval = ActorNet(s_dim, a_dim)
self.actor_target = ActorNet(s_dim, a_dim)
self.critic_eval = CriticNet(s_dim, a_dim)
self.critic_target = CriticNet(s_dim, a_dim)
# create 2 optimizers for actor and critic
self.actor_optimizer = torch.optim.Adam(self.actor_eval.parameters(), lr=LR_ACTOR)
self.critic_optimizer = torch.optim.Adam(self.critic_eval.parameters(), lr=LR_CRITIC)
# Define the loss function for critic network update
self.loss_func = nn.MSELoss()
def store_transition(self, s, a, r, s_): # how to store the episodic data to buffer
transition = np.hstack((s, a, [r], s_))
index = self.pointer % MEMORY_CAPACITY # replace the old data with new data
self.memory[index, :] = transition
self.pointer += 1
def choose_action(self, s):
# print(s)
s = torch.unsqueeze(torch.FloatTensor(s), 0)
return self.actor_eval(s)[0].detach()
def learn(self):
# softly update the target networks
for x in self.actor_target.state_dict().keys():
eval('self.actor_target.' + x + '.data.mul_((1-TAU))')
eval('self.actor_target.' + x + '.data.add_(TAU*self.actor_eval.' + x + '.data)')
for x in self.critic_target.state_dict().keys():
eval('self.critic_target.' + x + '.data.mul_((1-TAU))')
eval('self.critic_target.' + x + '.data.add_(TAU*self.critic_eval.' + x + '.data)')
# sample from buffer a mini-batch data
indices = np.random.choice(MEMORY_CAPACITY, size=BATCH_SIZE)
batch_trans = self.memory[indices, :]
# extract data from mini-batch of transitions including s, a, r, s_
batch_s = torch.FloatTensor(batch_trans[:, :self.s_dim])
batch_a = torch.FloatTensor(batch_trans[:, self.s_dim:self.s_dim + self.a_dim])
batch_r = torch.FloatTensor(batch_trans[:, -self.s_dim - 1: -self.s_dim])
batch_s_ = torch.FloatTensor(batch_trans[:, -self.s_dim:])
# make action and evaluate its action values
a = self.actor_eval(batch_s)
q = self.critic_eval(batch_s, a)
actor_loss = -torch.mean(q)
# optimize the loss of actor network
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# compute the target Q value using the information of next state
a_target = self.actor_target(batch_s_)
q_tmp = self.critic_target(batch_s_, a_target)
q_target =外汇跟单gendan5.com batch_r + GAMMA * q_tmp
# compute the current q value and the loss
q_eval = self.critic_eval(batch_s, batch_a)
td_error = self.loss_func(q_target, q_eval)
# optimize the loss of critic network
self.critic_optimizer.zero_grad()
td_error.backward()
self.critic_optimizer.step()
############################### Training ######################################
# Define the env in gym
env = gym.make(ENV_NAME)
env = env.unwrapped
env.seed(1)
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.shape[0]
a_bound = env.action_space.high
a_low_bound = env.action_space.low
ddpg = DDPG(a_dim, s_dim, a_bound)
var = 3 # the controller of exploration which will decay during training process
t1 = time.time()
for i in range(EPISODES):
s = env.reset()
ep_r = 0
for j in range(EP_STEPS):
if RENDER: env.render()
# add explorative noise to action
a = ddpg.choose_action(s)
a = np.clip(np.random.normal(a, var), a_low_bound, a_bound)
s_, r, done, info = env.step(a)
ddpg.store_transition(s, a, r / 10, s_) # store the transition to memory
if ddpg.pointer > MEMORY_CAPACITY:
var *= 0.9995 # decay the exploration controller factor
ddpg.learn()
s = s_
ep_r += r
if j == EP_STEPS - 1:
print('Episode: ', i, ' Reward: %i' % (ep_r), 'Explore: %.2f' % var)
if ep_r > -300: RENDER = True
# break
print('Running time: ', time.time() - t1)
