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 OUTdef 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 actionsclass 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_valueclass 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 += 1def 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 =[期货](https://www.gendan5.com/futures.html) self.actor_target(batch_s_) q_tmp = self.critic_target(batch_s_, a_target) q_target = 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 = 0for 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 # breakprint('Running time: ', time.time() - t1)