pytorch 实现DDPG多好的代码

Posted 2022-11-23 gz153016

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 = 32
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 = 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)