1.Actor-Critic既学习价值函数,也学习策略函数

2.价值函数用来评估当前的状态是好的,还是不好的,进而帮助Actor进行策略更新

actor_loss = torch.mean(-log_probs * td_delta.detach())  # 即由td_delta来调控损失

3.Critic的学习价值,由Q_value相同的求解方式求出,即Critic(state) = reward + Critic(next_state) * gamma 

td_target = rewards + self.gamma * self.critic(next_states) * (1 - dones)

td_delta = td_target - self.critic(states)  # 时序差分

train.py

import gym
import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import rl_utils
from model import ActorCritic



actor_lr = 1e-3
critic_lr = 1e-2
num_episodes = 1000
hidden_dim = 128
gamma = 0.98
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

env_name = "CartPole-v0"
env = gym.make(env_name)
env.seed(0)
torch.manual_seed(0)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
agent = ActorCritic(state_dim, hidden_dim, action_dim, actor_lr, critic_lr,
                    gamma, device)

return_list = rl_utils.train_on_policy_agent(env, agent, num_episodes)


episodes_list = list(range(len(return_list)))
plt.plot(episodes_list, return_list)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title('Actor-Critic on {}'.format(env_name))
plt.show()

mv_return = rl_utils.moving_average(return_list, 9)
plt.plot(episodes_list, mv_return)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title('Actor-Critic on {}'.format(env_name))
plt.show()

model.py 

import torch
import torch.nn.functional as F


class PolicyNet(torch.nn.Module):
    def __init__(self, state_dim, hidden_dim, action_dim):
        super(PolicyNet, self).__init__()
        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim, action_dim)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        return F.softmax(self.fc2(x), dim=1)


class ValueNet(torch.nn.Module):
    def __init__(self, state_dim, hidden_dim):
        super(ValueNet, self).__init__()
        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim, 1)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        return self.fc2(x)



class ActorCritic:
    def __init__(self, state_dim, hidden_dim, action_dim, actor_lr, critic_lr,
                 gamma, device):
        self.actor = PolicyNet(state_dim, hidden_dim, action_dim).to(device)
        self.critic = ValueNet(state_dim, hidden_dim).to(device)
        # 策略网络优化器
        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=actor_lr)
        self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=critic_lr)

        self.gamma = gamma
        self.device = device

    def take_action(self, state):
        state = torch.tensor([state], dtype=torch.float).to(self.device)
        probs = self.actor(state)
        action_dist = torch.distributions.Categorical(probs)
        action = action_dist.sample()
        return action.item()


    def update(self, transition_dict):
        states = torch.tensor(transition_dict['states'],
                              dtype=torch.float).to(self.device)
        actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(self.device)
        rewards = torch.tensor(transition_dict['rewards'],
                               dtype=torch.float).view(-1, 1).to(self.device)
        next_states = torch.tensor(transition_dict['next_states'],
                                   dtype=torch.float).to(self.device)
        dones = torch.tensor(transition_dict['dones'],
                             dtype=torch.float).view(-1, 1).to(self.device)

        # 时序差分目标
        td_target = rewards + self.gamma * self.critic(next_states) * (1 - dones)

        td_delta = td_target - self.critic(states)  # 时序差分误差
        log_probs = torch.log(self.actor(states).gather(1, actions))
        actor_loss = torch.mean(-log_probs * td_delta.detach())  # 即由td_delta来调控损失
        # 均方误差损失函数
        critic_loss = torch.mean(
            F.mse_loss(self.critic(states), td_target)
        )
        self.actor_optimizer.zero_grad()
        self.critic_optimizer.zero_grad()
        actor_loss.backward()
        critic_loss.backward()
        self.actor_optimizer.step()
        self.critic_optimizer.step()

rl_utils.py

from tqdm import tqdm
import numpy as np
import torch
import collections
import random


class ReplayBuffer:
    def __init__(self, capacity):
        self.buffer = collections.deque(maxlen=capacity)

    def add(self, state, action, reward, next_state, done):
        self.buffer.append((state, action, reward, next_state, done))

    def sample(self, batch_size):
        transitions = random.sample(self.buffer, batch_size)
        state, action, reward, next_state, done = zip(*transitions)
        return np.array(state), action, reward, np.array(next_state), done

    def size(self):
        return len(self.buffer)


def moving_average(a, window_size):
    cumulative_sum = np.cumsum(np.insert(a, 0, 0))
    middle = (cumulative_sum[window_size:] - cumulative_sum[:-window_size]) / window_size
    r = np.arange(1, window_size - 1, 2)
    begin = np.cumsum(a[:window_size - 1])[::2] / r
    end = (np.cumsum(a[:-window_size:-1])[::2] / r)[::-1]
    return np.concatenate((begin, middle, end))


def train_on_policy_agent(env, agent, num_episodes):
    return_list = []
    for i in range(10):
        with tqdm(total=int(num_episodes / 10), desc='Iteration %d' % i) as pbar:
            for i_episode in range(int(num_episodes / 10)):
                episode_return = 0
                transition_dict = {'states': [], 'actions': [], 'next_states': [], 'rewards': [], 'dones': []}
                state = env.reset()
                done = False
                # 一局放入到一块数据里面
                while not done:
                    action = agent.take_action(state)
                    next_state, reward, done, _ = env.step(action)
                    transition_dict['states'].append(state)
                    transition_dict['actions'].append(action)
                    transition_dict['next_states'].append(next_state)
                    transition_dict['rewards'].append(reward)
                    transition_dict['dones'].append(done)
                    state = next_state
                    episode_return += reward
                return_list.append(episode_return)
                agent.update(transition_dict)
                if (i_episode + 1) % 10 == 0:
                    pbar.set_postfix({'episode': '%d' % (num_episodes / 10 * i + i_episode + 1),
                                      'return': '%.3f' % np.mean(return_list[-10:])})
                pbar.update(1)
    return return_list


def train_off_policy_agent(env, agent, num_episodes, replay_buffer, minimal_size, batch_size):
    return_list = []
    for i in range(10):
        with tqdm(total=int(num_episodes / 10), desc='Iteration %d' % i) as pbar:
            for i_episode in range(int(num_episodes / 10)):
                episode_return = 0
                state = env.reset()
                done = False
                while not done:
                    action = agent.take_action(state)
                    next_state, reward, done, _ = env.step(action)
                    replay_buffer.add(state, action, reward, next_state, done)
                    state = next_state
                    episode_return += reward
                    if replay_buffer.size() > minimal_size:
                        b_s, b_a, b_r, b_ns, b_d = replay_buffer.sample(batch_size)
                        transition_dict = {'states': b_s, 'actions': b_a, 'next_states': b_ns, 'rewards': b_r,
                                           'dones': b_d}
                        agent.update(transition_dict)
                return_list.append(episode_return)
                if (i_episode + 1) % 10 == 0:
                    pbar.set_postfix({'episode': '%d' % (num_episodes / 10 * i + i_episode + 1),
                                      'return': '%.3f' % np.mean(return_list[-10:])})
                pbar.update(1)
    return return_list


def compute_advantage(gamma, lmbda, td_delta):
    td_delta = td_delta.detach().numpy()
    advantage_list = []
    advantage = 0.0
    for delta in td_delta[::-1]:
        advantage = gamma * lmbda * advantage + delta
        advantage_list.append(advantage)
    advantage_list.reverse()
    return torch.tensor(advantage_list, dtype=torch.float)

 

posted on 2022-09-12 09:49  python我的最爱  阅读(498)  评论(0编辑  收藏  举报