【ROS】ros-noetic和anaconda联合使用【实操】

【ROS】ros-noetic和anaconda联合使用【实操】

在介绍完基本的联合使用方式后（参考 这篇博客），笔者希望使用ros能完成 gym环境中强化学习算法的训练。

1. requirement

需要手动使用pip安装下列包

torch
gym
tqdm
matplotlib

例如pip install gym。

2. 新建ros包中的python脚本

roscd test_ros_python
cd scripts
touch test_python.py
chom +x ./test_python.py

并且记住在CMakeLists.txt文件中完成配置，这里就不再赘述。

3. SAC算法

在完成环境的配置之后，使用Hands on RL 之 Off-policy Maximum Entropy Actor-Critic (SAC)中的代码，放置在test_python.py脚本文件中，如下

#! /usr/bin/env python
# coding :utf-8

print('\n*****************************************\n\t[test libraries]:\n')
import rospy
import sys
sys.path.append('/home/sjh/anaconda3/envs/metaRL/lib/python3.8/site-packages')
print(' - rospy.__file__ = %s'%rospy.__file__)

import torch
import torch.nn as nn
import torch.nn.functional as F![请添加图片描述](https://img-blog.csdnimg.cn/c9dcec0b049c480eba94001a2ced6170.png)

from torch.distributions import Normal

from tqdm import tqdm
import collections
import random
import numpy as np
import matplotlib.pyplot as plt
import gym
print(' - torch.__file__ = %s'%torch.__file__)
print(' - gym.__file__ = %s'%gym.__file__)

print('\n*****************************************\n\t[finish test]\n')

# replay buffer
class ReplayBuffer():
    def __init__(self, capacity):
        self.buffer = collections.deque(maxlen=capacity)
    
    def add(self, s, r, a, s_, d):
        self.buffer.append((s,r,a,s_,d))
    
    def sample(self, batch_size):
        transitions = random.sample(self.buffer, batch_size)
        states, rewards, actions, next_states, dones = zip(*transitions)
        return np.array(states), rewards, actions, np.array(next_states), dones
    
    def size(self):
        return len(self.buffer)

# Actor
class PolicyNet_Continuous(nn.Module):
    """动作空间符合高斯分布,输出动作空间的均值mu,和标准差std"""
    def __init__(self, state_dim, hidden_dim, action_dim, action_bound):
        super(PolicyNet_Continuous, self).__init__()
        self.fc1 = nn.Sequential(
            nn.Linear(in_features=state_dim, out_features=hidden_dim),
            nn.ReLU()
        )
        self.fc_mu = nn.Linear(in_features=hidden_dim, out_features=action_dim)
        self.fc_std = nn.Sequential(
            nn.Linear(in_features=hidden_dim, out_features=action_dim),
            nn.Softplus()
        )
        self.action_bound = action_bound

    def forward(self, s):
        x = self.fc1(s)
        mu = self.fc_mu(x)
        std = self.fc_std(x)
        distribution = Normal(mu, std)
        normal_sample = distribution.rsample()
        normal_log_prob = distribution.log_prob(normal_sample)
        # get action limit to [-1,1]
        action = torch.tanh(normal_sample)
        # get tanh_normal log probability
        tanh_log_prob = normal_log_prob - torch.log(1 - torch.tanh(action).pow(2) + 1e-7)
        # get action bounded
        action = action * self.action_bound
        return action, tanh_log_prob


# Critic
class QValueNet_Continuous(nn.Module):
    def __init__(self, state_dim, hidden_dim, action_dim):
        super(QValueNet_Continuous, self).__init__()
        self.fc1 = nn.Sequential(
            nn.Linear(in_features=state_dim + action_dim, out_features=hidden_dim),
            nn.ReLU()
        )
        self.fc2 = nn.Sequential(
            nn.Linear(in_features=hidden_dim, out_features=hidden_dim),
            nn.ReLU()
        )
        self.fc_out = nn.Linear(in_features=hidden_dim, out_features=1)
    
    def forward(self, s, a):
        cat = torch.cat([s,a], dim=1)
        x = self.fc1(cat)
        x = self.fc2(x)
        return self.fc_out(x)

# maximize entropy deep reinforcement learning SAC
class SAC_Continuous():
    def __init__(self, state_dim, hidden_dim, action_dim, action_bound,
                    actor_lr, critic_lr, alpha_lr, target_entropy, tau, gamma,
                    device):
        # actor
        self.actor = PolicyNet_Continuous(state_dim, hidden_dim, action_dim, action_bound).to(device)
        # two critics
        self.critic1 = QValueNet_Continuous(state_dim, hidden_dim, action_dim).to(device)
        self.critic2 = QValueNet_Continuous(state_dim, hidden_dim, action_dim).to(device)
        # two target critics
        self.target_critic1 = QValueNet_Continuous(state_dim, hidden_dim, action_dim).to(device)
        self.target_critic2 = QValueNet_Continuous(state_dim, hidden_dim, action_dim).to(device)
        # initialize with same parameters
        self.target_critic1.load_state_dict(self.critic1.state_dict())
        self.target_critic2.load_state_dict(self.critic2.state_dict())
        # specify optimizers
        self.optimizer_actor = torch.optim.Adam(self.actor.parameters(), lr=actor_lr)
        self.optimizer_critic1 = torch.optim.Adam(self.critic1.parameters(), lr=critic_lr)
        self.optimizer_critic2 = torch.optim.Adam(self.critic2.parameters(), lr=critic_lr)
        # 使用alpha的log值可以使训练稳定
        self.log_alpha = torch.tensor(np.log(0.01), dtype=torch.float, requires_grad = True)
        self.optimizer_log_alpha = torch.optim.Adam([self.log_alpha], lr=alpha_lr)

        self.target_entropy = target_entropy
        self.gamma = gamma
        self.tau = tau
        self.device = device
    
    def take_action(self, state):
        state = torch.tensor(np.array([state]), dtype=torch.float).to(self.device)
        action, _ = self.actor(state)
        return [action.item()]
    
    # calculate td target
    def calc_target(self, rewards, next_states, dones):
        next_action, log_prob = self.actor(next_states)
        entropy = -log_prob
        q1_values = self.target_critic1(next_states, next_action)
        q2_values = self.target_critic2(next_states, next_action)
        next_values = torch.min(q1_values, q2_values) + self.log_alpha.exp() * entropy
        td_target = rewards + self.gamma * next_values * (1-dones)
        return td_target

    # soft update method
    def soft_update(self, net, target_net):
        for param_target, param in zip(target_net.parameters(), net.parameters()):
            param_target.data.copy_(param_target.data * (1.0-self.tau) + param.data * self.tau)
        
    def update(self, transition_dict):
        states = torch.tensor(transition_dict['states'], dtype=torch.float).to(self.device)
        rewards = torch.tensor(transition_dict['rewards'], dtype=torch.float).view(-1,1).to(self.device)
        actions = torch.tensor(transition_dict['actions'], 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)

        rewards = (rewards + 8.0) / 8.0     #对倒立摆环境的奖励进行重塑，方便训练

        # update two Q-value network
        td_target = self.calc_target(rewards, next_states, dones).detach()
        critic1_loss = torch.mean(F.mse_loss(td_target, self.critic1(states, actions)))
        critic2_loss = torch.mean(F.mse_loss(td_target, self.critic2(states, actions)))

        self.optimizer_critic1.zero_grad()
        critic1_loss.backward()
        self.optimizer_critic1.step()
        self.optimizer_critic2.zero_grad()
        critic2_loss.backward()
        self.optimizer_critic2.step()

        # update policy network
        new_actions, log_prob = self.actor(states)
        entropy = - log_prob
        q1_value = self.critic1(states, new_actions)
        q2_value = self.critic2(states, new_actions)
        actor_loss = torch.mean(-self.log_alpha.exp() * entropy - torch.min(q1_value, q2_value))
        self.optimizer_actor.zero_grad()
        actor_loss.backward()
        self.optimizer_actor.step()

        # update temperature alpha
        alpha_loss = torch.mean((entropy - self.target_entropy).detach() * self.log_alpha.exp())
        self.optimizer_log_alpha.zero_grad()
        alpha_loss.backward()
        self.optimizer_log_alpha.step()

        # soft update target Q-value network
        self.soft_update(self.critic1, self.target_critic1)
        self.soft_update(self.critic2, self.target_critic2)


def train_off_policy_agent(env, agent, num_episodes, replay_buffer, minimal_size, batch_size, render, seed_number):
    return_list = []
    for i in range(10):
        with tqdm(total=int(num_episodes/10), desc='Iteration %d'%(i+1)) as pbar:
            for i_episode in range(int(num_episodes/10)):
                observation, _ = env.reset(seed=seed_number)
                done = False
                episode_return = 0

                while not done:
                    if render:
                        env.render()
                    action = agent.take_action(observation)
                    observation_, reward, terminated, truncated, _ = env.step(action)
                    done = terminated or truncated
                    replay_buffer.add(observation, action, reward, observation_, done)
                    # swap states
                    observation = observation_
                    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,
                            'rewards': b_r,
                            'next_states': b_ns,
                            '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)
    env.close()
    return return_list

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 plot_curve(return_list, mv_return, algorithm_name, env_name):
    episodes_list = list(range(len(return_list)))
    plt.plot(episodes_list, return_list, c='gray', alpha=0.6)
    plt.plot(episodes_list, mv_return)
    plt.xlabel('Episodes')
    plt.ylabel('Returns')
    plt.title('{} on {}'.format(algorithm_name, env_name))
    plt.show()



if __name__ == "__main__":
    rospy.init_node('ros_sac')
    rospy.loginfo(">>>>>>>>>> SAC for ROS >>>>>>>>>>")

    # reproducible
    seed_number = 0
    random.seed(seed_number)
    np.random.seed(seed_number)
    torch.manual_seed(seed_number)

    num_episodes = 150     # episodes length
    hidden_dim = 128        # hidden layers dimension
    gamma = 0.98            # discounted rate
    actor_lr = 1e-4         # lr of actor
    critic_lr = 1e-3        # lr of critic
    alpha_lr = 1e-4
    tau = 0.005             # soft update parameter
    buffer_size = 10000
    minimal_size = 1000
    batch_size = 64

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    env_name = 'Pendulum-v1'

    render = False
    if render:
        env = gym.make(id=env_name, render_mode='human')
    else:
        env = gym.make(id=env_name)

    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.shape[0]  
    action_bound = env.action_space.high[0]
    # entropy初始化为动作空间维度的负数
    target_entropy = - env.action_space.shape[0]

    replaybuffer = ReplayBuffer(buffer_size)
    agent = SAC_Continuous(state_dim, hidden_dim, action_dim, action_bound, actor_lr, critic_lr, alpha_lr, target_entropy, tau, gamma, device)
    return_list = train_off_policy_agent(env, agent, num_episodes, replaybuffer, minimal_size, batch_size, render, seed_number)

    mv_return = moving_average(return_list, 9)
    plot_curve(return_list, mv_return, 'SAC', env_name)

运行的结果如下所示

如果将代码中的render设置为True将会看到一个倒立摆，如下所示，但是这需要安装pygame，执行下面的语句即可

pip install gym[classic_control]

Reference

【ROS】ros-noetic和anaconda联合使用（1）
Hands on RL 之 Off-policy Maximum Entropy Actor-Critic (SAC)