update projects

projects/codes/DDPG/ddpg.py

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+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-09 20:25:52
+@LastEditor: John
+LastEditTime: 2022-06-09 19:04:44
+@Discription: 
+@Environment: python 3.7.7
+'''
+import random
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+class ReplayBuffer:
+    def __init__(self, capacity):
+        self.capacity = capacity # 经验回放的容量
+        self.buffer = [] # 缓冲区
+        self.position = 0 
+    
+    def push(self, state, action, reward, next_state, done):
+        ''' 缓冲区是一个队列，容量超出时去掉开始存入的转移(transition)
+        '''
+        if len(self.buffer) < self.capacity:
+            self.buffer.append(None)
+        self.buffer[self.position] = (state, action, reward, next_state, done)
+        self.position = (self.position + 1) % self.capacity 
+    
+    def sample(self, batch_size):
+        batch = random.sample(self.buffer, batch_size) # 随机采出小批量转移
+        state, action, reward, next_state, done =  zip(*batch) # 解压成状态，动作等
+        return state, action, reward, next_state, done
+    
+    def __len__(self):
+        ''' 返回当前存储的量
+        '''
+        return len(self.buffer)
+class Actor(nn.Module):
+    def __init__(self, n_states, n_actions, hidden_dim, init_w=3e-3):
+        super(Actor, self).__init__()  
+        self.linear1 = nn.Linear(n_states, hidden_dim)
+        self.linear2 = nn.Linear(hidden_dim, hidden_dim)
+        self.linear3 = nn.Linear(hidden_dim, n_actions)
+        
+        self.linear3.weight.data.uniform_(-init_w, init_w)
+        self.linear3.bias.data.uniform_(-init_w, init_w)
+        
+    def forward(self, x):
+        x = F.relu(self.linear1(x))
+        x = F.relu(self.linear2(x))
+        x = torch.tanh(self.linear3(x))
+        return x
+class Critic(nn.Module):
+    def __init__(self, n_states, n_actions, hidden_dim, init_w=3e-3):
+        super(Critic, self).__init__()
+        
+        self.linear1 = nn.Linear(n_states + n_actions, hidden_dim)
+        self.linear2 = nn.Linear(hidden_dim, hidden_dim)
+        self.linear3 = nn.Linear(hidden_dim, 1)
+        # 随机初始化为较小的值
+        self.linear3.weight.data.uniform_(-init_w, init_w)
+        self.linear3.bias.data.uniform_(-init_w, init_w)
+        
+    def forward(self, state, action):
+        # 按维数1拼接
+        x = torch.cat([state, action], 1)
+        x = F.relu(self.linear1(x))
+        x = F.relu(self.linear2(x))
+        x = self.linear3(x)
+        return x
+class DDPG:
+    def __init__(self, n_states, n_actions, cfg):
+        self.device = torch.device(cfg.device)
+        self.critic = Critic(n_states, n_actions, cfg.hidden_dim).to(self.device)
+        self.actor = Actor(n_states, n_actions, cfg.hidden_dim).to(self.device)
+        self.target_critic = Critic(n_states, n_actions, cfg.hidden_dim).to(self.device)
+        self.target_actor = Actor(n_states, n_actions, cfg.hidden_dim).to(self.device)
+
+        # 复制参数到目标网络
+        for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
+            target_param.data.copy_(param.data)
+        for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
+            target_param.data.copy_(param.data)
+
+        self.critic_optimizer = optim.Adam(
+            self.critic.parameters(),  lr=cfg.critic_lr)
+        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=cfg.actor_lr)
+        self.memory = ReplayBuffer(cfg.memory_capacity)
+        self.batch_size = cfg.batch_size
+        self.soft_tau = cfg.soft_tau # 软更新参数
+        self.gamma = cfg.gamma
+
+    def choose_action(self, state):
+        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
+        action = self.actor(state)
+        return action.detach().cpu().numpy()[0, 0]
+
+    def update(self):
+        if len(self.memory) < self.batch_size: # 当 memory 中不满足一个批量时，不更新策略
+            return
+        # 从经验回放中(replay memory)中随机采样一个批量的转移(transition)
+        state, action, reward, next_state, done = self.memory.sample(self.batch_size)
+        # 转变为张量
+        state = torch.FloatTensor(np.array(state)).to(self.device)
+        next_state = torch.FloatTensor(np.array(next_state)).to(self.device)
+        action = torch.FloatTensor(np.array(action)).to(self.device)
+        reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
+        done = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(self.device)
+       
+        policy_loss = self.critic(state, self.actor(state))
+        policy_loss = -policy_loss.mean()
+        next_action = self.target_actor(next_state)
+        target_value = self.target_critic(next_state, next_action.detach())
+        expected_value = reward + (1.0 - done) * self.gamma * target_value
+        expected_value = torch.clamp(expected_value, -np.inf, np.inf)
+
+        value = self.critic(state, action)
+        value_loss = nn.MSELoss()(value, expected_value.detach())
+        
+        self.actor_optimizer.zero_grad()
+        policy_loss.backward()
+        self.actor_optimizer.step()
+        self.critic_optimizer.zero_grad()
+        value_loss.backward()
+        self.critic_optimizer.step()
+        # 软更新
+        for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
+            target_param.data.copy_(
+                target_param.data * (1.0 - self.soft_tau) +
+                param.data * self.soft_tau
+            )
+        for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
+            target_param.data.copy_(
+                target_param.data * (1.0 - self.soft_tau) +
+                param.data * self.soft_tau
+            )
+    def save(self,path):
+        torch.save(self.actor.state_dict(), path+'checkpoint.pt')
+
+    def load(self,path):
+        self.actor.load_state_dict(torch.load(path+'checkpoint.pt')) 

projects/codes/DDPG/env.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-10 15:28:30
+@LastEditor: John
+LastEditTime: 2021-09-16 00:52:30
+@Discription: 
+@Environment: python 3.7.7
+'''
+import gym
+import numpy as np
+
+class NormalizedActions(gym.ActionWrapper):
+    ''' 将action范围重定在[0.1]之间
+    '''
+    def action(self, action):
+        low_bound   = self.action_space.low
+        upper_bound = self.action_space.high
+        action = low_bound + (action + 1.0) * 0.5 * (upper_bound - low_bound)
+        action = np.clip(action, low_bound, upper_bound)
+        return action
+
+    def reverse_action(self, action):
+        low_bound   = self.action_space.low
+        upper_bound = self.action_space.high
+        action = 2 * (action - low_bound) / (upper_bound - low_bound) - 1
+        action = np.clip(action, low_bound, upper_bound)
+        return action
+
+class OUNoise(object):
+    '''Ornstein–Uhlenbeck噪声
+    '''
+    def __init__(self, action_space, mu=0.0, theta=0.15, max_sigma=0.3, min_sigma=0.3, decay_period=100000):
+        self.mu           = mu # OU噪声的参数
+        self.theta        = theta # OU噪声的参数
+        self.sigma        = max_sigma # OU噪声的参数
+        self.max_sigma    = max_sigma
+        self.min_sigma    = min_sigma
+        self.decay_period = decay_period
+        self.n_actions   = action_space.shape[0]
+        self.low          = action_space.low
+        self.high         = action_space.high
+        self.reset()
+    def reset(self):
+        self.obs = np.ones(self.n_actions) * self.mu
+    def evolve_obs(self):
+        x  = self.obs
+        dx = self.theta * (self.mu - x) + self.sigma * np.random.randn(self.n_actions)
+        self.obs = x + dx
+        return self.obs
+    def get_action(self, action, t=0):
+        ou_obs = self.evolve_obs()
+        self.sigma = self.max_sigma - (self.max_sigma - self.min_sigma) * min(1.0, t / self.decay_period) # sigma会逐渐衰减
+        return np.clip(action + ou_obs, self.low, self.high) # 动作加上噪声后进行剪切

projects/codes/DDPG/outputs/Pendulum-v1/20220713-225402/results/params.json

@@ -0,0 +1,18 @@
+{
+    "algo_name": "DDPG",
+    "env_name": "Pendulum-v1",
+    "train_eps": 300,
+    "test_eps": 20,
+    "gamma": 0.99,
+    "critic_lr": 0.001,
+    "actor_lr": 0.0001,
+    "memory_capacity": 8000,
+    "batch_size": 128,
+    "target_update": 2,
+    "soft_tau": 0.01,
+    "hidden_dim": 256,
+    "deivce": "cpu",
+    "result_path": "C:\\Users\\24438\\Desktop\\rl-tutorials/outputs/DDPG/outputs/Pendulum-v1/20220713-225402/results//",
+    "model_path": "C:\\Users\\24438\\Desktop\\rl-tutorials/outputs/DDPG/outputs/Pendulum-v1/20220713-225402/models/",
+    "save_fig": true
+}

projects/codes/DDPG/task0.py

@@ -0,0 +1,133 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-11 20:58:21
+@LastEditor: John
+LastEditTime: 2022-07-21 21:51:34
+@Discription: 
+@Environment: python 3.7.7
+'''
+import sys,os
+curr_path = os.path.dirname(os.path.abspath(__file__))  # current path
+parent_path = os.path.dirname(curr_path)  # parent path
+sys.path.append(parent_path)  # add to system path
+
+import datetime
+import gym
+import torch
+import argparse
+
+from env import NormalizedActions,OUNoise
+from ddpg import DDPG
+from common.utils import save_results,make_dir
+from common.utils import plot_rewards,save_args
+
+def get_args():
+    """ Hyperparameters
+    """
+    curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # Obtain current time
+    parser = argparse.ArgumentParser(description="hyperparameters")      
+    parser.add_argument('--algo_name',default='DDPG',type=str,help="name of algorithm")
+    parser.add_argument('--env_name',default='Pendulum-v1',type=str,help="name of environment")
+    parser.add_argument('--train_eps',default=300,type=int,help="episodes of training")
+    parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
+    parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
+    parser.add_argument('--critic_lr',default=1e-3,type=float,help="learning rate of critic")
+    parser.add_argument('--actor_lr',default=1e-4,type=float,help="learning rate of actor")
+    parser.add_argument('--memory_capacity',default=8000,type=int,help="memory capacity")
+    parser.add_argument('--batch_size',default=128,type=int)
+    parser.add_argument('--target_update',default=2,type=int)
+    parser.add_argument('--soft_tau',default=1e-2,type=float)
+    parser.add_argument('--hidden_dim',default=256,type=int)
+    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
+    parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
+            '/' + curr_time + '/results/' )
+    parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
+            '/' + curr_time + '/models/' ) # path to save models
+    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")   
+    args = parser.parse_args()                           
+    return args
+
+def env_agent_config(cfg,seed=1):
+    env = NormalizedActions(gym.make(cfg.env_name)) # 装饰action噪声
+    env.seed(seed) # 随机种子
+    n_states = env.observation_space.shape[0]
+    n_actions = env.action_space.shape[0]
+    agent = DDPG(n_states,n_actions,cfg)
+    return env,agent
+def train(cfg, env, agent):
+    print('Start training!')
+    print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
+    ou_noise = OUNoise(env.action_space)  # noise of action
+    rewards = [] # 记录所有回合的奖励
+    ma_rewards = []  # 记录所有回合的滑动平均奖励
+    for i_ep in range(cfg.train_eps):
+        state = env.reset()
+        ou_noise.reset()
+        done = False
+        ep_reward = 0
+        i_step = 0
+        while not done:
+            i_step += 1
+            action = agent.choose_action(state)
+            action = ou_noise.get_action(action, i_step) 
+            next_state, reward, done, _ = env.step(action)
+            ep_reward += reward
+            agent.memory.push(state, action, reward, next_state, done)
+            agent.update()
+            state = next_state
+        if (i_ep+1)%10 == 0:
+            print(f'Env:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
+        rewards.append(ep_reward)
+        if ma_rewards:
+            ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
+        else:
+            ma_rewards.append(ep_reward)
+    print('Finish training!')
+    return {'rewards':rewards,'ma_rewards':ma_rewards}
+
+def test(cfg, env, agent):
+    print('Start testing')
+    print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
+    rewards = [] # 记录所有回合的奖励
+    ma_rewards = []  # 记录所有回合的滑动平均奖励
+    for i_ep in range(cfg.test_eps):
+        state = env.reset() 
+        done = False
+        ep_reward = 0
+        i_step = 0
+        while not done:
+            i_step += 1
+            action = agent.choose_action(state)
+            next_state, reward, done, _ = env.step(action)
+            ep_reward += reward
+            state = next_state
+        rewards.append(ep_reward)
+        if ma_rewards:
+            ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
+        else:
+            ma_rewards.append(ep_reward)
+        print(f"Epside:{i_ep+1}/{cfg.test_eps}, Reward:{ep_reward:.1f}")
+    print('Finish testing!')
+    return {'rewards':rewards,'ma_rewards':ma_rewards}
+if __name__ == "__main__":
+    cfg = get_args()
+    # training
+    env,agent = env_agent_config(cfg,seed=1)
+    res_dic = train(cfg, env, agent)
+    make_dir(cfg.result_path, cfg.model_path)
+    save_args(cfg)
+    agent.save(path=cfg.model_path)
+    save_results(res_dic, tag='train',
+                 path=cfg.result_path)  
+    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="train")  
+    # testing
+    env,agent = env_agent_config(cfg,seed=10)
+    agent.load(path=cfg.model_path)
+    res_dic = test(cfg,env,agent)
+    save_results(res_dic, tag='test',
+                 path=cfg.result_path)  
+    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="test")   
+