add double_dqn

codes/double_dqn/dqn.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-12 00:50:49
+@LastEditor: John
+LastEditTime: 2020-09-01 22:54:02
+@Discription: 
+@Environment: python 3.7.7
+'''
+'''off-policy
+'''
+
+
+
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+import random
+import math
+import numpy as np
+from memory import ReplayBuffer
+from model import FCN
+class DQN:
+    def __init__(self, n_states, n_actions, gamma=0.99, epsilon_start=0.9, epsilon_end=0.05, epsilon_decay=200, memory_capacity=10000, policy_lr=0.01, batch_size=128, device="cpu"):
+        self.actions_count = 0
+        self.n_actions = n_actions  # 总的动作个数
+        self.device = device  # 设备，cpu或gpu等
+        self.gamma = gamma
+        # e-greedy策略相关参数
+        self.epsilon = 0
+        self.epsilon_start = epsilon_start
+        self.epsilon_end = epsilon_end
+        self.epsilon_decay = epsilon_decay
+        self.batch_size = batch_size
+        self.policy_net = FCN(n_states, n_actions).to(self.device)
+        self.target_net = FCN(n_states, n_actions).to(self.device)
+        # target_net的初始模型参数完全复制policy_net
+        self.target_net.load_state_dict(self.policy_net.state_dict())
+        self.target_net.eval()  # 不启用 BatchNormalization 和 Dropout
+        # 可查parameters()与state_dict()的区别，前者require_grad=True
+        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=policy_lr)
+        self.loss = 0
+        self.memory = ReplayBuffer(memory_capacity)
+
+    def select_action(self, state):
+        '''选择动作
+        Args:
+            state [array]: [description]
+        Returns:
+            action [array]: [description]
+        '''
+        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
+            math.exp(-1. * self.actions_count / self.epsilon_decay)
+        self.actions_count += 1
+        if random.random() > self.epsilon:
+            with torch.no_grad():
+                # 先转为张量便于丢给神经网络,state元素数据原本为float64
+                # 注意state=torch.tensor(state).unsqueeze(0)跟state=torch.tensor([state])等价
+                state = torch.tensor(
+                    [state], device=self.device, dtype=torch.float32)
+                # 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
+                q_value = self.policy_net(state)
+                # tensor.max(1)返回每行的最大值以及对应的下标，
+                # 如torch.return_types.max(values=tensor([10.3587]),indices=tensor([0]))
+                # 所以tensor.max(1)[1]返回最大值对应的下标，即action
+                action = q_value.max(1)[1].item()  
+        else:
+            action = random.randrange(self.n_actions)
+        return action
+
+    def update(self):
+
+        if len(self.memory) < self.batch_size:
+            return
+        # 从memory中随机采样transition
+        state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
+            self.batch_size)
+        # 转为张量
+        # 例如tensor([[-4.5543e-02, -2.3910e-01,  1.8344e-02,  2.3158e-01],...,[-1.8615e-02, -2.3921e-01, -1.1791e-02,  2.3400e-01]])
+        state_batch = torch.tensor(
+            state_batch, device=self.device, dtype=torch.float)
+        action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(
+            1)  # 例如tensor([[1],...,[0]])
+        reward_batch = torch.tensor(
+            reward_batch, device=self.device, dtype=torch.float)  # tensor([1., 1.,...,1])
+        next_state_batch = torch.tensor(
+            next_state_batch, device=self.device, dtype=torch.float)
+        done_batch = torch.tensor(np.float32(
+            done_batch), device=self.device).unsqueeze(1)  # 将bool转为float然后转为张量
+
+        # 计算当前(s_t,a)对应的Q(s_t, a)
+        # 关于torch.gather,对于a=torch.Tensor([[1,2],[3,4]])
+        # 那么a.gather(1,torch.Tensor([[0],[1]]))=torch.Tensor([[1],[3]])
+        q_values = self.policy_net(state_batch).gather(
+            dim=1, index=action_batch)  # 等价于self.forward
+        # 计算所有next states的V(s_{t+1})，即通过target_net中选取reward最大的对应states
+        next_state_values = self.target_net(
+            next_state_batch).max(1)[0].detach()  # 比如tensor([ 0.0060, -0.0171,...,])
+        # 计算 expected_q_value
+        # 对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
+        expected_q_values = reward_batch + self.gamma * \
+            next_state_values * (1-done_batch[0])
+        # self.loss = F.smooth_l1_loss(q_values,expected_q_values.unsqueeze(1)) # 计算 Huber loss
+        self.loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1))  # 计算 均方误差loss
+        # 优化模型
+        self.optimizer.zero_grad()  # zero_grad清除上一步所有旧的gradients from the last step
+        # loss.backward()使用backpropagation计算loss相对于所有parameters(需要gradients)的微分
+        self.loss.backward()
+        for param in self.policy_net.parameters():  # clip防止梯度爆炸
+            param.grad.data.clamp_(-1, 1)
+        self.optimizer.step()  # 更新模型
+        
+    def save_model(self,path):
+        torch.save(self.target_net.state_dict(), path)
+
+    def load_model(self,path):
+        self.policy_net.load_state_dict(torch.load(path))

codes/double_dqn/main.py

@@ -0,0 +1,143 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-12 00:48:57
+@LastEditor: John
+LastEditTime: 2020-09-01 22:54:23
+@Discription: 
+@Environment: python 3.7.7
+'''
+import gym
+import torch
+from dqn import DQN
+from plot import plot
+import argparse
+
+def get_args():
+    '''模型参数
+    '''
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--gamma", default=0.99,
+                        type=float)  # q-learning中的gamma
+    parser.add_argument("--epsilon_start", default=0.95,
+                        type=float)  # 基于贪心选择action对应的参数epsilon
+    parser.add_argument("--epsilon_end", default=0.05, type=float)
+    parser.add_argument("--epsilon_decay", default=500, type=float)
+    parser.add_argument("--policy_lr", default=0.01, type=float)
+    parser.add_argument("--memory_capacity", default=1000,
+                        type=int, help="capacity of Replay Memory") 
+
+    parser.add_argument("--batch_size", default=32, type=int,
+                        help="batch size of memory sampling")
+    parser.add_argument("--train_eps", default=200, type=int) # 训练的最大episode数目
+    parser.add_argument("--train_steps", default=200, type=int) # 训练每个episode的长度
+    parser.add_argument("--eval_eps", default=200, type=int) # 训练的最大episode数目
+    parser.add_argument("--eval_steps", default=200, type=int) # 训练每个episode的长度
+    parser.add_argument("--target_update", default=2, type=int,
+                        help="when(every default 10 eisodes) to update target net ")
+    config = parser.parse_args()
+
+    return config
+
+def train():
+    cfg = get_args()
+    # if gpu is to be used
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
+    env = gym.make('CartPole-v0').unwrapped # 可google为什么unwrapped gym，此处一般不需要
+    env.seed(1) # 设置env随机种子
+    n_states = env.observation_space.shape[0]
+    n_actions = env.action_space.n
+    agent = DQN(n_states=n_states, n_actions=n_actions, device=device, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start,
+                epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay, policy_lr=cfg.policy_lr, memory_capacity=cfg.memory_capacity, batch_size=cfg.batch_size)
+    rewards = []
+    moving_average_rewards = []
+    ep_steps = []
+    for i_episode in range(1, cfg.train_eps+1):
+        state = env.reset() # reset环境状态
+        ep_reward = 0
+        for i_step in range(1, cfg.train_steps+1):
+            action = agent.select_action(state) # 根据当前环境state选择action
+            next_state, reward, done, _ = env.step(action) # 更新环境参数
+            ep_reward += reward
+            agent.memory.push(state, action, reward, next_state, done) # 将state等这些transition存入memory
+            state = next_state # 跳转到下一个状态
+            agent.update() # 每步更新网络
+            if done:
+                break
+        # 更新target network，复制DQN中的所有weights and biases
+        if i_episode % cfg.target_update == 0:
+            agent.target_net.load_state_dict(agent.policy_net.state_dict())
+        print('Episode:', i_episode, ' Reward: %i' %
+              int(ep_reward), 'n_steps:', i_step, 'done: ', done,' Explore: %.2f' % agent.epsilon)
+        ep_steps.append(i_step)
+        rewards.append(ep_reward)
+        # 计算滑动窗口的reward
+        if i_episode == 1:
+            moving_average_rewards.append(ep_reward)
+        else:
+            moving_average_rewards.append(
+                0.9*moving_average_rewards[-1]+0.1*ep_reward)
+    import os
+    import numpy as np
+    save_path = os.path.dirname(__file__)+"/saved_model/"
+    if not os.path.exists(save_path):
+        os.mkdir(save_path)
+    agent.save_model(save_path+'checkpoint.pth')
+    # 存储reward等相关结果
+    output_path = os.path.dirname(__file__)+"/result/"
+    # 检测是否存在文件夹
+    if not os.path.exists(output_path):
+        os.mkdir(output_path)
+    np.save(output_path+"rewards.npy", rewards)
+    np.save(output_path+"moving_average_rewards.npy", moving_average_rewards)
+    np.save(output_path+"steps.npy", ep_steps)
+    print('Complete！')
+    plot(rewards)
+    plot(moving_average_rewards, ylabel="moving_average_rewards")
+    plot(ep_steps, ylabel="steps_of_each_episode")
+
+def eval():
+    cfg = get_args()
+    # if gpu is to be used
+    env = gym.make('CartPole-v0').unwrapped # 可google为什么unwrapped gym，此处一般不需要
+    env.seed(1) # 设置env随机种子
+    n_states = env.observation_space.shape[0]
+    n_actions = env.action_space.n
+    agent = DQN(n_states=n_states, n_actions=n_actions, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start,
+                epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay, policy_lr=cfg.policy_lr, memory_capacity=cfg.memory_capacity, batch_size=cfg.batch_size)
+    import os
+    save_path = os.path.dirname(__file__)+"/saved_model/"
+    if not os.path.exists(save_path):
+        os.mkdir(save_path)
+    agent.load_model(save_path+'checkpoint.pth')
+    rewards = []
+    moving_average_rewards = []
+    ep_steps = []
+    for i_episode in range(1, cfg.eval_eps+1):
+        state = env.reset() # reset环境状态
+        ep_reward = 0
+        for i_step in range(1, cfg.eval_steps+1):
+            action = agent.select_action(state) # 根据当前环境state选择action
+            next_state, reward, done, _ = env.step(action) # 更新环境参数
+            ep_reward += reward
+            state = next_state # 跳转到下一个状态
+            if done:
+                break
+        print('Episode:', i_episode, ' Reward: %i' %
+              int(ep_reward), 'n_steps:', i_step, 'done: ', done,' Explore: %.2f' % agent.epsilon)
+        ep_steps.append(i_step)
+        rewards.append(ep_reward)
+        # 计算滑动窗口的reward
+        if i_episode == 1:
+            moving_average_rewards.append(ep_reward)
+        else:
+            moving_average_rewards.append(
+                0.9*moving_average_rewards[-1]+0.1*ep_reward)
+    plot(rewards,save_fig=False)
+    plot(moving_average_rewards, ylabel="moving_average_rewards",save_fig=False)
+    plot(ep_steps, ylabel="steps_of_each_episode",save_fig=False)
+if __name__ == "__main__":
+    # train()
+    eval()

codes/double_dqn/memory.py

@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-10 15:27:16
+@LastEditor: John
+@LastEditTime: 2020-06-14 11:36:24
+@Discription: 
+@Environment: python 3.7.7
+'''
+import random
+import numpy as np
+
+class ReplayBuffer:
+    
+    def __init__(self, capacity):
+        self.capacity = capacity
+        self.buffer = []
+        self.position = 0
+    
+    def push(self, state, action, reward, next_state, done):
+        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)
+        state, action, reward, next_state, done =  zip(*batch)
+        return state, action, reward, next_state, done
+    
+    def __len__(self):
+        return len(self.buffer)
+

codes/double_dqn/model.py

@@ -0,0 +1,30 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-12 00:47:02
+@LastEditor: John
+LastEditTime: 2020-08-19 16:55:54
+@Discription: 
+@Environment: python 3.7.7
+'''
+import torch.nn as nn
+import torch.nn.functional as F
+
+class FCN(nn.Module):
+    def __init__(self, n_states=4, n_actions=18):
+        """ 初始化q网络，为全连接网络
+            n_states: 输入的feature即环境的state数目
+            n_actions: 输出的action总个数
+        """
+        super(FCN, self).__init__()
+        self.fc1 = nn.Linear(n_states, 128) # 输入层
+        self.fc2 = nn.Linear(128, 128) # 隐藏层
+        self.fc3 = nn.Linear(128, n_actions) # 输出层
+        
+    def forward(self, x):
+        # 各层对应的激活函数
+        x = F.relu(self.fc1(x)) 
+        x = F.relu(self.fc2(x))
+        return self.fc3(x)

codes/double_dqn/plot.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-11 16:30:09
+@LastEditor: John
+LastEditTime: 2020-09-01 22:46:43
+@Discription: 
+@Environment: python 3.7.7
+'''
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import numpy as np
+import os 
+
+def plot(item,ylabel='rewards',save_fig = True):
+    sns.set()
+    plt.figure()
+    plt.plot(np.arange(len(item)), item)
+    plt.title(ylabel+' of DQN') 
+    plt.ylabel(ylabel)
+    plt.xlabel('episodes')
+    if save_fig:
+        plt.savefig(os.path.dirname(__file__)+"/result/"+ylabel+".png")
+    plt.show()
+if __name__ == "__main__":
+
+    output_path = os.path.dirname(__file__)+"/result/"
+    rewards=np.load(output_path+"rewards.npy", )
+    moving_average_rewards=np.load(output_path+"moving_average_rewards.npy",)
+    plot(rewards)
+    plot(moving_average_rewards,ylabel='moving_average_rewards')