update

.gitignore

@@ -1,3 +1,4 @@
 .DS_STORE
 __pycache__
 .vscode
+test.py

codes/A2C/test.py

@@ -1,162 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2021-03-20 17:43:17
-LastEditor: John
-LastEditTime: 2021-04-05 11:19:20
-Discription: 
-Environment: 
-'''
-import sys
-import torch  
-import gym
-import numpy as np  
-import torch.nn as nn
-import torch.optim as optim
-import torch.nn.functional as F
-from torch.autograd import Variable
-import matplotlib.pyplot as plt
-import pandas as pd
-
-
-learning_rate = 3e-4
-
-# Constants
-GAMMA = 0.99
-
-class A2CConfig:
-    ''' hyperparameters
-    '''
-    def __init__(self):
-        self.gamma = 0.99
-        self.lr = 3e-4 # learnning rate
-        self.actor_lr = 1e-4 # learnning rate of actor network
-        self.memory_capacity = 10000 # capacity of replay memory
-        self.batch_size = 128
-        self.train_eps = 3000
-        self.train_steps = 200
-        self.eval_eps = 200
-        self.eval_steps = 200
-        self.target_update = 4
-        self.hidden_dim = 256
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    
-    
-class ActorCritic(nn.Module):
-    def __init__(self, n_states, n_actions, hidden_dim, learning_rate=3e-4):
-        super(ActorCritic, self).__init__()
-
-        self.n_actions = n_actions
-        self.critic_linear1 = nn.Linear(n_states, hidden_dim)
-        self.critic_linear2 = nn.Linear(hidden_dim, 1)
-
-        self.actor_linear1 = nn.Linear(n_states, hidden_dim)
-        self.actor_linear2 = nn.Linear(hidden_dim, n_actions)
-    
-    def forward(self, state):
-        state = Variable(torch.from_numpy(state).float().unsqueeze(0))
-        value = F.relu(self.critic_linear1(state))
-        value = self.critic_linear2(value)
-        policy_dist = F.relu(self.actor_linear1(state))
-        policy_dist = F.softmax(self.actor_linear2(policy_dist), dim=1)
-
-        return value, policy_dist
-
-class A2C:
-    def __init__(self,n_states,n_actions,cfg):
-        self.model = ActorCritic(n_states, n_actions, cfg.hidden_dim)
-        self.optimizer = optim.Adam(self.model.parameters(), lr=cfg.lr)
-    def choose_action(self,state):
-        pass
-    def update(self):
-        pass
-    
-def train(cfg,env,agent):
-    n_states = env.observation_space.shape[0]
-    n_actions = env.action_space.n
-    actor_critic = ActorCritic(n_states, n_actions, cfg.hidden_dim)
-    ac_optimizer = optim.Adam(actor_critic.parameters(), lr=learning_rate)
-
-    all_lengths = []
-    average_lengths = []
-    all_rewards = []
-    entropy_term = 0
-
-    for episode in range(cfg.train_eps):
-        log_probs = []
-        values = []
-        rewards = []
-        state = env.reset()
-        for steps in range(cfg.train_steps):
-            value, policy_dist = actor_critic.forward(state)
-            value = value.detach().numpy()[0,0]
-            dist = policy_dist.detach().numpy() 
-
-            action = np.random.choice(n_actions, p=np.squeeze(dist))
-            log_prob = torch.log(policy_dist.squeeze(0)[action])
-            entropy = -np.sum(np.mean(dist) * np.log(dist))
-            new_state, reward, done, _ = env.step(action)
-
-            rewards.append(reward)
-            values.append(value)
-            log_probs.append(log_prob)
-            entropy_term += entropy
-            state = new_state
-            
-            if done or steps == cfg.train_steps-1:
-                Qval, _ = actor_critic.forward(new_state)
-                Qval = Qval.detach().numpy()[0,0]
-                all_rewards.append(np.sum(rewards))
-                all_lengths.append(steps)
-                average_lengths.append(np.mean(all_lengths[-10:]))
-                if episode % 10 == 0:                    
-                    sys.stdout.write("episode: {}, reward: {}, total length: {}, average length: {} \n".format(episode, np.sum(rewards), steps+1, average_lengths[-1]))
-                break
-        
-        # compute Q values
-        Qvals = np.zeros_like(values)
-        for t in reversed(range(len(rewards))):
-            Qval = rewards[t] + GAMMA * Qval
-            Qvals[t] = Qval
-  
-        #update actor critic
-        values = torch.FloatTensor(values)
-        Qvals = torch.FloatTensor(Qvals)
-        log_probs = torch.stack(log_probs)
-        
-        advantage = Qvals - values
-        actor_loss = (-log_probs * advantage).mean()
-        critic_loss = 0.5 * advantage.pow(2).mean()
-        ac_loss = actor_loss + critic_loss + 0.001 * entropy_term
-
-        ac_optimizer.zero_grad()
-        ac_loss.backward()
-        ac_optimizer.step()
-
-        
-    
-    # Plot results
-    smoothed_rewards = pd.Series.rolling(pd.Series(all_rewards), 10).mean()
-    smoothed_rewards = [elem for elem in smoothed_rewards]
-    plt.plot(all_rewards)
-    plt.plot(smoothed_rewards)
-    plt.plot()
-    plt.xlabel('Episode')
-    plt.ylabel('Reward')
-    plt.show()
-
-    plt.plot(all_lengths)
-    plt.plot(average_lengths)
-    plt.xlabel('Episode')
-    plt.ylabel('Episode length')
-    plt.show()
-
-if __name__ == "__main__":
-    cfg = A2CConfig()
-    env = gym.make("CartPole-v0")
-    n_states = env.observation_space.shape[0]
-    n_actions = env.action_space.n
-    agent = A2C(n_states,n_actions,cfg)
-    train(cfg,env,agent)    

codes/DDPG/agent.py

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-09 20:25:52
 @LastEditor: John
-LastEditTime: 2021-03-31 00:56:32
+LastEditTime: 2021-05-04 14:50:17
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -26,6 +26,7 @@ class DDPG:
         self.target_critic = Critic(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
         self.target_actor = Actor(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
 
+        # copy parameters to target net
         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()):
@@ -42,7 +43,6 @@ class DDPG:
     def choose_action(self, state):
         state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
         action = self.actor(state)
-        # torch.detach()用于切断反向传播
         return action.detach().cpu().numpy()[0, 0]
 
     def update(self):
@@ -50,13 +50,13 @@ class DDPG:
             return
         state, action, reward, next_state, done = self.memory.sample(
             self.batch_size)
-        # 将所有变量转为张量
+        # convert variables to Tensor
         state = torch.FloatTensor(state).to(self.device)
         next_state = torch.FloatTensor(next_state).to(self.device)
         action = torch.FloatTensor(action).to(self.device)
         reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
         done = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(self.device)
-        # 注意critic将(s_t,a)作为输入
+       
         policy_loss = self.critic(state, self.actor(state))
         policy_loss = -policy_loss.mean()
         next_action = self.target_actor(next_state)

codes/DDPG/main.py

@@ -1,94 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-@Author: John
-@Email: johnjim0816@gmail.com
-@Date: 2020-06-11 20:58:21
-@LastEditor: John
-LastEditTime: 2021-04-29 01:58:50
-@Discription: 
-@Environment: python 3.7.7
-'''
-import sys,os
-from pathlib import Path
-import sys,os
-curr_path = os.path.dirname(__file__)
-parent_path=os.path.dirname(curr_path) 
-sys.path.append(parent_path) # add current terminal path to sys.path
-
-import torch
-import gym
-import numpy as np
-import datetime
-from DDPG.agent import DDPG
-from DDPG.env import NormalizedActions,OUNoise
-from common.plot import plot_rewards
-from common.utils import save_results
-
-SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
-SAVED_MODEL_PATH = curr_path+"/saved_model/"+SEQUENCE+'/' # path to save model
-if not os.path.exists(curr_path+"/saved_model/"): os.mkdir(curr_path+"/saved_model/")
-if not os.path.exists(SAVED_MODEL_PATH): os.mkdir(SAVED_MODEL_PATH)
-RESULT_PATH = curr_path+"/results/"+SEQUENCE+'/' # path to save rewards
-if not os.path.exists(curr_path+"/results/"): os.mkdir(curr_path+"/results/")
-if not os.path.exists(RESULT_PATH): os.mkdir(RESULT_PATH)
-
-class DDPGConfig:
-    def __init__(self):
-        self.env = 'Pendulum-v0'
-        self.algo = 'DDPG'
-        self.gamma = 0.99
-        self.critic_lr = 1e-3  
-        self.actor_lr = 1e-4 
-        self.memory_capacity = 10000
-        self.batch_size = 128
-        self.train_eps =300
-        self.eval_eps = 200
-        self.eval_steps = 200
-        self.target_update = 4
-        self.hidden_dim = 30
-        self.soft_tau=1e-2
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-def train(cfg,env,agent):
-    print('Start to train ! ')
-    ou_noise = OUNoise(env.action_space) # action noise
-    rewards = []
-    ma_rewards = [] # moving average rewards
-    ep_steps = []
-    for i_episode 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)  # 即paper中的random process
-            next_state, reward, done, _ = env.step(action)
-            ep_reward += reward
-            agent.memory.push(state, action, reward, next_state, done)
-            agent.update()
-            state = next_state
-        print('Episode:{}/{}, Reward:{}'.format(i_episode+1,cfg.train_eps,ep_reward))
-        ep_steps.append(i_step)
-        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('Complete training！')
-    return rewards,ma_rewards
-
-if __name__ == "__main__":
-    cfg = DDPGConfig()
-    env = NormalizedActions(gym.make("Pendulum-v0"))
-    env.seed(1) # 设置env随机种子
-    state_dim = env.observation_space.shape[0]
-    action_dim = env.action_space.shape[0]
-    agent = DDPG(state_dim,action_dim,cfg)
-    rewards,ma_rewards = train(cfg,env,agent)
-    agent.save(path=SAVED_MODEL_PATH)
-    save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
-    plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)
-    

codes/DDPG/task0_train.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-11 20:58:21
+@LastEditor: John
+LastEditTime: 2021-05-04 14:49:45
+@Discription: 
+@Environment: python 3.7.7
+'''
+import sys,os
+curr_path = os.path.dirname(__file__)
+parent_path = os.path.dirname(curr_path)
+sys.path.append(parent_path)  # add current terminal path to sys.path
+
+import datetime
+import gym
+import torch
+
+from DDPG.env import NormalizedActions, OUNoise
+from DDPG.agent import DDPG
+from common.utils import save_results,make_dir
+from common.plot import plot_rewards
+
+curr_time = datetime.datetime.now().strftime(
+    "%Y%m%d-%H%M%S")  # obtain current time
+
+
+class DDPGConfig:
+    def __init__(self):
+        self.algo = 'DDPG'
+        self.env = 'Pendulum-v0' # env name
+        self.result_path = curr_path+"/outputs/" + self.env + \
+            '/'+curr_time+'/results/'  # path to save results
+        self.model_path = curr_path+"/outputs/" + self.env + \
+            '/'+curr_time+'/models/'  # path to save results
+        self.gamma = 0.99
+        self.critic_lr = 1e-3
+        self.actor_lr = 1e-4
+        self.memory_capacity = 10000
+        self.batch_size = 128
+        self.train_eps = 300
+        self.eval_eps = 50
+        self.eval_steps = 200
+        self.target_update = 4
+        self.hidden_dim = 30
+        self.soft_tau = 1e-2
+        self.device = torch.device(
+            "cuda" if torch.cuda.is_available() else "cpu")
+
+def env_agent_config(cfg,seed=1):
+    env = NormalizedActions(gym.make(cfg.env)) 
+    env.seed(seed)
+    state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.shape[0]
+    agent = DDPG(state_dim,action_dim,cfg)
+    return env,agent
+
+def train(cfg, env, agent):
+    print('Start to train ! ')
+    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
+    ou_noise = OUNoise(env.action_space)  # action noise
+    rewards = []
+    ma_rewards = []  # moving average rewards
+    for i_episode 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)  # 即paper中的random process
+            next_state, reward, done, _ = env.step(action)
+            ep_reward += reward
+            agent.memory.push(state, action, reward, next_state, done)
+            agent.update()
+            state = next_state
+        print('Episode:{}/{}, Reward:{}'.format(i_episode+1, cfg.train_eps, ep_reward))
+        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('Complete training！')
+    return rewards, ma_rewards
+
+def eval(cfg, env, agent):
+    print('Start to Eval ! ')
+    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
+    rewards = []
+    ma_rewards = []  # moving average rewards
+    for i_episode in range(cfg.eval_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
+        print('Episode:{}/{}, Reward:{}'.format(i_episode+1, cfg.train_eps, ep_reward))
+        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('Complete Eval！')
+    return rewards, ma_rewards
+
+
+if __name__ == "__main__":
+    cfg = DDPGConfig()
+
+    # train
+    env,agent = env_agent_config(cfg,seed=1)
+    rewards, ma_rewards = train(cfg, env, agent)
+    make_dir(cfg.result_path, cfg.model_path)
+    agent.save(path=cfg.model_path)
+    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
+    plot_rewards(rewards, ma_rewards, tag="train",
+                 algo=cfg.algo, path=cfg.result_path)
+    
+    # eval
+    env,agent = env_agent_config(cfg,seed=10)
+    agent.load(path=cfg.model_path)
+    rewards,ma_rewards = eval(cfg,env,agent)
+    save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
+    plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
+    

codes/DQN/task0_train.py

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:48:57
 @LastEditor: John
-LastEditTime: 2021-04-29 22:23:38
+LastEditTime: 2021-05-04 15:01:34
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -18,16 +18,13 @@ import datetime
 import torch
 import gym
 
-from common.utils import save_results, make_dir, del_empty_dir
+from common.utils import save_results, make_dir
 from common.plot import plot_rewards
 from DQN.agent import DQN
 
-
-
 curr_time = datetime.datetime.now().strftime(
     "%Y%m%d-%H%M%S")  # obtain current time
 
-
 class DQNConfig:
     def __init__(self):
         self.algo = "DQN"  # name of algo
@@ -80,7 +77,7 @@ def train(cfg, env, agent):
             agent.target_net.load_state_dict(agent.policy_net.state_dict())
         print('Episode:{}/{}, Reward:{}'.format(i_episode+1, cfg.train_eps, ep_reward))
         rewards.append(ep_reward)
-        # 计算滑动窗口的reward
+        # save ma rewards
         if ma_rewards:
             ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
         else:

codes/DQN_cnn/README.md

@@ -1,2 +0,0 @@
-# DQN with cnn
-原理与[DQN](../DQN)相同，只是将神经网络换成卷积神经网络，用于二维观测信息(state或obervation)

codes/DQN_cnn/agent.py

@@ -1,107 +0,0 @@
-import random
-import math
-import torch
-import torch.optim as optim
-import torch.nn.functional as F
-from DQN_cnn.memory import ReplayBuffer
-from DQN_cnn.model import CNN
-
-
-class DQNcnn:
-    def __init__(self, screen_height,screen_width, action_dim, cfg):
-
-        self.device = cfg.device  
-        self.action_dim = action_dim
-        self.gamma = cfg.gamma
-        # e-greedy策略相关参数
-        self.actions_count = 0 
-        self.epsilon = 0
-        self.epsilon_start = cfg.epsilon_start
-        self.epsilon_end = cfg.epsilon_end
-        self.epsilon_decay = cfg.epsilon_decay
-        self.batch_size = cfg.batch_size
-        self.policy_net = CNN(screen_height, screen_width,
-                              action_dim).to(self.device)
-        self.target_net = CNN(screen_height, screen_width,
-                              action_dim).to(self.device)
-        self.target_net.load_state_dict(self.policy_net.state_dict()) # target_net的初始模型参数完全复制policy_net
-        self.target_net.eval()  # 不启用 BatchNormalization 和 Dropout
-        self.optimizer = optim.RMSprop(self.policy_net.parameters(),lr = cfg.lr) # 可查parameters()与state_dict()的区别，前者require_grad=True
-        self.loss = 0
-        self.memory = ReplayBuffer(cfg.memory_capacity)
-        
-
-    def choose_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():
-                q_value = self.policy_net(state) # q_value比如tensor([[-0.2522,  0.3887]])
-                # 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].view(1, 1)  # 注意这里action是个张量，如tensor([1])
-                return action
-        else:
-            return torch.tensor([[random.randrange(self.action_dim)]], device=self.device, dtype=torch.long)
-
-    def update(self):
-        if len(self.memory) < self.batch_size:
-            return
-        transitions = self.memory.sample(self.batch_size)
-        # Transpose the batch (see https://stackoverflow.com/a/19343/3343043 for
-        # detailed explanation). This converts batch-array of Transitions
-        # to Transition of batch-arrays.
-        batch = self.memory.Transition(*zip(*transitions))
-
-        # Compute a mask of non-final states and concatenate the batch elements
-        # (a final state would've been the one after which simulation ended)
-        non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,
-                                                batch.state_)), device=self.device, dtype=torch.bool)
-        
-        non_final_state_s = torch.cat([s for s in batch.state_
-                                           if s is not None])
-        state_batch = torch.cat(batch.state)   
-        action_batch = torch.cat(batch.action) 
-        reward_batch = torch.cat(batch.reward) # tensor([1., 1.,...,])
-        
-
-        # Compute Q(s_t, a) - the model computes Q(s_t), then we select the
-        # columns of actions taken. These are the actions which would've been taken
-        # for each batch state according to policy_net
-        state_action_values = self.policy_net(
-            state_batch).gather(1, action_batch) #tensor([[ 1.1217],...,[ 0.8314]])
-
-        # Compute V(s_{t+1}) for all next states.
-        # Expected values of actions for non_final_state_s are computed based
-        # on the "older" target_net; selecting their best reward with max(1)[0].
-        # This is merged based on the mask, such that we'll have either the expected
-        # state value or 0 in case the state was final.
-        state__values = torch.zeros(self.batch_size, device=self.device)
-
-        state__values[non_final_mask] = self.target_net(
-            non_final_state_s).max(1)[0].detach()
-
-        # Compute the expected Q values
-        expected_state_action_values = (state__values * self.gamma) + reward_batch # tensor([0.9685, 0.9683,...,])
-        
-        # Compute Huber loss
-        self.loss = F.smooth_l1_loss(
-            state_action_values, expected_state_action_values.unsqueeze(1))  # .unsqueeze增加一个维度
-        # Optimize the model
-        self.optimizer.zero_grad() # zero_grad clears old gradients from the last step (otherwise you’d just accumulate the gradients from all loss.backward() calls).
-        self.loss.backward() # loss.backward() computes the derivative of the loss w.r.t. the parameters (or anything requiring gradients) using backpropagation.
-        for param in self.policy_net.parameters(): # clip防止梯度爆炸
-            param.grad.data.clamp_(-1, 1)
-        self.optimizer.step() # causes the optimizer to take a step based on the gradients of the parameters.
-
-
-if __name__ == "__main__":
-    dqn = DQN()

codes/DQN_cnn/env.py

@@ -1,66 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-@Author: John
-@Email: johnjim0816@gmail.com
-@Date: 2020-06-11 10:02:35
-@LastEditor: John
-@LastEditTime: 2020-06-11 16:57:34
-@Discription: 
-@Environment: python 3.7.7
-'''
-
-import numpy as np
-import torch
-import torchvision.transforms as T
-from PIL import Image
-
-resize = T.Compose([T.ToPILImage(),
-                    T.Resize(40, interpolation=Image.CUBIC),
-                    T.ToTensor()])
-
-
-def get_cart_location(env,screen_width):
-    world_width = env.x_threshold * 2
-    scale = screen_width / world_width
-    return int(env.state[0] * scale + screen_width / 2.0)  # MIDDLE OF CART
-
-def get_screen(env,device):
-    # Returned screen requested by gym is 400x600x3, but is sometimes larger
-    # such as 800x1200x3. Transpose it into torch order (CHW).
-    screen = env.render(mode='rgb_array').transpose((2, 0, 1))
-    # Cart is in the lower half, so strip off the top and bottom of the screen
-    _, screen_height, screen_width = screen.shape
-    screen = screen[:, int(screen_height*0.4):int(screen_height * 0.8)]
-    view_width = int(screen_width * 0.6)
-    cart_location = get_cart_location(env,screen_width)
-    if cart_location < view_width // 2:
-        slice_range = slice(view_width)
-    elif cart_location > (screen_width - view_width // 2):
-        slice_range = slice(-view_width, None)
-    else:
-        slice_range = slice(cart_location - view_width // 2,
-                            cart_location + view_width // 2)
-    # Strip off the edges, so that we have a square image centered on a cart
-    screen = screen[:, :, slice_range]
-    # Convert to float, rescale, convert to torch tensor
-    # (this doesn't require a copy)
-    screen = np.ascontiguousarray(screen, dtype=np.float32) / 255
-    screen = torch.from_numpy(screen)
-    # Resize, and add a batch dimension (BCHW)
-    return resize(screen).unsqueeze(0).to(device)
-
-if __name__ == "__main__":
-
-    import gym
-    env = gym.make('CartPole-v0').unwrapped
-    # if gpu is to be used
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    env.reset()
-    import matplotlib.pyplot as plt
-    
-    plt.figure()
-    plt.imshow(get_screen(env,device).cpu().squeeze(0).permute(1, 2, 0).numpy(),
-            interpolation='none')
-    plt.title('Example extracted screen')
-    plt.show()

codes/DQN_cnn/main.py

@@ -1,112 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-@Author: John
-@Email: johnjim0816@gmail.com
-@Date: 2020-06-11 10:01:09
-@LastEditor: John
-LastEditTime: 2021-04-05 11:06:23
-@Discription: 
-@Environment: python 3.7.7
-'''
-import sys,os
-curr_path = os.path.dirname(__file__)
-parent_path=os.path.dirname(curr_path) 
-sys.path.append(parent_path) # add current terminal path to sys.path
-
-import gym
-import torch
-import datetime
-from DQN_cnn.env import get_screen
-from DQN_cnn.agent import DQNcnn
-from common.plot import plot_rewards
-from common.utils import save_results
-
-SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
-SAVED_MODEL_PATH = curr_path+"/saved_model/"+SEQUENCE+'/' # path to save model
-if not os.path.exists(curr_path+"/saved_model/"): 
-    os.mkdir(curr_path+"/saved_model/")
-if not os.path.exists(SAVED_MODEL_PATH): 
-    os.mkdir(SAVED_MODEL_PATH)
-RESULT_PATH = curr_path+"/results/"+SEQUENCE+'/' # path to save rewards
-if not os.path.exists(curr_path+"/results/"): 
-    os.mkdir(curr_path+"/results/")
-if not os.path.exists(RESULT_PATH): 
-    os.mkdir(RESULT_PATH)
-
-class DQNcnnConfig:
-    def __init__(self) -> None:
-        self.algo = "DQN_cnn"  # name of algo
-        self.gamma = 0.99
-        self.epsilon_start = 0.95  # e-greedy策略的初始epsilon
-        self.epsilon_end = 0.05
-        self.epsilon_decay = 200
-        self.lr = 0.01  # leanring rate
-        self.memory_capacity = 10000  # Replay Memory容量
-        self.batch_size = 64
-        self.train_eps = 250  # 训练的episode数目
-        self.train_steps = 200  # 训练每个episode的最大长度
-        self.target_update = 4  # target net的更新频率
-        self.eval_eps = 20  # 测试的episode数目
-        self.eval_steps = 200  # 测试每个episode的最大长度
-        self.hidden_dim = 128  # 神经网络隐藏层维度
-        self.device = torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu")  # if gpu is to be used
-
-def train(cfg, env, agent):
-    rewards = []
-    ma_rewards = []
-    for i_episode in range(cfg.train_eps):
-        # Initialize the environment and state
-        env.reset()
-        last_screen = get_screen(env, cfg.device)
-        current_screen = get_screen(env, cfg.device)
-        state = current_screen - last_screen
-        ep_reward = 0
-        for i_step in range(cfg.train_steps+1):
-            # Select and perform an action
-            action = agent.choose_action(state)
-            _, reward, done, _ = env.step(action.item())
-            ep_reward += reward
-            reward = torch.tensor([reward], device=cfg.device)
-            # Observe new state
-            last_screen = current_screen
-            current_screen = get_screen(env, cfg.device)
-            if done:
-                break
-            state_ = current_screen - last_screen
-            # Store the transition in memory
-            agent.memory.push(state, action, state_, reward)
-            # Move to the next state
-            state = state_
-            # Perform one step of the optimization (on the target network)
-            agent.update()
-        # Update the target network, copying all weights and biases in DQN
-        if i_episode % cfg.target_update == 0:
-            agent.target_net.load_state_dict(agent.policy_net.state_dict())
-        print('Episode:{}/{}, Reward:{}, Steps:{}, Explore:{:.2f}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step+1,agent.epsilon,done))
-        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)
-    return rewards,ma_rewards
-
-
-if __name__ == "__main__":
-    cfg = DQNcnnConfig()
-    # Get screen size so that we can initialize layers correctly based on shape
-    # returned from AI gym. Typical dimensions at this point are close to 3x40x90
-    # which is the result of a clamped and down-scaled render buffer in get_screen(env,device)
-    # 因为这里环境的state需要从默认的向量改为图像，所以要unwrapped更改state
-    env = gym.make('CartPole-v0').unwrapped
-    env.reset()
-    init_screen = get_screen(env, cfg.device)
-    _, _, screen_height, screen_width = init_screen.shape
-    # Get number of actions from gym action space
-    action_dim = env.action_space.n
-    agent = DQNcnn(screen_height, screen_width,
-                   action_dim, cfg)
-    rewards,ma_rewards = train(cfg,env,agent)
-    save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
-    plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)

codes/DQN_cnn/memory.py

@@ -1,35 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-@Author: John
-@Email: johnjim0816@gmail.com
-@Date: 2020-06-11 09:42:44
-@LastEditor: John
-LastEditTime: 2021-03-23 20:38:41
-@Discription: 
-@Environment: python 3.7.7
-'''
-from collections import namedtuple
-import random
-            
-class ReplayBuffer(object):
-
-    def __init__(self, capacity):
-        self.capacity = capacity
-        self.buffer = []
-        self.position = 0
-        self.Transition = namedtuple('Transition',
-                        ('state', 'action', 'state_', 'reward'))
-
-    def push(self, *args):
-        """Saves a transition."""
-        if len(self.buffer) < self.capacity:
-            self.buffer.append(None)
-        self.buffer[self.position] = self.Transition(*args)
-        self.position = (self.position + 1) % self.capacity
-
-    def sample(self, batch_size):
-        return random.sample(self.buffer, batch_size)
-
-    def __len__(self):
-        return len(self.buffer)

codes/DQN_cnn/model.py

@@ -1,41 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-@Author: John
-@Email: johnjim0816@gmail.com
-@Date: 2020-06-11 12:18:12
-@LastEditor: John
-@LastEditTime: 2020-06-11 17:23:45
-@Discription: 
-@Environment: python 3.7.7
-'''
-import torch.nn as nn
-import torch.nn.functional as F
-
-class CNN(nn.Module):
-
-    def __init__(self, h, w, n_outputs):
-        super(CNN, self).__init__()
-        self.conv1 = nn.Conv2d(3, 16, kernel_size=5, stride=2)
-        self.bn1 = nn.BatchNorm2d(16)
-        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=2)
-        self.bn2 = nn.BatchNorm2d(32)
-        self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2)
-        self.bn3 = nn.BatchNorm2d(32)
-
-        # Number of Linear input connections depends on output of conv2d layers
-        # and therefore the input image size, so compute it.
-        def conv2d_size_out(size, kernel_size = 5, stride = 2):
-            return (size - (kernel_size - 1) - 1) // stride  + 1
-        convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(w)))
-        convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(h)))
-        linear_input_size = convw * convh * 32
-        self.head = nn.Linear(linear_input_size, n_outputs)
-
-    # Called with either one element to determine next action, or a batch
-    # during optimization. Returns tensor([[left0exp,right0exp]...]).
-    def forward(self, x):
-        x = F.relu(self.bn1(self.conv1(x)))
-        x = F.relu(self.bn2(self.conv2(x)))
-        x = F.relu(self.bn3(self.conv3(x)))
-        return self.head(x.view(x.size(0), -1))

codes/DoubleDQN/agent.py

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:50:49
 @LastEditor: John
-LastEditTime: 2021-03-28 11:07:35
+LastEditTime: 2021-05-04 15:04:45
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -42,14 +42,7 @@ class DoubleDQN:
         self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
         self.loss = 0
         self.memory = ReplayBuffer(cfg.memory_capacity)
-
+    def predict(self,state):
-    def choose_action(self, state):
-        '''选择动作
-        '''
-        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
@@ -61,6 +54,15 @@ class DoubleDQN:
                 # 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()  
+        return action
+    def choose_action(self, state):
+        '''选择动作
+        '''
+        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:
+            action  = self.predict(state)
         else:
             action = random.randrange(self.action_dim)
         return action
@@ -113,7 +115,9 @@ class DoubleDQN:
         self.optimizer.step()  # 更新模型
 
     def save(self,path):
-        torch.save(self.target_net.state_dict(), path+'DoubleDQN_checkpoint.pth')
+        torch.save(self.target_net.state_dict(), path+'checkpoint.pth')
 
     def load(self,path):
-        self.target_net.load_state_dict(torch.load(path+'DoubleDQN_checkpoint.pth'))    
+        self.target_net.load_state_dict(torch.load(path+'checkpoint.pth'))  
+        for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
+            param.data.copy_(target_param.data)  

codes/DoubleDQN/main.py

@@ -1,93 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-@Author: John
-@Email: johnjim0816@gmail.com
-@Date: 2020-06-12 00:48:57
-@LastEditor: John
-LastEditTime: 2021-03-28 11:05:14
-@Discription: 
-@Environment: python 3.7.7
-'''
-import sys,os
-sys.path.append(os.getcwd()) # add current terminal path
-import gym
-import torch
-import datetime
-from DoubleDQN.agent import DoubleDQN
-from common.plot import plot_rewards
-from common.utils import save_results
-
-SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
-SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径
-if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): 
-    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
-if not os.path.exists(SAVED_MODEL_PATH): 
-    os.mkdir(SAVED_MODEL_PATH)
-RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径
-if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): 
-    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
-if not os.path.exists(RESULT_PATH): 
-    os.mkdir(RESULT_PATH)
-
-class DoubleDQNConfig:
-    def __init__(self):
-        self.algo = "Double DQN" # name of algo
-        self.gamma = 0.99
-        self.epsilon_start = 0.9 # e-greedy策略的初始epsilon
-        self.epsilon_end = 0.01
-        self.epsilon_decay = 200
-        self.lr = 0.01 # 学习率
-        self.memory_capacity = 10000 # Replay Memory容量
-        self.batch_size = 128
-        self.train_eps = 300 # 训练的episode数目
-        self.train_steps = 200 # 训练每个episode的最大长度
-        self.target_update = 2 # target net的更新频率
-        self.eval_eps = 20 # 测试的episode数目
-        self.eval_steps = 200 # 测试每个episode的最大长度
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
-        self.hidden_dim = 128 # 神经网络隐藏层维度
- 
-
-def train(cfg,env,agent):
-    print('Start to train !')
-    rewards,ma_rewards = [],[]
-    ep_steps = []
-    for i_episode in range(cfg.train_eps):
-        state = env.reset() # reset环境状态
-        ep_reward = 0
-        for i_step in range(cfg.train_steps):
-            action = agent.choose_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:{}/{}, Reward:{}, Steps:{}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step,done))
-        ep_steps.append(i_step)
-        rewards.append(ep_reward)
-        # 计算滑动窗口的reward
-        if ma_rewards:
-            ma_rewards.append(
-                0.9*ma_rewards[-1]+0.1*ep_reward)
-        else:
-            ma_rewards.append(ep_reward)   
-    print('Complete training！')
-    return rewards,ma_rewards
-    
-if __name__ == "__main__":
-    cfg = DoubleDQNConfig()
-    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
-    state_dim = env.observation_space.shape[0]
-    action_dim = env.action_space.n
-    agent = DoubleDQN(state_dim,action_dim,cfg)
-    rewards,ma_rewards = train(cfg,env,agent)
-    agent.save(path=SAVED_MODEL_PATH)
-    save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
-    plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)

codes/DoubleDQN/task0_train.py

@@ -0,0 +1,119 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-12 00:48:57
+@LastEditor: John
+LastEditTime: 2021-05-04 15:05:37
+@Discription: 
+@Environment: python 3.7.7
+'''
+import sys,os
+curr_path = os.path.dirname(__file__)
+parent_path = os.path.dirname(curr_path)
+sys.path.append(parent_path)  # add current terminal path to sys.path
+
+import gym
+import torch
+import datetime
+from DoubleDQN.agent import DoubleDQN
+from common.plot import plot_rewards
+from common.utils import save_results, make_dir
+
+curr_time = datetime.datetime.now().strftime(
+    "%Y%m%d-%H%M%S")  # obtain current time
+
+class DoubleDQNConfig:
+    def __init__(self):
+        self.algo = "DoubleDQN" # name of algo
+        self.env = 'CartPole-v0'  # env name
+        self.result_path = curr_path+"/outputs/" + self.env + \
+            '/'+curr_time+'/results/'  # path to save results
+        self.model_path = curr_path+"/outputs/" + self.env + \
+            '/'+curr_time+'/models/'  # path to save results
+        self.gamma = 0.99 
+        self.epsilon_start = 0.9 # start epsilon of e-greedy policy
+        self.epsilon_end = 0.01 
+        self.epsilon_decay = 200
+        self.lr = 0.01 # learning rate
+        self.memory_capacity = 10000 # capacity of Replay Memory
+        self.batch_size = 128
+        self.train_eps = 300 # max tranng episodes
+        self.train_steps = 200 # max training steps per episode
+        self.target_update = 2 # update frequency of target net
+        self.eval_eps = 50 # max evaling episodes
+        self.eval_steps = 200 # max evaling steps per episode
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check gpu
+        self.hidden_dim = 128 # hidden size of net
+ 
+def env_agent_config(cfg,seed=1):
+    env = gym.make(cfg.env)  
+    env.seed(seed)
+    state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.n
+    agent = DoubleDQN(state_dim,action_dim,cfg)
+    return env,agent
+    
+def train(cfg,env,agent):
+    print('Start to train !')
+    rewards,ma_rewards = [],[]
+    for i_ep in range(cfg.train_eps):
+        state = env.reset() # reset环境状态
+        ep_reward = 0
+        while True:
+            action = agent.choose_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
+        if i_ep % cfg.target_update == 0:
+            agent.target_net.load_state_dict(agent.policy_net.state_dict())
+        print(f'Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward}')
+        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('Complete training！')
+    return rewards,ma_rewards
+
+def eval(cfg,env,agent):
+    rewards = []  
+    ma_rewards = []
+    for i_ep in range(cfg.eval_eps):
+        state = env.reset() 
+        ep_reward = 0   
+        while True:
+            action = agent.predict(state)  
+            next_state, reward, done, _ = env.step(action)  
+            state = next_state  
+            ep_reward += reward
+            if done:
+                break
+        rewards.append(ep_reward)
+        if ma_rewards:
+            ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
+        else:
+            ma_rewards.append(ep_reward)
+        print(f"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}")
+    return rewards,ma_rewards    
+if __name__ == "__main__":
+    cfg = DoubleDQNConfig()
+    env,agent = env_agent_config(cfg,seed=1)
+    rewards, ma_rewards = train(cfg, env, agent)
+    make_dir(cfg.result_path, cfg.model_path)
+    agent.save(path=cfg.model_path)
+    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
+    plot_rewards(rewards, ma_rewards, tag="train",
+                 algo=cfg.algo, path=cfg.result_path)
+
+    env,agent = env_agent_config(cfg,seed=10)
+    agent.load(path=cfg.model_path)
+    rewards,ma_rewards = eval(cfg,env,agent)
+    save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
+    plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)

codes/DoubleDQN/utils.py

@@ -1,21 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2020-10-15 21:28:00
-LastEditor: John
-LastEditTime: 2020-10-15 21:50:30
-Discription: 
-Environment: 
-'''
-import os
-import numpy as np
-
-
-def save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path='./results'):
-    if not os.path.exists(result_path): # 检测是否存在文件夹
-        os.mkdir(result_path)
-    np.save(result_path+'rewards_'+tag+'.npy', rewards)
-    np.save(result_path+'moving_average_rewards_'+tag+'.npy', moving_average_rewards)
-    np.save(result_path+'steps_'+tag+'.npy',ep_steps )

codes/README_en.md

@@ -21,9 +21,6 @@ Note that ```model.py```,```memory.py```,```plot.py``` shall be utilized in diff
 
 python 3.7、pytorch 1.6.0-1.7.1、gym 0.17.0-0.18.0
 ## Usage
-运行带有```train```的py文件或ipynb文件进行训练，如果前面带有```task```如```task0_train.py```，表示对task0任务训练
-类似的带有```eval```即为测试。
-
 run python scripts or jupyter notebook file with ```train``` to train the agent, if there is a ```task``` like ```task0_train.py```, it means to train with task 0.
 
 similar to file with ```eval```, which means to evaluate the agent.
@@ -36,7 +33,7 @@ similar to file with ```eval```, which means to evaluate the agent.
 |        [Q-Learning](./QLearning)         | [towardsdatascience blog](https://towardsdatascience.com/simple-reinforcement-learning-q-learning-fcddc4b6fe56),[q learning paper](https://ieeexplore.ieee.org/document/8836506) | [CliffWalking-v0](./envs/gym_info.md)     |                                    |
 |             [Sarsa](./Sarsa)             | [geeksforgeeks blog](https://www.geeksforgeeks.org/sarsa-reinforcement-learning/) | [Racetrack](./envs/racetrack_env.md)      |                                    |
 |               [DQN](./DQN)               | [DQN Paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf),[Nature DQN Paper](https://www.nature.com/articles/nature14236) | [CartPole-v0](./envs/gym_info.md)         |                                    |
-|           [DQN-cnn](./DQN_cnn)           | [DQN Paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)  | [CartPole-v0](./envs/gym_info.md)         | 与DQN相比使用了CNN而不是全链接网络 |
+|           [DQN-cnn](./DQN_cnn)           | [DQN Paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)  | [CartPole-v0](./envs/gym_info.md)         |  |
 |         [DoubleDQN](./DoubleDQN)         |     [DoubleDQN Paper](https://arxiv.org/abs/1509.06461)      | [CartPole-v0](./envs/gym_info.md)         |                                    |
 |   [Hierarchical DQN](HierarchicalDQN)    |       [H-DQN Paper](https://arxiv.org/abs/1604.06057)        | [CartPole-v0](./envs/gym_info.md)         |                                    |
 |    [PolicyGradient](./PolicyGradient)    | [Lil'log](https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html) | [CartPole-v0](./envs/gym_info.md)         |                                    |

codes/common/model.py

@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2021-03-12 21:14:12
 LastEditor: John
-LastEditTime: 2021-03-31 13:49:06
+LastEditTime: 2021-05-04 02:45:27
 Discription: 
 Environment: 
 '''
@@ -63,7 +63,7 @@ class Actor(nn.Module):
     def forward(self, x):
         x = F.relu(self.linear1(x))
         x = F.relu(self.linear2(x))
-        x = F.tanh(self.linear3(x))
+        x = torch.tanh(self.linear3(x))
         return x
 
 class ActorCritic(nn.Module):

codes/test.py

@@ -1,20 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: JiangJi
-Email: johnjim0816@gmail.com
-Date: 2021-03-25 23:25:15
-LastEditor: JiangJi
-LastEditTime: 2021-04-28 21:36:50
-Discription: 
-Environment: 
-'''
-import random
-dic = {0:"鳗鱼家",1:"一心",2:"bada"}
-print("0:鳗鱼家，1:一心，2:bada")
-print("三次随机，取最后一次选择")
-for i in range(3):
-    if i ==2:
-        print(f"去{dic[random.randint(0,2)]}")
-    else:
-        print(f"不去{dic[random.randint(0,2)]}")