update

codes/QLearning/README.md

@@ -1,3 +0,0 @@
-# Q-learning
-
-#TODO

codes/QLearning/agent.py

@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2020-09-11 23:03:00
 LastEditor: John
-LastEditTime: 2021-04-29 16:59:41
+LastEditTime: 2021-09-11 21:53:18
 Discription: use defaultdict to define Q table
 Environment: 
 '''
@@ -30,20 +30,20 @@ class QLearning(object):
     def choose_action(self, state):
         self.sample_count += 1
         self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
-            math.exp(-1. * self.sample_count / self.epsilon_decay)
-        # e-greedy policy
+            math.exp(-1. * self.sample_count / self.epsilon_decay) # epsilon是会递减的，这里选择指数递减
+        # e-greedy 策略
         if np.random.uniform(0, 1) > self.epsilon:
-            action = self.predict(state)
+            action = np.argmax(self.Q_table[str(state)]) # 选择Q(s,a)最大对应的动作
         else:
-            action = np.random.choice(self.action_dim) 
+            action = np.random.choice(self.action_dim) # 随机选择动作
         return action
     def predict(self,state):
         action = np.argmax(self.Q_table[str(state)])
         return action
     def update(self, state, action, reward, next_state, done):
-        Q_predict = self.Q_table[str(state)][action]
-        if done:
-            Q_target = reward  # terminal state
+        Q_predict = self.Q_table[str(state)][action] 
+        if done: # 终止状态
+            Q_target = reward  
         else:
             Q_target = reward + self.gamma * np.max(self.Q_table[str(next_state)]) 
         self.Q_table[str(state)][action] += self.lr * (Q_target - Q_predict)
@@ -54,6 +54,8 @@ class QLearning(object):
             f=path+"Qleaning_model.pkl",
             pickle_module=dill
         )
+        print("保存模型成功！")
     def load(self, path):
         import dill
-        self.Q_table =torch.load(f=path+'Qleaning_model.pkl',pickle_module=dill)
+        self.Q_table =torch.load(f=path+'Qleaning_model.pkl',pickle_module=dill)
+        print("加载模型成功！")

codes/QLearning/agent1.py

@@ -1,88 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2020-09-11 23:03:00
-LastEditor: John
-LastEditTime: 2021-04-29 17:02:00
-Discription: 
-Environment: 
-'''
-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import numpy as np
-import math
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2020-09-11 23:03:00
-LastEditor: John
-LastEditTime: 2021-04-29 16:45:33
-Discription: use np array to define Q table
-Environment: 
-'''
-import numpy as np
-import math
-
-class QLearning(object):
-    def __init__(self,
-                 state_dim,action_dim,cfg):
-        self.action_dim = action_dim  # dimension of acgtion
-        self.lr = cfg.lr  # learning rate
-        self.gamma = cfg.gamma  
-        self.epsilon = 0 
-        self.sample_count = 0  
-        self.epsilon_start = cfg.epsilon_start
-        self.epsilon_end = cfg.epsilon_end
-        self.epsilon_decay = cfg.epsilon_decay
-        self.Q_table = np.zeros((state_dim, action_dim)) # Q表
-        
-    def choose_action(self, state):
-        self.sample_count += 1
-        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
-            math.exp(-1. * self.sample_count / self.epsilon_decay)
-        if np.random.uniform(0, 1) > self.epsilon:  # 随机选取0-1之间的值，如果大于epsilon就按照贪心策略选取action，否则随机选取
-            action = self.predict(state)
-        else:
-            action = np.random.choice(self.action_dim)  #有一定概率随机探索选取一个动作
-        return action
-
-    def predict(self, state):
-        '''根据输入观测值，采样输出的动作值，带探索，测试模型时使用
-        '''
-        Q_list = self.Q_table[state, :]
-        Q_max = np.max(Q_list)
-        action_list = np.where(Q_list == Q_max)[0]  
-        action = np.random.choice(action_list) # Q_max可能对应多个 action ，可以随机抽取一个
-        return action
-            
-    def update(self, state, action, reward, next_state, done):
-        Q_predict = self.Q_table[state, action]
-        if done:
-            Q_target = reward  # 没有下一个状态了
-        else:
-            Q_target = reward + self.gamma * np.max(
-                self.Q_table[next_state, :])  # Q_table-learning
-        self.Q_table[state, action] += self.lr * (Q_target - Q_predict)  # 修正q
-    def save(self,path):
-        np.save(path+"Q_table.npy", self.Q_table)
-    def load(self, path):
-        self.Q_table = np.load(path+"Q_table.npy")
-
-

codes/QLearning/task0_eval.py

@@ -1,84 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2020-09-11 23:03:00
-LastEditor: John
-LastEditTime: 2021-04-29 17:01:43
-Discription: 
-Environment: 
-'''
-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 datetime
-
-from envs.gridworld_env import CliffWalkingWapper
-from QLearning.agent import QLearning
-from common.plot import plot_rewards
-from common.utils import save_results
-
-curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
-
-class QlearningConfig:
-    '''训练相关参数'''
-    def __init__(self):
-        self.algo = 'Qlearning'
-        self.env = 'CliffWalking-v0' # 0 up, 1 right, 2 down, 3 left
-        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 models
-        self.train_eps = 300 # 训练的episode数目
-        self.eval_eps = 30
-        self.gamma = 0.9 # reward的衰减率
-        self.epsilon_start = 0.95 # e-greedy策略中初始epsilon
-        self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
-        self.epsilon_decay = 200 # e-greedy策略中epsilon的衰减率
-        self.lr = 0.1 # learning rate
-
-def env_agent_config(cfg,seed=1):
-    env = gym.make(cfg.env)  
-    env = CliffWalkingWapper(env)
-    env.seed(seed)
-    state_dim = env.observation_space.n
-    action_dim = env.action_space.n
-    agent = QLearning(state_dim,action_dim,cfg)
-    return env,agent
-    
-def eval(cfg,env,agent):
-    # env = gym.make("FrozenLake-v0", is_slippery=False)  # 0 left, 1 down, 2 right, 3 up
-    # env = FrozenLakeWapper(env)
-    rewards = []  # 记录所有episode的reward
-    ma_rewards = [] # 滑动平均的reward
-    for i_ep in range(cfg.eval_eps):
-        ep_reward = 0  # 记录每个episode的reward
-        state = env.reset()  # 重置环境, 重新开一局（即开始新的一个episode）
-        while True:
-            action = agent.predict(state)  # 根据算法选择一个动作
-            next_state, reward, done, _ = env.step(action)  # 与环境进行一个交互
-            state = next_state  # 存储上一个观察值
-            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 = QlearningConfig()
-    env,agent = env_agent_config(cfg,seed=15)
-    cfg.model_path = './'+'QLearning/outputs/CliffWalking-v0/20210429-165825/models'+'/'
-    cfg.result_path = './'+'QLearning/outputs/CliffWalking-v0/20210429-165825/results'+'/'
-    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/QLearning/task0_train.py

@@ -5,14 +5,14 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2020-09-11 23:03:00
 LastEditor: John
-LastEditTime: 2021-05-06 17:04:38
+LastEditTime: 2021-09-12 01:29:40
 Discription: 
 Environment: 
 '''
 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
+curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前路径
+parent_path=os.path.dirname(curr_path) # 父路径，这里就是我们的项目路径
+sys.path.append(parent_path) # 由于需要引用项目路径下的其他模块比如envs，所以需要添加路径到sys.path
 
 import gym
 import torch
@@ -20,49 +20,49 @@ import datetime
 
 from envs.gridworld_env import CliffWalkingWapper
 from QLearning.agent import QLearning
-from common.plot import plot_rewards
+from common.plot import plot_rewards,plot_rewards_cn
 from common.utils import save_results,make_dir
-curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
 
+curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
 class QlearningConfig:
     '''训练相关参数'''
     def __init__(self):
-        self.algo = 'Qlearning'
-        self.env = 'CliffWalking-v0' # 0 up, 1 right, 2 down, 3 left
-        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 models
-        self.train_eps = 300 # 训练的episode数目
-        self.eval_eps = 30
+        self.algo = 'Q-learning' # 算法名称
+        self.env = 'CliffWalking-v0' # 环境名称
+        self.result_path = curr_path+"/outputs/" +self.env+'/'+curr_time+'/results/'  # 保存结果的路径
+        self.model_path = curr_path+"/outputs/" +self.env+'/'+curr_time+'/models/'  # 保存模型的路径
+        self.train_eps = 200 # 训练的回合数
+        self.eval_eps = 30 # 测试的回合数
         self.gamma = 0.9 # reward的衰减率
-        self.epsilon_start = 0.95 # e-greedy策略中初始epsilon
+        self.epsilon_start = 0.90 # e-greedy策略中初始epsilon
         self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
         self.epsilon_decay = 200 # e-greedy策略中epsilon的衰减率
-        self.lr = 0.1 # learning rate
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check gpu
+        self.lr = 0.05 # 学习率
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
 
         
 def env_agent_config(cfg,seed=1):
     env = gym.make(cfg.env)  
     env = CliffWalkingWapper(env)
-    env.seed(seed)
-    state_dim = env.observation_space.n
-    action_dim = env.action_space.n
-    agent = QLearning(state_dim,action_dim,cfg)
+    env.seed(seed) # 设置随机种子
+    n_states = env.observation_space.n # 状态维度
+    n_actions = env.action_space.n # 动作维度
+    agent = QLearning(n_states,n_actions,cfg)
     return env,agent
 
 def train(cfg,env,agent):
-    print('Start to train !')
-    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
+    print('开始训练！')
+    print(f'环境:{cfg.env}, 算法:{cfg.algo}, 设备:{cfg.device}')
     rewards = []  
-    ma_rewards = [] # moving average reward
+    ma_rewards = [] # 滑动平均奖励
     for i_ep in range(cfg.train_eps):
-        ep_reward = 0  # 记录每个episode的reward
+        ep_reward = 0  # 记录每个回合的奖励
         state = env.reset()  # 重置环境, 重新开一局（即开始新的一个episode）
         while True:
             action = agent.choose_action(state)  # 根据算法选择一个动作
             next_state, reward, done, _ = env.step(action)  # 与环境进行一次动作交互
             agent.update(state, action, reward, next_state, done)  # Q-learning算法更新
-            state = next_state  # 存储上一个观察值
+            state = next_state  # 更新状态
             ep_reward += reward
             if done:
                 break
@@ -71,20 +71,18 @@ def train(cfg,env,agent):
             ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
         else:
             ma_rewards.append(ep_reward)
-        print("Episode:{}/{}: reward:{:.1f}".format(i_ep+1, cfg.train_eps,ep_reward))
-    print('Complete training！')
+        print("回合数：{}/{}，奖励{:.1f}".format(i_ep+1, cfg.train_eps,ep_reward))
+    print('完成训练！')
     return rewards,ma_rewards
     
 def eval(cfg,env,agent):
-    # env = gym.make("FrozenLake-v0", is_slippery=False)  # 0 left, 1 down, 2 right, 3 up
-    # env = FrozenLakeWapper(env)
-    print('Start to eval !')
-    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
-    rewards = []  # 记录所有episode的reward
-    ma_rewards = [] # 滑动平均的reward
+    print('开始测试！')
+    print(f'环境：{cfg.env}, 算法：{cfg.algo}, 设备：{cfg.device}')
+    rewards = []  # 记录所有回合的奖励
+    ma_rewards = [] # 滑动平均的奖励
     for i_ep in range(cfg.eval_eps):
         ep_reward = 0  # 记录每个episode的reward
-        state = env.reset()  # 重置环境, 重新开一局（即开始新的一个episode）
+        state = env.reset()  # 重置环境, 重新开一局（即开始新的一个回合）
         while True:
             action = agent.predict(state)  # 根据算法选择一个动作
             next_state, reward, done, _ = env.step(action)  # 与环境进行一个交互
@@ -97,23 +95,26 @@ def eval(cfg,env,agent):
             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}")
-    print('Complete evaling！')
+        print(f"回合数：{i_ep+1}/{cfg.eval_eps}, 奖励：{ep_reward:.1f}")
+    print('完成测试！')
     return rewards,ma_rewards
     
 if __name__ == "__main__":
     cfg = QlearningConfig()
+
+    # 训练
     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",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
+    make_dir(cfg.result_path,cfg.model_path) # 创建文件夹
+    agent.save(path=cfg.model_path) # 保存模型
+    save_results(rewards,ma_rewards,tag='train',path=cfg.result_path) # 保存结果
+    plot_rewards_cn(rewards,ma_rewards,tag="train",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
 
-    env,agent = env_agent_config(cfg,seed=10)
-    agent.load(path=cfg.model_path)
+    # # 测试
+    # env,agent = env_agent_config(cfg,seed=10)
+    # agent.load(path=cfg.model_path) # 加载模型
     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)
+    plot_rewards_cn(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)