update Qlearning

codes/QLearning/agent.py

@@ -5,11 +5,10 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2020-09-11 23:03:00
 LastEditor: John
-LastEditTime: 2021-03-11 19:16:27
+LastEditTime: 2021-03-12 16:48:25
 Discription: 
 Environment: 
 '''
-from functools import update_wrapper
 import numpy as np
 import math
 import torch
@@ -53,11 +52,11 @@ class QLearning(object):
         import dill
         torch.save(
             obj=self.Q_table,
-            f=path,
+            f=path+"Qleaning_model.pkl",
             pickle_module=dill
         )
-
     def load(self, path):
         '''从文件中读取数据到 Q表格
         '''
-        self.Q_table =torch.load(f='prod_dls.pkl',pickle_module=dill)
+        import dill
+        self.Q_table =torch.load(f=path+'Qleaning_model.pkl',pickle_module=dill)

codes/QLearning/main.py

@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2020-09-11 23:03:00
 LastEditor: John
-LastEditTime: 2021-03-11 19:22:50
+LastEditTime: 2021-03-12 16:52:26
 Discription: 
 Environment: 
 '''
@@ -15,101 +15,101 @@ sys.path.append(os.getcwd()) # 添加当前终端路径
 import argparse
 import gym
 import datetime
-from QLearning.plot import plot
-from QLearning.utils import save_results
+
 from envs.gridworld_env import CliffWalkingWapper, FrozenLakeWapper
 from QLearning.agent import QLearning
+from common.plot import plot_rewards
+from common.utils import save_results
 
-SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/'
-RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/result/"+SEQUENCE+'/'
+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/"): # 检测是否存在文件夹
+    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
+if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
+    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/"): # 检测是否存在文件夹
+    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
+if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
+    os.mkdir(RESULT_PATH)
 
-def get_args():
-    '''训练的模型参数
-    '''
-    parser = argparse.ArgumentParser()
+class QlearningConfig:
     '''训练相关参数'''
-    parser.add_argument("--n_episodes", default=500,
-                        type=int, help="训练的最大episode数目")       
-    '''算法相关参数'''
-    parser.add_argument("--gamma", default=0.9,
-                        type=float, help="reward的衰减率")
-    parser.add_argument("--epsilon_start", default=0.99,
-                        type=float, help="e-greedy策略中初始epsilon")
-    parser.add_argument("--epsilon_end", default=0.01,
-                        type=float, help="e-greedy策略中的结束epsilon")
-    parser.add_argument("--epsilon_decay", default=200,
-                        type=float, help="e-greedy策略中epsilon的衰减率")
-    parser.add_argument("--lr", default=0.1, type=float, help="学习率")
-    config = parser.parse_args()
-    return config
+    def __init__(self):
+        self.n_episodes = 200 # 训练的episode数目
+        self.gamma = 0.9 # reward的衰减率
+        self.epsilon_start = 0.99 # 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 # 学习率
+
 def train(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,
+    rewards = []  # 记录所有episode的reward
+    ma_rewards = [] # 滑动平均的reward
     steps = []  # 记录所有episode的steps
     for i_episode in range(cfg.n_episodes):
         ep_reward = 0  # 记录每个episode的reward
         ep_steps = 0  # 记录每个episode走了多少step
-        obs = 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episode）
         while True:
-            action = agent.choose_action(obs)  # 根据算法选择一个动作
-            next_obs, reward, done, _ = env.step(action)  # 与环境进行一个交互
-            # 训练 Q-learning算法
-            agent.update(obs, action, reward, next_obs, done)  # 不需要下一步的action
-            obs = next_obs  # 存储上一个观察值
+            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
             ep_reward += reward
             ep_steps += 1  # 计算step数
             if done:
                 break
         steps.append(ep_steps)
+        rewards.append(ep_reward)
         # 计算滑动平均的reward
-        if rewards:
-            rewards.append(rewards[-1]*0.9+ep_reward*0.1)
+        if ma_rewards:
+            ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
         else:
-            rewards.append(ep_reward)
+            ma_rewards.append(ep_reward)
         print("Episode:{}/{}: reward:{:.1f}".format(i_episode+1, cfg.n_episodes,ep_reward))
-    plot(rewards)
-    if not os.path.exists(SAVED_MODEL_PATH):
-        os.mkdir(SAVED_MODEL_PATH)
-    agent.save(SAVED_MODEL_PATH+'Q_table.pkl')  # 训练结束，保存模型
-    '''存储reward等相关结果'''
-    save_results(rewards,tag='train',result_path=RESULT_PATH)
+    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)
-    rewards = []  # 记录所有episode的reward,
+    rewards = []  # 记录所有episode的reward
+    ma_rewards = [] # 滑动平均的reward
     steps = []  # 记录所有episode的steps
-    for i_episode in range(20):
+    for i_episode in range(cfg.n_episodes):
         ep_reward = 0  # 记录每个episode的reward
         ep_steps = 0  # 记录每个episode走了多少step
-        obs = 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episode）
         while True:
-            action = agent.choose_action(obs)  # 根据算法选择一个动作
-            next_obs, reward, done, _ = env.step(action)  # 与环境进行一个交互
-            obs = next_obs  # 存储上一个观察值
+            action = agent.choose_action(state)  # 根据算法选择一个动作
+            next_state, reward, done, _ = env.step(action)  # 与环境进行一个交互
+            state = next_state  # 存储上一个观察值
             ep_reward += reward
             ep_steps += 1  # 计算step数
             if done:
                 break
         steps.append(ep_steps)
+        rewards.append(ep_reward)
         # 计算滑动平均的reward
-        if rewards:
-            rewards.append(rewards[-1]*0.9+ep_reward*0.1)
+        if ma_rewards:
+            ma_rewards.append(rewards[-1]*0.9+ep_reward*0.1)
         else:
-            rewards.append(ep_reward)
+            ma_rewards.append(ep_reward)
         print("Episode:{}/{}: reward:{:.1f}".format(i_episode+1, cfg.n_episodes,ep_reward))
-    plot(rewards)
-    '''存储reward等相关结果'''
-    save_results(rewards,tag='eval',result_path=RESULT_PATH)
+    return rewards,ma_rewards
     
 if __name__ == "__main__":
-    cfg = get_args()
+    cfg = QlearningConfig()
     env = gym.make("CliffWalking-v0")  # 0 up, 1 right, 2 down, 3 left
     env = CliffWalkingWapper(env)
     n_actions = env.action_space.n
     agent = QLearning(n_actions,cfg)
-    train(cfg,env,agent)
-    eval(cfg,env,agent)
+    rewards,ma_rewards = train(cfg,env,agent)
+    agent.save(path=SAVED_MODEL_PATH)
+    # eval(cfg,env,agent)
+    save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
+    plot_rewards(rewards,ma_rewards,tag="train",algo = "On-Policy First-Visit MC Control",path=RESULT_PATH)
+    
     

codes/QLearning/plot.py

@@ -1,35 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2020-10-07 20:57:11
-LastEditor: John
-LastEditTime: 2020-10-07 21:00:29
-Discription: 
-Environment: 
-'''
-import matplotlib.pyplot as plt
-import seaborn as sns
-import numpy as np
-import os 
-
-def plot(item,ylabel='rewards'):
-    sns.set()
-    plt.figure()
-    plt.plot(np.arange(len(item)), item)
-    plt.title(ylabel+' of Q-learning') 
-    plt.ylabel(ylabel)
-    plt.xlabel('episodes')
-    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_train.npy", )
-    MA_rewards=np.load(output_path+"MA_rewards_train.npy")
-    steps = np.load(output_path+"steps_train.npy")
-    plot(rewards)
-    plot(MA_rewards,ylabel='moving_average_rewards')
-    plot(steps,ylabel='steps')

codes/QLearning/utils.py

@@ -1,22 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2020-11-23 13:44:52
-LastEditor: John
-LastEditTime: 2021-03-11 19:18:34
-Discription: 
-Environment: 
-'''
-import os
-import numpy as np
-
-
-def save_results(rewards,tag='train',result_path='./result'):
-    '''保存reward等结果
-    '''
-    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)
-    print('results saved!')