update

codes/QLearning/task0_eval.py

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+#!/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)
+    
+