update

codes/QLearning/task0_train.py

@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2020-09-11 23:03:00
 LastEditor: John
-LastEditTime: 2021-04-29 17:01:08
+LastEditTime: 2021-05-06 17:04:38
 Discription: 
 Environment: 
 '''
@@ -15,6 +15,7 @@ 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 envs.gridworld_env import CliffWalkingWapper
@@ -37,6 +38,8 @@ class QlearningConfig:
         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
+
         
 def env_agent_config(cfg,seed=1):
     env = gym.make(cfg.env)  
@@ -48,6 +51,8 @@ def env_agent_config(cfg,seed=1):
     return env,agent
 
 def train(cfg,env,agent):
+    print('Start to train !')
+    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
     rewards = []  
     ma_rewards = [] # moving average reward
     for i_ep in range(cfg.train_eps):
@@ -67,11 +72,14 @@ def train(cfg,env,agent):
         else:
             ma_rewards.append(ep_reward)
         print("Episode:{}/{}: reward:{:.1f}".format(i_ep+1, cfg.train_eps,ep_reward))
+    print('Complete training！')
     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
     for i_ep in range(cfg.eval_eps):
@@ -90,6 +98,7 @@ def eval(cfg,env,agent):
         else:
             ma_rewards.append(ep_reward)
         print(f"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}")
+    print('Complete evaling！')
     return rewards,ma_rewards
     
 if __name__ == "__main__":

codes/SAC/task0_train.py

@@ -5,12 +5,10 @@ Author: JiangJi
 Email: johnjim0816@gmail.com
 Date: 2021-04-29 12:59:22
 LastEditor: JiangJi
-LastEditTime: 2021-05-06 01:47:36
+LastEditTime: 2021-05-06 16:58:01
 Discription: 
 Environment: 
 '''
-
-
 import sys,os
 curr_path = os.path.dirname(__file__)
 parent_path = os.path.dirname(curr_path)

codes/Sarsa/main.py

@@ -1,80 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2021-03-11 17:59:16
-LastEditor: John
-LastEditTime: 2021-03-12 17:01:43
-Discription: 
-Environment: 
-'''
-import sys,os
-sys.path.append(os.getcwd())
-import datetime
-from envs.racetrack_env import RacetrackEnv
-from Sarsa.agent import Sarsa
-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/"): # 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)
-
-class SarsaConfig:
-    ''' parameters for Sarsa
-    '''
-    def __init__(self):
-       self.epsilon = 0.15 # epsilon: The probability to select a random action . 
-       self.gamma = 0.9 # gamma: Gamma discount factor.
-       self.lr = 0.2 # learning rate: step size parameter
-       self.n_episodes = 150
-       self.n_steps = 2000
-
-def sarsa_train(cfg,env,agent):
-    rewards = []
-    ma_rewards = []
-    for i_episode in range(cfg.n_episodes):
-        # Print out which episode we're on, useful for debugging.
-        # Generate an episode.
-        # An episode is an array of (state, action, reward) tuples
-        state = env.reset()
-        ep_reward = 0
-        while True:
-        # for t in range(cfg.n_steps):
-            action = agent.choose_action(state)
-            next_state, reward, done = env.step(action)
-            ep_reward+=reward
-            next_action = agent.choose_action(next_state)
-            agent.update(state, action, reward, next_state, next_action,done)
-            state = next_state
-            if done:
-                break  
-        if ma_rewards:
-            ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
-        else:
-            ma_rewards.append(ep_reward)
-        rewards.append(ep_reward)
-        # if (i_episode+1)%10==0:
-        #     print("Episode:{}/{}: Reward:{}".format(i_episode+1, cfg.n_episodes,ep_reward))
-    return rewards,ma_rewards
-    
-if __name__ == "__main__":
-    sarsa_cfg = SarsaConfig()
-    env = RacetrackEnv()
-    action_dim=9
-    agent = Sarsa(action_dim,sarsa_cfg)
-    rewards,ma_rewards = sarsa_train(sarsa_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 = "On-Policy First-Visit MC Control",path=RESULT_PATH)
-
-

codes/Sarsa/task0_train.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-11 17:59:16
+LastEditor: John
+LastEditTime: 2021-05-06 17:12:37
+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 datetime
+from envs.racetrack_env import RacetrackEnv
+from Sarsa.agent import Sarsa
+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 SarsaConfig:
+    ''' parameters for Sarsa
+    '''
+    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 = 200
+        self.eval_eps = 50
+        self.epsilon = 0.15 # epsilon: The probability to select a random action . 
+        self.gamma = 0.9 # gamma: Gamma discount factor.
+        self.lr = 0.2 # learning rate: step size parameter
+        self.n_steps = 2000
+
+def env_agent_config(cfg,seed=1):
+    env = RacetrackEnv()
+    action_dim=9
+    agent = Sarsa(action_dim,cfg)
+    return env,agent
+        
+def train(cfg,env,agent):
+    rewards = []
+    ma_rewards = []
+    for i_episode in range(cfg.train_eps):
+        # Print out which episode we're on, useful for debugging.
+        # Generate an episode.
+        # An episode is an array of (state, action, reward) tuples
+        state = env.reset()
+        ep_reward = 0
+        while True:
+        # for t in range(cfg.n_steps):
+            action = agent.choose_action(state)
+            next_state, reward, done = env.step(action)
+            ep_reward+=reward
+            next_action = agent.choose_action(next_state)
+            agent.update(state, action, reward, next_state, next_action,done)
+            state = next_state
+            if done:
+                break  
+        if ma_rewards:
+            ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
+        else:
+            ma_rewards.append(ep_reward)
+        rewards.append(ep_reward)
+        if (i_episode+1)%10==0:
+            print("Episode:{}/{}: Reward:{}".format(i_episode+1, cfg.train_eps,ep_reward))
+    return rewards,ma_rewards
+
+def eval(cfg,env,agent):
+    rewards = []
+    ma_rewards = []
+    for i_episode in range(cfg.eval_eps):
+        # Print out which episode we're on, useful for debugging.
+        # Generate an episode.
+        # An episode is an array of (state, action, reward) tuples
+        state = env.reset()
+        ep_reward = 0
+        while True:
+        # for t in range(cfg.n_steps):
+            action = agent.choose_action(state)
+            next_state, reward, done = env.step(action)
+            ep_reward+=reward
+            state = next_state
+            if done:
+                break  
+        if ma_rewards:
+            ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
+        else:
+            ma_rewards.append(ep_reward)
+        rewards.append(ep_reward)
+        if (i_episode+1)%10==0:
+            print("Episode:{}/{}: Reward:{}".format(i_episode+1, cfg.eval_eps,ep_reward))
+    print('Complete evaling！')
+    return rewards,ma_rewards
+        
+if __name__ == "__main__":
+    cfg = SarsaConfig()
+    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)
+
+    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)
+    
+    
+