update Sarsa

2021-03-12 17:19:04 +08:00
parent 1fdcfbbd66
commit f1394feb65
10 changed files with 147 additions and 357 deletions
--- a/codes/Sarsa/main.py
+++ b/codes/Sarsa/main.py
@@ -0,0 +1,80 @@
+#!/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()
+    n_actions=9
+    agent = Sarsa(n_actions,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)
+
+