add MonteCarlo

2021-03-12 16:21:09 +08:00
parent 47390be0cf
commit f4a37178d2
10 changed files with 196 additions and 0 deletions
--- a/codes/MonteCarlo/main.py
+++ b/codes/MonteCarlo/main.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-11 14:26:44
+LastEditor: John
+LastEditTime: 2021-03-12 16:15:46
+Discription: 
+Environment: 
+'''
+import sys,os
+sys.path.append(os.getcwd())
+import argparse
+import datetime
+
+from envs.racetrack_env import RacetrackEnv
+from MonteCarlo.agent import FisrtVisitMC
+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 MCConfig:
+    def __init__(self): 
+        self.epsilon = 0.15 # epsilon: The probability to select a random action . 
+        self.gamma = 0.9 # gamma: Gamma discount factor.
+        self.n_episodes = 300
+        self.n_steps = 2000
+
+def get_mc_args():
+    '''set parameters
+    '''
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--epsilon", default=0.15, type=float)  # epsilon: The probability to select a random action . float between 0 and 1.
+    parser.add_argument("--gamma", default=0.9, type=float)  # gamma: Gamma discount factor.
+    parser.add_argument("--n_episodes", default=150, type=int)
+    parser.add_argument("--n_steps", default=2000, type=int)
+    mc_cfg = parser.parse_args()
+    return mc_cfg
+
+
+
+def mc_train(cfg,env,agent):
+    rewards = []
+    ma_rewards = [] # moving average rewards
+    for i_episode in range(cfg.n_episodes):
+        one_ep_transition = []
+        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
+            one_ep_transition.append((state, action, reward))
+            state = next_state
+            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)
+        agent.update(one_ep_transition)
+        if (i_episode+1)%10==0:
+            print("Episode:{}/{}: Reward:{}".format(i_episode+1, mc_cfg.n_episodes,ep_reward))
+    return rewards,ma_rewards
+if __name__ == "__main__":
+    mc_cfg = MCConfig()
+    env = RacetrackEnv()
+    n_actions=9
+    agent = FisrtVisitMC(n_actions,mc_cfg)
+    rewards,ma_rewards= mc_train(mc_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)
+    
+