updata Sarsa

codes/Sarsa/task0.py

@@ -0,0 +1,119 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-11 17:59:16
+LastEditor: John
+LastEditTime: 2022-04-24 23:03:51
+Discription: 
+Environment: 
+'''
+import sys,os
+curr_path = os.path.dirname(os.path.abspath(__file__)) # current path of file
+parent_path = os.path.dirname(curr_path)
+sys.path.append(parent_path)  # add current terminal path to sys.path
+
+import datetime
+import torch
+from envs.racetrack_env import RacetrackEnv
+from Sarsa.sarsa import Sarsa
+from common.utils import save_results,make_dir,plot_rewards
+
+curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
+
+class Config:
+    ''' parameters for Sarsa
+    '''
+    def __init__(self):
+        self.algo_name = 'Qlearning'
+        self.env_name = 'CliffWalking-v0' # 0 up, 1 right, 2 down, 3 left
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check GPU
+        self.result_path = curr_path+"/outputs/" +self.env_name+'/'+curr_time+'/results/'  # path to save results
+        self.model_path = curr_path+"/outputs/" +self.env_name+'/'+curr_time+'/models/'  # path to save models
+        self.train_eps = 300
+        self.test_eps = 20
+        self.epsilon_start = 0.90  # start value of epsilon
+        self.epsilon_end = 0.01  # end value of epsilon
+        self.epsilon_decay = 200  # decay rate of epsilon
+        self.gamma = 0.99 # gamma: Gamma discount factor.
+        self.lr = 0.2 # learning rate: step size parameter
+        self.n_steps = 200
+        self.save = True # if save figures
+
+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_ep in range(cfg.train_eps):
+        state = env.reset()
+        action = agent.choose_action(state)
+        ep_reward = 0
+        # while True:
+        for _ in range(cfg.n_steps):
+            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
+            action = next_action
+            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_ep+1)%2==0:
+            print(f"Episode:{i_ep+1}, Reward:{ep_reward}, Epsilon:{agent.epsilon}")
+    return rewards,ma_rewards
+
+def eval(cfg,env,agent):
+    rewards = []
+    ma_rewards = []
+    for i_ep in range(cfg.test_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 _ in range(cfg.n_steps):
+            action = agent.predict_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_ep+1)%1==0:
+            print("Episode:{}/{}: Reward:{}".format(i_ep+1, cfg.test_eps,ep_reward))
+    print('Complete evaling！')
+    return rewards,ma_rewards
+        
+if __name__ == "__main__":
+    cfg = Config()
+    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, cfg, tag="train")
+
+    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='test',path=cfg.result_path)
+    plot_rewards(rewards, ma_rewards, cfg, tag="test")
+    
+    
+