更新算法模版

projects/codes/QLearning/task0.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-09-11 23:03:00
+LastEditor: John
+LastEditTime: 2022-10-30 02:04:55
+Discription: 
+Environment: 
+'''
+import sys,os
+os.environ["KMP_DUPLICATE_LIB_OK"]  =  "TRUE" # avoid "OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized."
+curr_path = os.path.dirname(os.path.abspath(__file__))  # current path
+parent_path = os.path.dirname(curr_path)  # parent path 
+sys.path.append(parent_path)  # add path to system path
+
+import gym
+import datetime
+import argparse
+from envs.gridworld_env import FrozenLakeWapper
+from envs.wrappers import CliffWalkingWapper
+from envs.register import register_env
+from qlearning import QLearning
+from common.utils import all_seed,merge_class_attrs
+from common.launcher import Launcher
+from config.config import GeneralConfigQLearning,AlgoConfigQLearning
+
+class Main(Launcher):
+    def __init__(self) -> None:
+        super().__init__()
+        self.cfgs['general_cfg'] = merge_class_attrs(self.cfgs['general_cfg'],GeneralConfigQLearning())
+        self.cfgs['algo_cfg'] = merge_class_attrs(self.cfgs['algo_cfg'],AlgoConfigQLearning())
+    def env_agent_config(self,cfg,logger):
+        ''' create env and agent
+        '''  
+        register_env(cfg.env_name)
+        env = gym.make(cfg.env_name,new_step_api=False)  # create env
+        if cfg.env_name == 'CliffWalking-v0':
+            env = CliffWalkingWapper(env)
+        if cfg.seed !=0: # set random seed
+            all_seed(env,seed=cfg.seed) 
+        try: # state dimension
+            n_states = env.observation_space.n # print(hasattr(env.observation_space, 'n'))
+        except AttributeError:
+            n_states = env.observation_space.shape[0] # print(hasattr(env.observation_space, 'shape'))
+        n_actions = env.action_space.n  # action dimension
+        logger.info(f"n_states: {n_states}, n_actions: {n_actions}") # print info
+        # update to cfg paramters
+        setattr(cfg, 'n_states', n_states)
+        setattr(cfg, 'n_actions', n_actions)
+        agent = QLearning(cfg)
+        return env,agent
+    def train(self,cfg,env,agent,logger):
+        logger.info("Start training!")
+        logger.info(f"Env: {cfg.env_name}, Algorithm: {cfg.algo_name}, Device: {cfg.device}")
+        rewards = []  # record rewards for all episodes
+        steps = [] # record steps for all episodes
+        for i_ep in range(cfg.train_eps):
+            ep_reward = 0  # reward per episode
+            ep_step = 0 # step per episode
+            state = env.reset()  # reset and obtain initial state
+            for _ in range(cfg.max_steps):
+                action = agent.sample_action(state)  # sample action
+                next_state, reward, terminated, _ = env.step(action)  # update env and return transitions
+                agent.update(state, action, reward, next_state, terminated)  # update agent
+                state = next_state  # update state
+                ep_reward += reward
+                ep_step += 1
+                if terminated:
+                    break
+            rewards.append(ep_reward)
+            steps.append(ep_step)
+            logger.info(f'Episode: {i_ep+1}/{cfg.train_eps}, Reward: {ep_reward:.2f}, Steps:{ep_step:d}, Epislon: {agent.epsilon:.3f}')
+        logger.info("Finish training!")
+        return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
+    def test(self,cfg,env,agent,logger):
+        logger.info("Start testing!")
+        logger.info(f"Env: {cfg.env_name}, Algorithm: {cfg.algo_name}, Device: {cfg.device}")
+        rewards = []  # record rewards for all episodes
+        steps = [] # record steps for all episodes
+        for i_ep in range(cfg.test_eps):
+            ep_reward = 0  # reward per episode
+            ep_step = 0
+            state = env.reset()  # reset and obtain initial state
+            for _ in range(cfg.max_steps):
+                action = agent.predict_action(state)  # predict action
+                next_state, reward, terminated, _ = env.step(action)  
+                state = next_state 
+                ep_reward += reward
+                ep_step += 1
+                if terminated:
+                    break
+            rewards.append(ep_reward)
+            steps.append(ep_step)
+            logger.info(f"Episode: {i_ep+1}/{cfg.test_eps}, Reward: {ep_reward:.2f}, Steps:{ep_step:d}")
+        logger.info("Finish testing!")
+        return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
+
+if __name__ == "__main__":
+    main = Main()
+    main.run()
+   
+
+        
+