125 lines
4.9 KiB
Python
125 lines
4.9 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: John
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Email: johnjim0816@gmail.com
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Date: 2021-03-11 14:26:44
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LastEditor: John
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LastEditTime: 2022-11-08 23:35:18
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Discription:
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Environment:
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'''
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import sys,os
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os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" # avoid "OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized."
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curr_path = os.path.dirname(os.path.abspath(__file__)) # current path
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parent_path = os.path.dirname(curr_path) # parent path
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sys.path.append(parent_path) # add path to system path
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import datetime
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import gym
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from envs.wrappers import CliffWalkingWapper
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from envs.register import register_env
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from common.utils import merge_class_attrs,all_seed
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from common.launcher import Launcher
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from MonteCarlo.agent import FisrtVisitMC
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from MonteCarlo.config.config import GeneralConfigMC,AlgoConfigMC
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class Main(Launcher):
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def __init__(self) -> None:
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super().__init__()
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self.cfgs['general_cfg'] = merge_class_attrs(self.cfgs['general_cfg'],GeneralConfigMC())
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self.cfgs['algo_cfg'] = merge_class_attrs(self.cfgs['algo_cfg'],AlgoConfigMC())
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def env_agent_config(self,cfg,logger):
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''' create env and agent
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'''
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register_env(cfg.env_name)
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env = gym.make(cfg.env_name,new_step_api=False) # create env
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if cfg.env_name == 'CliffWalking-v0':
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env = CliffWalkingWapper(env)
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if cfg.seed !=0: # set random seed
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all_seed(env,seed=cfg.seed)
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try: # state dimension
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n_states = env.observation_space.n # print(hasattr(env.observation_space, 'n'))
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except AttributeError:
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n_states = env.observation_space.shape[0] # print(hasattr(env.observation_space, 'shape'))
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n_actions = env.action_space.n # action dimension
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logger.info(f"n_states: {n_states}, n_actions: {n_actions}") # print info
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# update to cfg paramters
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setattr(cfg, 'n_states', n_states)
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setattr(cfg, 'n_actions', n_actions)
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agent = FisrtVisitMC(cfg)
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return env,agent
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def train_one_episode(self, env, agent, cfg):
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ep_reward = 0 # reward per episode
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ep_step = 0
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state = env.reset() # reset and obtain initial state
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one_ep_transition = []
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for _ in range(cfg.max_steps):
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ep_step += 1
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action = agent.sample_action(state) # sample action
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next_state, reward, terminated, info = env.step(action) # update env and return transitions under new_step_api of OpenAI Gym
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one_ep_transition.append((state, action, reward)) # save transitions
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agent.update(one_ep_transition) # update agent
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state = next_state # update next state for env
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ep_reward += reward #
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if terminated:
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break
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return agent,ep_reward,ep_step
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def test_one_episode(self, env, agent, cfg):
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ep_reward = 0 # reward per episode
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ep_step = 0
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state = env.reset() # reset and obtain initial state
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for _ in range(cfg.max_steps):
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ep_step += 1
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action = agent.predict_action(state) # sample action
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next_state, reward, terminated, info = env.step(action) # update env and return transitions under new_step_api of OpenAI Gym
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state = next_state # update next state for env
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ep_reward += reward #
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if terminated:
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break
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return agent,ep_reward,ep_step
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def train(cfg, env, agent):
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print("开始训练！")
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print(f"环境：{cfg.env_name}，算法：{cfg.algo_name}，设备：{cfg.device}")
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rewards = []
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for i_ep in range(cfg.train_eps):
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state = env.reset()
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ep_reward = 0
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one_ep_transition = []
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while True:
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action = agent.sample(state)
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next_state, reward, done = env.step(action)
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ep_reward += reward
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one_ep_transition.append((state, action, reward))
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state = next_state
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if done:
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break
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rewards.append(ep_reward)
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agent.update(one_ep_transition)
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if (i_ep+1) % 10 == 0:
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print(f"Episode:{i_ep+1}/{cfg.train_eps}: Reward:{ep_reward}")
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print("完成训练")
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return {'rewards':rewards}
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def test(cfg, env, agent):
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print("开始测试！")
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print(f"环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}")
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rewards = []
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for i_ep in range(cfg.test_eps):
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state = env.reset()
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ep_reward = 0
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while True:
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action = agent.predict(state)
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next_state, reward, done = env.step(action)
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ep_reward += reward
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state = next_state
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if done:
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break
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rewards.append(ep_reward)
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print(f'回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.2f}')
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return {'rewards':rewards}
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if __name__ == "__main__":
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main = Main()
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main.run() |