139 lines
6.3 KiB
Python
139 lines
6.3 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: JiangJi
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Email: johnjim0816@gmail.com
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Date: 2022-10-12 11:09:54
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LastEditor: JiangJi
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LastEditTime: 2022-10-31 00:13:31
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Discription: CartPole-v1,Acrobot-v1
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'''
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import sys,os
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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 to system path
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import gym
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from common.utils import all_seed,merge_class_attrs
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from common.models import MLP
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from common.memories import ReplayBuffer
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from common.launcher import Launcher
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from envs.register import register_env
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from dqn import DQN
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from config.config import GeneralConfigDQN,AlgoConfigDQN
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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'],GeneralConfigDQN())
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self.cfgs['algo_cfg'] = merge_class_attrs(self.cfgs['algo_cfg'],AlgoConfigDQN())
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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=True) # create 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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# cfg.update({"n_states":n_states,"n_actions":n_actions}) # update to cfg paramters
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model = MLP(n_states,n_actions,hidden_dim=cfg.hidden_dim)
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memory = ReplayBuffer(cfg.buffer_size) # replay buffer
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agent = DQN(model,memory,cfg) # create agent
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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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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, truncated , info = env.step(action) # update env and return transitions under new_step_api of OpenAI Gym
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agent.memory.push(state, action, reward,
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next_state, terminated) # save transitions
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agent.update() # 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, truncated , 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(self,env, agent,cfg,logger):
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# ''' 训练
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# '''
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# logger.info("Start training!")
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# logger.info(f"Env: {cfg.env_name}, Algorithm: {cfg.algo_name}, Device: {cfg.device}")
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# rewards = [] # record rewards for all episodes
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# steps = [] # record steps for all episodes
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# for i_ep in range(cfg.train_eps):
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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.sample_action(state) # sample action
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# next_state, reward, terminated, truncated , info = env.step(action) # update env and return transitions under new_step_api of OpenAI Gym
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# agent.memory.push(state, action, reward,
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# next_state, terminated) # save transitions
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# state = next_state # update next state for env
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# agent.update() # update agent
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# ep_reward += reward #
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# if terminated:
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# break
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# if (i_ep + 1) % cfg.target_update == 0: # target net update, target_update means "C" in pseucodes
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# agent.target_net.load_state_dict(agent.policy_net.state_dict())
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# steps.append(ep_step)
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# rewards.append(ep_reward)
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# logger.info(f'Episode: {i_ep+1}/{cfg.train_eps}, Reward: {ep_reward:.2f}: Epislon: {agent.epsilon:.3f}')
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# logger.info("Finish training!")
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# env.close()
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# res_dic = {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
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# return res_dic
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# def test(self,cfg, env, agent,logger):
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# logger.info("Start testing!")
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# logger.info(f"Env: {cfg.env_name}, Algorithm: {cfg.algo_name}, Device: {cfg.device}")
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# rewards = [] # record rewards for all episodes
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# steps = [] # record steps for all episodes
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# for i_ep in range(cfg.test_eps):
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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) # predict action
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# next_state, reward, terminated, _, _ = env.step(action)
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# state = next_state
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# ep_reward += reward
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# if terminated:
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# break
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# steps.append(ep_step)
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# rewards.append(ep_reward)
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# logger.info(f"Episode: {i_ep+1}/{cfg.test_eps}, Reward: {ep_reward:.2f}")
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# logger.info("Finish testing!")
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# env.close()
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# return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
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if __name__ == "__main__":
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main = Main()
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main.run()
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