150 lines
6.8 KiB
Python
150 lines
6.8 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: 2022-10-30 01:19:43
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LastEditor: JiangJi
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LastEditTime: 2022-11-01 00:08:22
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Discription: the only difference from task0.py is that the actor here we use ActorSoftmaxTanh instead of ActorSoftmax with ReLU
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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 gym
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import torch
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import numpy as np
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from common.utils import all_seed,merge_class_attrs
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from common.launcher import Launcher
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from common.memories import PGReplay
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from common.models import ActorNormal,Critic
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from envs.register import register_env
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from a2c import A2C
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from config.config import GeneralConfigA2C,AlgoConfigA2C
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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'],GeneralConfigA2C())
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self.cfgs['algo_cfg'] = merge_class_attrs(self.cfgs['algo_cfg'],AlgoConfigA2C())
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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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try:
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n_actions = env.action_space.n # action dimension
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except AttributeError:
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n_actions = env.action_space.shape[0]
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logger.info(f"action bound: {abs(env.action_space.low.item())}")
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setattr(cfg, 'action_bound', abs(env.action_space.low.item()))
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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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models = {'Actor':ActorNormal(n_states,n_actions, hidden_dim = cfg.actor_hidden_dim),'Critic':Critic(n_states,1,hidden_dim=cfg.critic_hidden_dim)}
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memories = {'ACMemory':PGReplay()}
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agent = A2C(models,memories,cfg)
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for k,v in models.items():
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logger.info(f"{k} model name: {type(v).__name__}")
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for k,v in memories.items():
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logger.info(f"{k} memory name: {type(v).__name__}")
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logger.info(f"agent name: {type(agent).__name__}")
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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 # step per episode
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ep_entropy = 0 # entropy per episode
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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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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
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agent.memory.push((agent.value,agent.log_prob,reward)) # save transitions
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state = next_state # update state
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ep_reward += reward
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ep_entropy += agent.entropy
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ep_step += 1
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if terminated:
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break
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agent.update(next_state,ep_entropy) # update agent
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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 # step per episode
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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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action = agent.predict_action(state) # predict action
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next_state, reward, terminated, truncated , info = env.step(action)
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state = next_state
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ep_reward += reward
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ep_step += 1
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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,cfg,env,agent,logger):
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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 # step per episode
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# ep_entropy = 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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# 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
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# agent.memory.push((agent.value,agent.log_prob,reward)) # save transitions
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# state = next_state # update state
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# ep_reward += reward
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# ep_entropy += agent.entropy
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# ep_step += 1
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# if terminated:
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# break
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# agent.update(next_state,ep_entropy) # update agent
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# rewards.append(ep_reward)
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# steps.append(ep_step)
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# logger.info(f"Episode: {i_ep+1}/{cfg.train_eps}, Reward: {ep_reward:.2f}, Steps:{ep_step}")
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# logger.info("Finish training!")
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# return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
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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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# action = agent.predict_action(state) # predict action
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# next_state, reward, terminated, truncated , info = env.step(action)
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# state = next_state
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# ep_reward += reward
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# ep_step += 1
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# if terminated:
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# break
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# rewards.append(ep_reward)
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# steps.append(ep_step)
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# logger.info(f"Episode: {i_ep+1}/{cfg.test_eps}, Reward: {ep_reward:.2f}, Steps:{ep_step}")
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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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