更新算法模版
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johnjim0816
@@ -1,32 +1,124 @@
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from common.utils import save_args,save_results,plot_rewards
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from common.utils import get_logger,save_results,save_cfgs,plot_rewards,merge_class_attrs,load_cfgs
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from common.config import GeneralConfig,AlgoConfig,MergedConfig
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import time
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from pathlib import Path
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import datetime
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import argparse
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class Launcher:
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def __init__(self) -> None:
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pass
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def get_args(self):
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cfg = {}
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return cfg
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def env_agent_config(self,cfg):
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self.get_cfg()
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def get_cfg(self):
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self.cfgs = {'general_cfg':GeneralConfig(),'algo_cfg':AlgoConfig()} # create config
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def process_yaml_cfg(self):
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''' load yaml config
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'''
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parser = argparse.ArgumentParser(description="hyperparameters")
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parser.add_argument('--yaml', default = None, type=str,help='the path of config file')
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args = parser.parse_args()
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if args.yaml is not None:
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load_cfgs(self.cfgs, args.yaml)
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def print_cfg(self,cfg):
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''' print parameters
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'''
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cfg_dict = vars(cfg)
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print("Hyperparameters:")
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print(''.join(['=']*80))
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tplt = "{:^20}\t{:^20}\t{:^20}"
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print(tplt.format("Name", "Value", "Type"))
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for k,v in cfg_dict.items():
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print(tplt.format(k,v,str(type(v))))
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print(''.join(['=']*80))
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def env_agent_config(self,cfg,logger):
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env,agent = None,None
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return env,agent
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def train(self,cfg, env, agent):
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res_dic = {}
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return res_dic
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def test(self,cfg, env, agent):
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res_dic = {}
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return res_dic
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def train_one_episode(self,env, agent, cfg):
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ep_reward = 0
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ep_step = 0
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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
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ep_step = 0
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return agent,ep_reward,ep_step
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def evaluate(self,env, agent, cfg):
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sum_eval_reward = 0
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for _ in range(cfg.eval_eps):
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_,eval_ep_reward,_ = self.test_one_episode(env, agent, cfg)
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sum_eval_reward += eval_ep_reward
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mean_eval_reward = sum_eval_reward/cfg.eval_eps
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return mean_eval_reward
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# def train(self,cfg, env, agent,logger):
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# res_dic = {}
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# return res_dic
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# def test(self,cfg, env, agent,logger):
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# res_dic = {}
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# return res_dic
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def create_path(self,cfg):
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
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self.task_dir = f"{cfg.mode.capitalize()}_{cfg.env_name}_{cfg.algo_name}_{curr_time}"
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Path(self.task_dir).mkdir(parents=True, exist_ok=True)
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self.model_dir = f"{self.task_dir}/models/"
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self.res_dir = f"{self.task_dir}/results/"
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self.log_dir = f"{self.task_dir}/logs/"
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def run(self):
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cfg = self.get_args()
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env, agent = self.env_agent_config(cfg)
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res_dic = self.train(cfg, env, agent)
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save_args(cfg,path = cfg['result_path']) # save parameters
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agent.save_model(path = cfg['model_path']) # save models
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save_results(res_dic, tag = 'train', path = cfg['result_path']) # save results
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plot_rewards(res_dic['rewards'], cfg, path = cfg['result_path'],tag = "train") # plot results
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# testing
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# env, agent = self.env_agent_config(cfg) # create new env for testing, sometimes can ignore this step
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agent.load_model(path = cfg['model_path']) # load model
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res_dic = self.test(cfg, env, agent)
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save_results(res_dic, tag='test',
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path = cfg['result_path'])
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plot_rewards(res_dic['rewards'], cfg, path = cfg['result_path'],tag = "test")
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self.process_yaml_cfg() # load yaml config
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cfg = MergedConfig() # merge config
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cfg = merge_class_attrs(cfg,self.cfgs['general_cfg'])
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cfg = merge_class_attrs(cfg,self.cfgs['algo_cfg'])
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self.print_cfg(cfg) # print the configuration
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self.create_path(cfg) # create the path to save the results
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logger = get_logger(self.log_dir) # create the logger
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env, agent = self.env_agent_config(cfg,logger)
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if cfg.load_checkpoint:
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agent.load_model(f"{cfg.load_path}/models/")
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logger.info(f"Start {cfg.mode}ing!")
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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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if cfg.mode.lower() == 'train':
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best_ep_reward = -float('inf')
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for i_ep in range(cfg.train_eps):
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agent,ep_reward,ep_step = self.train_one_episode(env, agent, cfg)
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logger.info(f"Episode: {i_ep+1}/{cfg.train_eps}, Reward: {ep_reward:.3f}, Step: {ep_step}")
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rewards.append(ep_reward)
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steps.append(ep_step)
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# for _ in range
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if (i_ep+1)%cfg.eval_per_episode == 0:
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mean_eval_reward = self.evaluate(env, agent, cfg)
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if mean_eval_reward >= best_ep_reward: # update best reward
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logger.info(f"Current episode {i_ep+1} has the best eval reward: {mean_eval_reward:.3f}")
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best_ep_reward = mean_eval_reward
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agent.save_model(self.model_dir) # save models with best reward
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# env.close()
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elif cfg.mode.lower() == 'test':
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for i_ep in range(cfg.test_eps):
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agent,ep_reward,ep_step = self.test_one_episode(env, agent, cfg)
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logger.info(f"Episode: {i_ep+1}/{cfg.test_eps}, Reward: {ep_reward:.3f}, Step: {ep_step}")
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rewards.append(ep_reward)
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steps.append(ep_step)
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agent.save_model(self.model_dir) # save models
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# env.close()
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logger.info(f"Finish {cfg.mode}ing!")
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res_dic = {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
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save_results(res_dic, self.res_dir) # save results
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save_cfgs(self.cfgs, self.task_dir) # save config
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plot_rewards(rewards, title=f"{cfg.mode.lower()}ing curve on {cfg.device} of {cfg.algo_name} for {cfg.env_name}" ,fpath= self.res_dir)
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# def run(self):
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# self.process_yaml_cfg() # load yaml config
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# cfg = MergedConfig() # merge config
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# cfg = merge_class_attrs(cfg,self.cfgs['general_cfg'])
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# cfg = merge_class_attrs(cfg,self.cfgs['algo_cfg'])
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# self.print_cfg(cfg) # print the configuration
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# self.create_path(cfg) # create the path to save the results
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# logger = get_logger(self.log_dir) # create the logger
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# env, agent = self.env_agent_config(cfg,logger)
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# if cfg.load_checkpoint:
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# agent.load_model(f"{cfg.load_path}/models/")
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# if cfg.mode.lower() == 'train':
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# res_dic = self.train(cfg, env, agent,logger)
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# elif cfg.mode.lower() == 'test':
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# res_dic = self.test(cfg, env, agent,logger)
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# save_results(res_dic, self.res_dir) # save results
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# save_cfgs(self.cfgs, self.task_dir) # save config
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# agent.save_model(self.model_dir) # save models
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# plot_rewards(res_dic['rewards'], title=f"{cfg.mode.lower()}ing curve on {cfg.device} of {cfg.algo_name} for {cfg.env_name}" ,fpath= self.res_dir)
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