154 lines
7.0 KiB
Python
154 lines
7.0 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: 2020-09-11 23:03:00
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LastEditor: John
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LastEditTime: 2022-08-24 11:27:01
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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 gym
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import datetime
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import argparse
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from envs.gridworld_env import CliffWalkingWapper,FrozenLakeWapper
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from qlearning import QLearning
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from common.utils import plot_rewards,save_args,all_seed
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from common.utils import save_results,make_dir
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def get_args():
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
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parser = argparse.ArgumentParser(description="hyperparameters")
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parser.add_argument('--algo_name',default='Q-learning',type=str,help="name of algorithm")
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parser.add_argument('--env_name',default='CliffWalking-v0',type=str,help="name of environment")
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parser.add_argument('--train_eps',default=400,type=int,help="episodes of training")
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parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
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parser.add_argument('--gamma',default=0.90,type=float,help="discounted factor")
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parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
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parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
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parser.add_argument('--epsilon_decay',default=300,type=int,help="decay rate of epsilon")
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parser.add_argument('--lr',default=0.1,type=float,help="learning rate")
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parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda")
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parser.add_argument('--seed',default=10,type=int,help="seed")
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parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")
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parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")
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args = parser.parse_args()
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default_args = {'result_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/results/",
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'model_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/models/",
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}
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args = {**vars(args),**default_args} # type(dict)
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return args
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def env_agent_config(cfg):
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''' create env and agent
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'''
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if cfg['env_name'] == 'CliffWalking-v0':
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env = gym.make(cfg['env_name'])
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env = CliffWalkingWapper(env)
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if cfg['env_name'] == 'FrozenLake-v1':
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env = gym.make(cfg['env_name'],is_slippery=False)
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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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n_states = env.observation_space.n # state dimension
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n_actions = env.action_space.n # action dimension
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print(f"n_states: {n_states}, 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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agent = QLearning(cfg)
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return env,agent
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def main(cfg,env,agent,tag = 'train'):
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print(f"Start {tag}ing!")
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print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
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rewards = [] # 记录奖励
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for i_ep in range(cfg.train_eps):
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ep_reward = 0 # 记录每个回合的奖励
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state = env.reset() # 重置环境,即开始新的回合
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while True:
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if tag == 'train':action = agent.sample_action(state) # 根据算法采样一个动作
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else: agent.predict_action(state)
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next_state, reward, done, _ = env.step(action) # 与环境进行一次动作交互
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if tag == 'train':agent.update(state, action, reward, next_state, done) # Q学习算法更新
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state = next_state # 更新状态
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ep_reward += reward
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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.train_eps}，奖励：{ep_reward:.1f}，Epsilon：{agent.epsilon}")
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print(f"Finish {tag}ing!")
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return {"rewards":rewards}
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def train(cfg,env,agent):
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print("Start training!")
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print(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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state = env.reset() # reset and obtain initial state
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while True:
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action = agent.sample_action(state) # sample action
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next_state, reward, done, _ = env.step(action) # update env and return transitions
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agent.update(state, action, reward, next_state, done) # update agent
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state = next_state # update state
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ep_reward += reward
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ep_step += 1
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if done:
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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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if (i_ep+1)%10==0:
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print(f'Episode: {i_ep+1}/{cfg["train_eps"]}, Reward: {ep_reward:.2f}, Steps:{ep_step}, Epislon: {agent.epsilon:.3f}')
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print("Finish training!")
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return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
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def test(cfg,env,agent):
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print("Start testing!")
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print(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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while True:
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action = agent.predict_action(state) # predict action
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next_state, reward, done, _ = 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 done:
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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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print(f"Episode: {i_ep+1}/{cfg['test_eps']}, Steps:{ep_step}, Reward: {ep_reward:.2f}")
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print("Finish testing!")
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return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
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if __name__ == "__main__":
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cfg = get_args()
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# training
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env, agent = env_agent_config(cfg)
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res_dic = 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 = 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 = 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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