hot update
This commit is contained in:
johnjim0816
@@ -5,7 +5,7 @@ Author: John
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Email: johnjim0816@gmail.com
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Date: 2020-11-22 23:21:53
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LastEditor: John
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LastEditTime: 2022-07-21 21:44:00
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LastEditTime: 2022-08-22 17:40:07
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Discription:
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Environment:
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'''
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@@ -19,10 +19,11 @@ import torch
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import datetime
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import argparse
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from itertools import count
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import torch.nn.functional as F
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from pg import PolicyGradient
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from common.utils import save_results, make_dir
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from common.utils import plot_rewards
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from common.utils import save_results, make_dir,all_seed,save_args,plot_rewards
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from common.models import MLP
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from common.memories import PGReplay
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def get_args():
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@@ -32,112 +33,107 @@ def get_args():
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parser = argparse.ArgumentParser(description="hyperparameters")
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parser.add_argument('--algo_name',default='PolicyGradient',type=str,help="name of algorithm")
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parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
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parser.add_argument('--train_eps',default=300,type=int,help="episodes of training")
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parser.add_argument('--train_eps',default=200,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=0.99,type=float,help="discounted factor")
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parser.add_argument('--lr',default=0.01,type=float,help="learning rate")
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parser.add_argument('--batch_size',default=8,type=int)
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parser.add_argument('--lr',default=0.005,type=float,help="learning rate")
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parser.add_argument('--update_fre',default=8,type=int)
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parser.add_argument('--hidden_dim',default=36,type=int)
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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=1,type=int,help="seed")
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parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
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'/' + curr_time + '/results/' )
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parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
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'/' + curr_time + '/models/' ) # path to save models
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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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parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")
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parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")
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args = parser.parse_args([])
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return args
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class PGNet(MLP):
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''' instead of outputing action, PG Net outputs propabilities of actions, we can use class inheritance from MLP here
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'''
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def forward(self, x):
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = F.sigmoid(self.fc3(x))
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return x
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def env_agent_config(cfg,seed=1):
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def env_agent_config(cfg):
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env = gym.make(cfg.env_name)
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env.seed(seed)
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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.shape[0]
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agent = PolicyGradient(n_states,cfg)
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n_actions = env.action_space.n # action dimension
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print(f"state dim: {n_states}, action dim: {n_actions}")
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model = PGNet(n_states,1,hidden_dim=cfg.hidden_dim)
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memory = PGReplay()
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agent = PolicyGradient(n_states,model,memory,cfg)
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return env,agent
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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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state_pool = [] # temp states pool per several episodes
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action_pool = []
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reward_pool = []
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print(f'Env:{cfg.env_name}, Algo:{cfg.algo_name}, Device:{cfg.device}')
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rewards = []
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ma_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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for _ in count():
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action = agent.choose_action(state) # 根据当前环境state选择action
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action = agent.sample_action(state) # sample action
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next_state, reward, done, _ = env.step(action)
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ep_reward += reward
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if done:
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reward = 0
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state_pool.append(state)
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action_pool.append(float(action))
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reward_pool.append(reward)
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agent.memory.push((state,float(action),reward))
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state = next_state
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if done:
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print(f'Episode：{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
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break
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if i_ep > 0 and i_ep % cfg.batch_size == 0:
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agent.update(reward_pool,state_pool,action_pool)
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state_pool = []
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action_pool = []
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reward_pool = []
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if (i_ep+1) % cfg.update_fre == 0:
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agent.update()
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(
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0.9*ma_rewards[-1]+0.1*ep_reward)
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else:
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ma_rewards.append(ep_reward)
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print('Finish training!')
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env.close() # close environment
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return rewards, ma_rewards
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res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
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return res_dic
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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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print("start testing!")
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print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
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rewards = []
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ma_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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for _ in count():
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action = agent.choose_action(state) # 根据当前环境state选择action
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action = agent.predict_action(state)
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next_state, reward, done, _ = env.step(action)
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ep_reward += reward
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if done:
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reward = 0
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state = next_state
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if done:
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print('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
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print(f'Episode: {i_ep+1}/{cfg.test_eps}，Reward: {ep_reward:.2f}')
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break
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(
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0.9*ma_rewards[-1]+0.1*ep_reward)
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else:
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ma_rewards.append(ep_reward)
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print('完成测试！')
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print("finish testing!")
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env.close()
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return rewards, ma_rewards
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return {'episodes':range(len(rewards)),'rewards':rewards}
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if __name__ == "__main__":
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cfg = Config()
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# 训练
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cfg = get_args()
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env, agent = env_agent_config(cfg)
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rewards, ma_rewards = train(cfg, env, agent)
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make_dir(cfg.result_path, cfg.model_path) # 创建保存结果和模型路径的文件夹
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agent.save(path=cfg.model_path) # 保存模型
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save_results(rewards, ma_rewards, tag='train',
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path=cfg.result_path) # 保存结果
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plot_rewards(rewards, ma_rewards, cfg, tag="train") # 画出结果
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# 测试
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env, agent = env_agent_config(cfg)
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agent.load(path=cfg.model_path) # 导入模型
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rewards, ma_rewards = test(cfg, env, agent)
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save_results(rewards, ma_rewards, tag='test',
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path=cfg.result_path) # 保存结果
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plot_rewards(rewards, ma_rewards, cfg, tag="test") # 画出结果
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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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