update projects
This commit is contained in:
johnjim0816
@@ -20,31 +20,33 @@ import argparse
|
||||
|
||||
from common.utils import save_results,make_dir
|
||||
from common.utils import plot_rewards,save_args
|
||||
from common.models import MLP
|
||||
from common.memories import ReplayBuffer
|
||||
from DoubleDQN.double_dqn import DoubleDQN
|
||||
|
||||
def get_args():
|
||||
""" Hyperparameters
|
||||
""" 超参数
|
||||
"""
|
||||
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # Obtain current time
|
||||
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
|
||||
parser = argparse.ArgumentParser(description="hyperparameters")
|
||||
parser.add_argument('--algo_name',default='DoubleDQN',type=str,help="name of algorithm")
|
||||
parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
|
||||
parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
|
||||
parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
|
||||
parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
|
||||
parser.add_argument('--gamma',default=0.95,type=float,help="discounted factor")
|
||||
parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
|
||||
parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
|
||||
parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon")
|
||||
parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
|
||||
parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity")
|
||||
parser.add_argument('--batch_size',default=64,type=int)
|
||||
parser.add_argument('--target_update',default=2,type=int)
|
||||
parser.add_argument('--target_update',default=4,type=int)
|
||||
parser.add_argument('--hidden_dim',default=256,type=int)
|
||||
parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda")
|
||||
parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
|
||||
'/' + curr_time + '/results/' )
|
||||
parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
|
||||
'/' + curr_time + '/models/' ) # path to save models
|
||||
'/' + curr_time + '/models/' ) # 保存模型的路径
|
||||
parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
@@ -55,19 +57,20 @@ def env_agent_config(cfg,seed=1):
|
||||
env.seed(seed)
|
||||
n_states = env.observation_space.shape[0]
|
||||
n_actions = env.action_space.n
|
||||
agent = DoubleDQN(n_states,n_actions,cfg)
|
||||
model = MLP(n_states, n_actions,hidden_dim=cfg.hidden_dim)
|
||||
memory = ReplayBuffer(cfg.memory_capacity)
|
||||
agent = DoubleDQN(n_states,n_actions,model,memory,cfg)
|
||||
return env,agent
|
||||
|
||||
def train(cfg,env,agent):
|
||||
print('Start training!')
|
||||
print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
|
||||
print("开始训练！")
|
||||
print(f"回合：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}")
|
||||
rewards = [] # 记录所有回合的奖励
|
||||
ma_rewards = [] # 记录所有回合的滑动平均奖励
|
||||
for i_ep in range(cfg.train_eps):
|
||||
ep_reward = 0 # 记录一回合内的奖励
|
||||
state = env.reset() # 重置环境，返回初始状态
|
||||
while True:
|
||||
action = agent.choose_action(state)
|
||||
action = agent.sample(state)
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
ep_reward += reward
|
||||
agent.memory.push(state, action, reward, next_state, done)
|
||||
@@ -78,61 +81,45 @@ def train(cfg,env,agent):
|
||||
if i_ep % cfg.target_update == 0:
|
||||
agent.target_net.load_state_dict(agent.policy_net.state_dict())
|
||||
if (i_ep+1)%10 == 0:
|
||||
print(f'Env:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(
|
||||
0.9*ma_rewards[-1]+0.1*ep_reward)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
print('Finish training!')
|
||||
return {'rewards':rewards,'ma_rewards':ma_rewards}
|
||||
print(f'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.2f}，Epislon：{agent.epsilon:.3f}')
|
||||
rewards.append(ep_reward)
|
||||
print("完成训练！")
|
||||
return {'rewards':rewards}
|
||||
|
||||
def test(cfg,env,agent):
|
||||
print('Start testing')
|
||||
print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
|
||||
############# 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0 ###############
|
||||
cfg.epsilon_start = 0.0 # e-greedy策略中初始epsilon
|
||||
cfg.epsilon_end = 0.0 # e-greedy策略中的终止epsilon
|
||||
################################################################################
|
||||
print("开始测试！")
|
||||
print(f"回合：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}")
|
||||
rewards = [] # 记录所有回合的奖励
|
||||
ma_rewards = [] # 记录所有回合的滑动平均奖励
|
||||
|
||||
for i_ep in range(cfg.test_eps):
|
||||
state = env.reset()
|
||||
ep_reward = 0
|
||||
while True:
|
||||
action = agent.choose_action(state)
|
||||
action = agent.predict(state)
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
state = next_state
|
||||
ep_reward += reward
|
||||
if done:
|
||||
break
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
print(f"Epside:{i_ep+1}/{cfg.test_eps}, Reward:{ep_reward:.1f}")
|
||||
print('Finish testing!')
|
||||
return {'rewards':rewards,'ma_rewards':ma_rewards}
|
||||
print(f'回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.2f}')
|
||||
print("完成测试！")
|
||||
return {'rewards':rewards}
|
||||
|
||||
if __name__ == "__main__":
|
||||
cfg = get_args()
|
||||
print(cfg.device)
|
||||
# training
|
||||
env,agent = env_agent_config(cfg,seed=1)
|
||||
# 训练
|
||||
env, agent = env_agent_config(cfg,seed=1)
|
||||
res_dic = train(cfg, env, agent)
|
||||
make_dir(cfg.result_path, cfg.model_path)
|
||||
save_args(cfg)
|
||||
agent.save(path=cfg.model_path)
|
||||
make_dir(cfg.result_path, cfg.model_path)
|
||||
save_args(cfg) # 保存参数
|
||||
agent.save(path=cfg.model_path) # 保存模型
|
||||
save_results(res_dic, tag='train',
|
||||
path=cfg.result_path)
|
||||
plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="train")
|
||||
# testing
|
||||
env,agent = env_agent_config(cfg,seed=10)
|
||||
agent.load(path=cfg.model_path)
|
||||
res_dic = test(cfg,env,agent)
|
||||
plot_rewards(res_dic['rewards'], cfg, tag="train")
|
||||
# 测试
|
||||
env, agent = env_agent_config(cfg,seed=1)
|
||||
agent.load(path=cfg.model_path) # 导入模型
|
||||
res_dic = test(cfg, env, agent)
|
||||
save_results(res_dic, tag='test',
|
||||
path=cfg.result_path)
|
||||
plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="test")
|
||||
path=cfg.result_path) # 保存结果
|
||||
plot_rewards(res_dic['rewards'], cfg, tag="test") # 画出结果
|
||||
|
||||
Reference in New Issue
Block a user