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johnjim0816
2021-09-15 10:32:52 +08:00
parent 95f3f4dd57
commit 5085040330
74 changed files with 431 additions and 433 deletions
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48
codes/DQN/task0_train.py
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@@ -5,14 +5,14 @@
@Email: johnjim0816@gmail.com
@Date: 2020-06-12 00:48:57
@LastEditor: John
LastEditTime: 2021-05-05 16:49:15
LastEditTime: 2021-09-15 02:19:54
@Discription:
@Environment: python 3.7.7
'''
import sys,os
curr_path = os.path.dirname(__file__)
parent_path = os.path.dirname(curr_path)
sys.path.append(parent_path) # add current terminal path to sys.path
curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
parent_path = os.path.dirname(curr_path) # 父路径
sys.path.append(parent_path) # 添加父路径到系统路径sys.path
import gym
import torch
@@ -22,42 +22,41 @@ from common.utils import save_results, make_dir
from common.plot import plot_rewards
from DQN.agent import DQN
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
class DQNConfig:
def __init__(self):
self.algo = "DQN" # name of algo
self.env = 'CartPole-v0'
self.algo = "DQN" # 算法名称
self.env = 'CartPole-v0' # 环境名称
self.result_path = curr_path+"/outputs/" + self.env + \
'/'+curr_time+'/results/' # path to save results
self.model_path = curr_path+"/outputs/" + self.env + \
'/'+curr_time+'/models/' # path to save models
self.train_eps = 300 # max trainng episodes
self.eval_eps = 50 # number of episodes for evaluating
self.gamma = 0.95
self.epsilon_start = 0.90 # start epsilon of e-greedy policy
self.epsilon_end = 0.01
self.epsilon_decay = 500
self.lr = 0.0001 # learning rate
self.train_eps = 200 # 训练的回合数
self.eval_eps = 30 # 测试的回合数
self.gamma = 0.95
self.epsilon_start = 0.90 # e-greedy策略中初始epsilon
self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
self.epsilon_decay = 500 # e-greedy策略中epsilon的衰减率
self.lr = 0.0001 # 学习率
self.memory_capacity = 100000 # capacity of Replay Memory
self.batch_size = 64
self.target_update = 4 # update frequency of target net
self.target_update = 4 # 目标网络的更新频率
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu") # check gpu
"cuda" if torch.cuda.is_available() else "cpu") # jian che
self.hidden_dim = 256 # hidden size of net
def env_agent_config(cfg,seed=1):
env = gym.make(cfg.env)
env.seed(seed)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
agent = DQN(state_dim,action_dim,cfg)
n_states = env.observation_space.shape[0]
n_actions = env.action_space.n
agent = DQN(n_states,n_actions,cfg)
return env,agent
def train(cfg, env, agent):
print('Start to train !')
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
print(f'Env: {cfg.env}, Algorithm: {cfg.algo}, Device: {cfg.device}')
rewards = []
ma_rewards = [] # moveing average reward
for i_ep in range(cfg.train_eps):
@@ -78,7 +77,7 @@ def train(cfg, env, agent):
if (i_ep+1)%10 == 0:
print('Episode:{}/{}, Reward:{}'.format(i_ep+1, cfg.train_eps, ep_reward))
rewards.append(ep_reward)
# save ma rewards
# save ma_rewards
if ma_rewards:
ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
else:
@@ -88,7 +87,7 @@ def train(cfg, env, agent):
def eval(cfg,env,agent):
print('Start to eval !')
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
print(f'Env: {cfg.env}, Algorithm: {cfg.algo}, Device: {cfg.device}')
rewards = []
ma_rewards = [] # moving average rewards
for i_ep in range(cfg.eval_eps):
@@ -106,8 +105,7 @@ def eval(cfg,env,agent):
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
else:
ma_rewards.append(ep_reward)
if (i_ep+1)%10 == 10:
print(f"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}")
print(f"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}")
print('Complete evaling！')
return rewards,ma_rewards