bacow/easy-rl
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update codes

Browse Source
This commit is contained in:
johnjim0816
2021-11-18 15:41:27 +08:00
parent 442e307b01
commit 129c0c65fa
103 changed files with 1025 additions and 558 deletions
Download Patch File Download Diff File Expand all files Collapse all files

66
codes/DDPG/task0_train.py
View File

@@ -12,7 +12,7 @@ LastEditTime: 2021-09-16 01:31:33
import sys,os
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
sys.path.append(parent_path) # 添加路径到系统路径sys.path
import datetime
import gym
@@ -21,44 +21,51 @@ import torch
from DDPG.env import NormalizedActions, OUNoise
from DDPG.agent import DDPG
from common.utils import save_results,make_dir
from common.plot import plot_rewards, plot_rewards_cn
from common.plot import plot_rewards
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
class DDPGConfig:
def __init__(self):
self.algo = 'DDPG' # 算法名称
self.env = 'Pendulum-v0' # 环境名称
self.result_path = curr_path+"/outputs/" + self.env + \
'/'+curr_time+'/results/' # 保存结果的路径
self.model_path = curr_path+"/outputs/" + self.env + \
'/'+curr_time+'/models/' # 保存模型的路径
self.env_name = 'Pendulum-v0' # 环境名称
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
self.train_eps = 300 # 训练的回合数
self.eval_eps = 50 # 测试的回合数
self.gamma = 0.99 # 折扣因子
self.critic_lr = 1e-3 # 评论家网络的学习率
self.actor_lr = 1e-4 # 演员网络的学习率
self.memory_capacity = 8000
self.batch_size = 128
self.target_update = 2
self.hidden_dim = 256
self.memory_capacity = 8000 # 经验回放的容量
self.batch_size = 128 # mini-batch SGD中的批量大小
self.target_update = 2 # 目标网络的更新频率
self.hidden_dim = 256 # 网络隐藏层维度
self.soft_tau = 1e-2 # 软更新参数
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class PlotConfig:
def __init__(self) -> None:
self.algo = "DQN" # 算法名称
self.env_name = 'CartPole-v0' # 环境名称
self.result_path = curr_path+"/outputs/" + self.env_name + \
'/'+curr_time+'/results/' # 保存结果的路径
self.model_path = curr_path+"/outputs/" + self.env_name + \
'/'+curr_time+'/models/' # 保存模型的路径
self.save = True # 是否保存图片
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
def env_agent_config(cfg,seed=1):
env = NormalizedActions(gym.make(cfg.env))
env = NormalizedActions(gym.make(cfg.env_name)) # 装饰action噪声
env.seed(seed) # 随机种子
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
agent = DDPG(state_dim,action_dim,cfg)
n_states = env.observation_space.shape[0]
n_actions = env.action_space.shape[0]
agent = DDPG(n_states,n_actions,cfg)
return env,agent
def train(cfg, env, agent):
print('开始训练！')
print(f'环境：{cfg.env}，算法：{cfg.algo}，设备：{cfg.device}')
print(f'环境：{cfg.env_name}，算法：{cfg.algo}，设备：{cfg.device}')
ou_noise = OUNoise(env.action_space) # 动作噪声
rewards = [] # 记录奖励
ma_rewards = [] # 记录滑动平均奖励
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):
state = env.reset()
ou_noise.reset()
@@ -86,9 +93,9 @@ def train(cfg, env, agent):
def eval(cfg, env, agent):
print('开始测试！')
print(f'环境：{cfg.env}, 算法：{cfg.algo}, 设备：{cfg.device}')
rewards = [] # 记录奖励
ma_rewards = [] # 记录滑动平均奖励
print(f'环境：{cfg.env_name}, 算法：{cfg.algo}, 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.eval_eps):
state = env.reset()
done = False
@@ -112,17 +119,18 @@ def eval(cfg, env, agent):
if __name__ == "__main__":
cfg = DDPGConfig()
plot_cfg = PlotConfig()
# 训练
env,agent = env_agent_config(cfg,seed=1)
rewards, ma_rewards = train(cfg, env, agent)
make_dir(cfg.result_path, cfg.model_path)
agent.save(path=cfg.model_path)
save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
plot_rewards_cn(rewards, ma_rewards, tag="train", env = cfg.env, algo=cfg.algo, path=cfg.result_path)
make_dir(plot_cfg.result_path, plot_cfg.model_path)
agent.save(path=plot_cfg.model_path)
save_results(rewards, ma_rewards, tag='train', path=plot_cfg.result_path)
plot_rewards(rewards, ma_rewards, plot_cfg, tag="train")
# 测试
env,agent = env_agent_config(cfg,seed=10)
agent.load(path=cfg.model_path)
rewards,ma_rewards = eval(cfg,env,agent)
agent.load(path=plot_cfg.model_path)
rewards,ma_rewards = eval(plot_cfg,env,agent)
save_results(rewards,ma_rewards,tag = 'eval',path = cfg.result_path)
plot_rewards_cn(rewards,ma_rewards,tag = "eval",env = cfg.env,algo = cfg.algo,path=cfg.result_path)
plot_rewards(rewards,ma_rewards,plot_cfg,tag = "eval")