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
2021-11-19 16:02:34 +08:00
parent 129c0c65fa
commit 64c319cab4
47 changed files with 262 additions and 255 deletions
Download Patch File Download Diff File Expand all files Collapse all files

46
codes/SAC/task0_train.py
View File

@@ -10,10 +10,9 @@ Discription:
Environment:
'''
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) # 添加路径到系统路径
import gym
import torch
@@ -24,7 +23,7 @@ from SAC.agent import SAC
from common.utils import save_results, make_dir
from common.plot import plot_rewards
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 SACConfig:
def __init__(self) -> None:
@@ -48,6 +47,14 @@ class SACConfig:
self.hidden_dim = 256
self.batch_size = 128
self.device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
class PlotConfig(SACConfig):
def __init__(self) -> None:
super().__init__()
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
def env_agent_config(cfg,seed=1):
env = NormalizedActions(gym.make(cfg.env_name))
@@ -58,13 +65,13 @@ def env_agent_config(cfg,seed=1):
return env,agent
def train(cfg,env,agent):
print('Start to train !')
print(f'Env: {cfg.env_name}, Algorithm: {cfg.algo}, Device: {cfg.device}')
rewards = []
ma_rewards = [] # moveing average reward
print('开始训练!')
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.train_eps):
state = env.reset()
ep_reward = 0
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
for i_step in range(cfg.train_steps):
action = agent.policy_net.get_action(state)
next_state, reward, done, _ = env.step(action)
@@ -111,21 +118,20 @@ def eval(cfg,env,agent):
if __name__ == "__main__":
cfg=SACConfig()
plot_cfg = PlotConfig()
# train
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(rewards, ma_rewards, tag="train",
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")
# eval
env,agent = env_agent_config(cfg,seed=10)
agent.load(path=cfg.model_path)
agent.load(path=plot_cfg.model_path)
rewards,ma_rewards = eval(cfg,env,agent)
save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
save_results(rewards,ma_rewards,tag='eval',path=plot_cfg.result_path)
plot_rewards(rewards,ma_rewards,plot_cfg,tag="eval")