update projects

projects/codes/PPO/task1.py

@@ -0,0 +1,67 @@
+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) # 添加路径到系统路径
+
+import gym
+import torch
+import datetime
+from common.utils import plot_rewards
+from common.utils import save_results,make_dir
+from ppo2 import PPO
+
+curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
+
+class PPOConfig:
+    def __init__(self) -> None:
+        self.algo = "PPO"  # 算法名称
+        self.env_name = 'Pendulum-v1' # 环境名称
+        self.continuous = True # 环境是否为连续动作
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # 检测GPU
+        self.train_eps = 200 # 训练的回合数
+        self.test_eps = 20 # 测试的回合数
+        self.batch_size = 5
+        self.gamma=0.99
+        self.n_epochs = 4
+        self.actor_lr = 0.0003
+        self.critic_lr = 0.0003
+        self.gae_lambda=0.95
+        self.policy_clip=0.2
+        self.hidden_dim = 256
+        self.update_fre = 20 # frequency of agent update
+
+class PlotConfig:
+    def __init__(self) -> None:
+        self.algo = "PPO"  # 算法名称
+        self.env_name = 'Pendulum-v1' # 环境名称
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # 检测GPU
+        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 = gym.make(cfg.env_name)  
+    env.seed(seed)
+    n_states = env.observation_space.shape[0]
+    n_actions = env.action_space.shape[0]
+    agent = PPO(n_states,n_actions,cfg)
+    return env,agent
+
+
+cfg  = PPOConfig()
+plot_cfg = PlotConfig()
+# 训练
+env,agent = env_agent_config(cfg,seed=1)
+rewards, ma_rewards = train(cfg, env, agent)
+make_dir(plot_cfg.result_path, plot_cfg.model_path) # 创建保存结果和模型路径的文件夹
+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=plot_cfg.model_path)
+rewards,ma_rewards = eval(cfg,env,agent)
+save_results(rewards,ma_rewards,tag='eval',path=plot_cfg.result_path)
+plot_rewards(rewards,ma_rewards,plot_cfg,tag="eval")