update

codes/SAC/task0_train.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: JiangJi
+Email: johnjim0816@gmail.com
+Date: 2021-04-29 12:59:22
+LastEditor: JiangJi
+LastEditTime: 2021-04-29 13:56:56
+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
+
+
+import gym
+import torch
+import datetime
+
+from SAC.env import NormalizedActions
+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
+
+class SACConfig:
+    def __init__(self) -> None:
+        self.algo = 'SAC'
+        self.env = 'Pendulum-v0'
+        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
+        self.train_steps = 500
+        
+        self.gamma = 0.99
+        self.mean_lambda=1e-3
+        self.std_lambda=1e-3
+        self.z_lambda=0.0
+        self.soft_tau=1e-2
+        self.value_lr  = 3e-4
+        self.soft_q_lr = 3e-4
+        self.policy_lr = 3e-4
+        self.capacity = 1000000
+        self.hidden_dim = 256
+        self.batch_size  = 128
+        self.device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
+def train(cfg,env,agent):
+    rewards  = []
+    ma_rewards = [] # moveing average reward
+    for i_ep in range(cfg.train_eps):
+        state = env.reset()
+        ep_reward = 0
+        for i_step in range(cfg.train_steps):
+            action = agent.policy_net.get_action(state)
+            next_state, reward, done, _ = env.step(action)
+            agent.memory.push(state, action, reward, next_state, done)
+            agent.update()
+            state = next_state
+            ep_reward += reward
+            if done:
+                break
+        print(f"Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.3f}")
+        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) 
+    return rewards, ma_rewards
+if __name__ == "__main__":
+    cfg=SACConfig()
+    env = NormalizedActions(gym.make("Pendulum-v0"))
+    action_dim = env.action_space.shape[0]
+    state_dim  = env.observation_space.shape[0]
+    agent = SAC(state_dim,action_dim,cfg)
+    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",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
+
+
+
+
+