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codes/HierarchicalDQN/main.py

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+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-24 22:14:04
+LastEditor: John
+LastEditTime: 2021-03-27 04:23:43
+Discription: 
+Environment: 
+'''
+import sys,os
+sys.path.append(os.getcwd()) # add current terminal path to sys.path
+import gym
+import numpy as np
+import torch
+import datetime
+from HierarchicalDQN.agent import HierarchicalDQN
+from common.plot import plot_rewards
+from common.utils import save_results
+
+SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
+SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/'  # path to save model
+if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): 
+    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
+if not os.path.exists(SAVED_MODEL_PATH):
+    os.mkdir(SAVED_MODEL_PATH)
+RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # path to save rewards
+if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): 
+    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
+if not os.path.exists(RESULT_PATH): 
+    os.mkdir(RESULT_PATH)
+
+class HierarchicalDQNConfig:
+    def __init__(self):
+        self.algo = "DQN" # name of algo
+        self.gamma = 0.99
+        self.epsilon_start = 0.95 # start epsilon of e-greedy policy
+        self.epsilon_end = 0.01
+        self.epsilon_decay = 200
+        self.lr = 0.01 # learning rate
+        self.memory_capacity = 800 # Replay Memory capacity
+        self.batch_size = 64
+        self.train_eps = 250 # 训练的episode数目
+        self.train_steps = 200 # 训练每个episode的最大长度
+        self.target_update = 2 # target net的更新频率
+        self.eval_eps = 20 # 测试的episode数目
+        self.eval_steps = 200 # 测试每个episode的最大长度
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
+        self.hidden_dim = 256 # dimension of hidden layer
+
+def train(cfg,env,agent):
+    print('Start to train !')
+    rewards = []
+    ma_rewards = [] # moving average reward
+    ep_steps = []
+    for i_episode in range(cfg.train_eps):
+        state = env.reset() 
+        extrinsic_reward = 0
+        for i_step in range(cfg.train_steps):
+            goal= agent.set_goal(state)
+            meta_state = state
+            goal_state  = np.concatenate([state, goal])
+            action = agent.choose_action(state) 
+            next_state, reward, done, _ = env.step(action)
+            extrinsic_reward += reward
+            intrinsic_reward = 1.0 if goal == np.argmax(next_state) else 0.0
+            agent.memory.push(goal_state, action, intrinsic_reward, np.concatenate([next_state, goal]), done)
+            state = next_state 
+            agent.update()
+            if done:
+                break
+        if i_episode % cfg.target_update == 0:
+            agent.target_net.load_state_dict(agent.policy_net.state_dict())
+        print('Episode:{}/{}, Reward:{}, Steps:{}, Done:{}'.format(i_episode+1,cfg.train_eps,extrinsic_reward,i_step+1,done))
+        ep_steps.append(i_step)
+        rewards.append(extrinsic_reward)
+        if ma_rewards:
+            ma_rewards.append(
+                0.9*ma_rewards[-1]+0.1*extrinsic_reward)
+        else:
+            ma_rewards.append(extrinsic_reward)   
+    agent.meta_memory.push(meta_state, goal, extrinsic_reward, state, done)
+    print('Complete training！')
+    return rewards,ma_rewards
+
+if __name__ == "__main__":
+    cfg = HierarchicalDQNConfig()
+    env = gym.make('CartPole-v0')
+    env.seed(1) 
+    state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.n
+    agent = HierarchicalDQN(state_dim,action_dim,cfg)
+    rewards,ma_rewards = train(cfg,env,agent)
+    agent.save(path=SAVED_MODEL_PATH)
+    save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
+    plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)