hot update DQN

projects/codes/DQN/dqn.py

@@ -71,7 +71,7 @@ class DQN:
             return
         else:
             if not self.update_flag:
-                print("begin to update!")
+                print("Begin to update!")
                 self.update_flag = True
         # sample a batch of transitions from replay buffer
         state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(

projects/codes/DQN/main.py

@@ -27,7 +27,7 @@ def get_args():
     parser.add_argument('--gamma',default=0.95,type=float,help="discounted factor")
     parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
     parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
-    parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon")
+    parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon, the higher value, the slower decay")
     parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
     parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity")
     parser.add_argument('--batch_size',default=64,type=int)
@@ -64,8 +64,8 @@ def env_agent_config(cfg):
 def train(cfg, env, agent):
     ''' 训练
     '''
-    print("start training!")
+    print("Start training!")
-    print(f"Env: {cfg['env_name']}, Algo: {cfg['algo_name']}, Device: {cfg['device']}")
+    print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
     rewards = []  # record rewards for all episodes
     steps = []
     for i_ep in range(cfg["train_eps"]):
@@ -89,17 +89,17 @@ def train(cfg, env, agent):
         rewards.append(ep_reward)
         if (i_ep + 1) % 10 == 0:
             print(f'Episode: {i_ep+1}/{cfg["train_eps"]}, Reward: {ep_reward:.2f}: Epislon: {agent.epsilon:.3f}')
-    print("finish training!")
+    print("Finish training!")
     env.close()
     res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
     return res_dic
 
 def test(cfg, env, agent):
-    print("start testing!")
+    print("Start testing!")
-    print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
+    print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
     rewards = []  # record rewards for all episodes
     steps = []
-    for i_ep in range(cfg.test_eps):
+    for i_ep in range(cfg['test_eps']):
         ep_reward = 0  # reward per episode
         ep_step = 0
         state = env.reset()  # reset and obtain initial state
@@ -113,8 +113,8 @@ def test(cfg, env, agent):
                 break
         steps.append(ep_step)
         rewards.append(ep_reward)
-        print(f'Episode: {i_ep+1}/{cfg.test_eps}，Reward: {ep_reward:.2f}')
+        print(f"Episode: {i_ep+1}/{cfg['test_eps']}，Reward: {ep_reward:.2f}")
-    print("finish testing!")
+    print("Finish testing!")
     env.close()
     return {'episodes':range(len(rewards)),'rewards':rewards}
 

projects/codes/DQN/outputs/Acrobot-v1/20220824-124401/results/params.json

@@ -0,0 +1 @@
+{"algo_name": "DQN", "env_name": "Acrobot-v1", "train_eps": 100, "test_eps": 20, "gamma": 0.95, "epsilon_start": 0.95, "epsilon_end": 0.01, "epsilon_decay": 1500, "lr": 0.002, "memory_capacity": 200000, "batch_size": 128, "target_update": 4, "hidden_dim": 256, "device": "cuda", "seed": 10, "show_fig": false, "save_fig": true, "result_path": "C:\\Users\\jiangji\\Desktop\\rl-tutorials\\codes\\DQN/outputs/Acrobot-v1/20220824-124401/results", "model_path": "C:\\Users\\jiangji\\Desktop\\rl-tutorials\\codes\\DQN/outputs/Acrobot-v1/20220824-124401/models", "n_states": 6, "n_actions": 3}

projects/codes/DQN/outputs/Acrobot-v1/20220824-124401/results/testing_results.csv

@@ -0,0 +1,21 @@
+episodes,rewards
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projects/codes/DQN/outputs/Acrobot-v1/20220824-124401/results/training_results.csv

@@ -0,0 +1,101 @@
+episodes,rewards
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+99,-219.0

projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/params.json

@@ -1 +0,0 @@
-{"algo_name": "DQN", "env_name": "CartPole-v0", "train_eps": 200, "test_eps": 20, "gamma": 0.95, "epsilon_start": 0.95, "epsilon_end": 0.01, "epsilon_decay": 500, "lr": 0.0001, "memory_capacity": 100000, "batch_size": 64, "target_update": 4, "hidden_dim": 256, "device": "cpu", "seed": 10, "result_path": "/Users/jj/Desktop/rl-tutorials/codes/DQN/outputs/CartPole-v0/20220818-143132/results", "model_path": "/Users/jj/Desktop/rl-tutorials/codes/DQN/outputs/CartPole-v0/20220818-143132/models", "show_fig": false, "save_fig": true}

projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/testing_results.csv

@@ -1,21 +0,0 @@
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projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/training_results.csv

@@ -1,201 +0,0 @@
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projects/codes/DQN/task0.py

@@ -1,133 +0,0 @@
-import sys,os
-curr_path = os.path.dirname(os.path.abspath(__file__))  # current path
-parent_path = os.path.dirname(curr_path)  # parent path 
-sys.path.append(parent_path)  # add path to system path
-
-import gym
-import torch
-import datetime
-import numpy as np
-import argparse
-from common.utils import save_results,all_seed
-from common.utils import plot_rewards,save_args
-from common.models import MLP
-from common.memories import ReplayBuffer
-from dqn import DQN
-
-def get_args():
-    """ hyperparameters
-    """
-    curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # obtain current time
-    parser = argparse.ArgumentParser(description="hyperparameters")      
-    parser.add_argument('--algo_name',default='DQN',type=str,help="name of algorithm")
-    parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
-    parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
-    parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
-    parser.add_argument('--gamma',default=0.95,type=float,help="discounted factor")
-    parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
-    parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
-    parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon")
-    parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
-    parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity")
-    parser.add_argument('--batch_size',default=64,type=int)
-    parser.add_argument('--target_update',default=4,type=int)
-    parser.add_argument('--hidden_dim',default=256,type=int)
-    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
-    parser.add_argument('--seed',default=10,type=int,help="seed") 
-    parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
-            '/' + curr_time + '/results' )
-    parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
-            '/' + curr_time + '/models' ) 
-    parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")  
-    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")           
-    args = parser.parse_args()                          
-    return args
-
-def env_agent_config(cfg):
-    ''' create env and agent
-    '''
-    env = gym.make(cfg.env_name)  # create env
-    if cfg.seed !=0: # set random seed
-        all_seed(env,seed=cfg.seed)
-    n_states = env.observation_space.shape[0]  # state dimension
-    n_actions = env.action_space.n  # action dimension
-    print(f"state dim: {n_states}, action dim: {n_actions}")
-    model = MLP(n_states,n_actions,hidden_dim=cfg.hidden_dim)
-    memory =  ReplayBuffer(cfg.memory_capacity) # replay buffer
-    agent = DQN(n_actions,model,memory,cfg)  # create agent
-    return env, agent
-
-def train(cfg, env, agent):
-    ''' 训练
-    '''
-    print("start training!")
-    print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
-    rewards = []  # record rewards for all episodes
-    steps = []
-    for i_ep in range(cfg.train_eps):
-        ep_reward = 0  # reward per episode
-        ep_step = 0
-        state = env.reset()  # reset and obtain initial state
-        while True:
-            ep_step += 1
-            action = agent.sample_action(state)  # sample action
-            next_state, reward, done, _ = env.step(action)  # update env and return transitions
-            agent.memory.push(state, action, reward,
-                              next_state, done)  # save transitions
-            state = next_state  # update next state for env
-            agent.update()  # update agent
-            ep_reward += reward  #
-            if done:
-                break
-        if (i_ep + 1) % cfg.target_update == 0:  # target net update, target_update means "C" in pseucodes
-            agent.target_net.load_state_dict(agent.policy_net.state_dict())
-        steps.append(ep_step)
-        rewards.append(ep_reward)
-        if (i_ep + 1) % 10 == 0:
-            print(f'Episode: {i_ep+1}/{cfg.train_eps}, Reward: {ep_reward:.2f}: Epislon: {agent.epsilon:.3f}')
-    print("finish training!")
-    env.close()
-    res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
-    return res_dic
-
-def test(cfg, env, agent):
-    print("start testing!")
-    print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
-    rewards = []  # record rewards for all episodes
-    steps = []
-    for i_ep in range(cfg.test_eps):
-        ep_reward = 0  # reward per episode
-        ep_step = 0
-        state = env.reset()  # reset and obtain initial state
-        while True:
-            ep_step+=1
-            action = agent.predict_action(state)  # predict action
-            next_state, reward, done, _ = env.step(action)  
-            state = next_state  
-            ep_reward += reward 
-            if done:
-                break
-        steps.append(ep_step)
-        rewards.append(ep_reward)
-        print(f'Episode: {i_ep+1}/{cfg.test_eps}，Reward: {ep_reward:.2f}')
-    print("finish testing!")
-    env.close()
-    return {'episodes':range(len(rewards)),'rewards':rewards}
-
-
-if __name__ == "__main__":
-    cfg = get_args()
-    # training
-    env, agent = env_agent_config(cfg)
-    res_dic = train(cfg, env, agent)
-    save_args(cfg,path = cfg.result_path) # save parameters
-    agent.save_model(path = cfg.model_path)  # save models
-    save_results(res_dic, tag = 'train', path = cfg.result_path) # save results
-    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "train")  # plot results
-    # testing
-    env, agent = env_agent_config(cfg) # create new env for testing, sometimes can ignore this step
-    agent.load_model(path = cfg.model_path)  # load model
-    res_dic = test(cfg, env, agent)
-    save_results(res_dic, tag='test',
-                 path = cfg.result_path)  
-    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "test")  

projects/codes/QLearning/outputs/CliffWalking-v0/20220802-163256/results/params.json

@@ -1,15 +0,0 @@
-{
-    "algo_name": "Q-learning",
-    "env_name": "CliffWalking-v0",
-    "train_eps": 400,
-    "test_eps": 20,
-    "gamma": 0.9,
-    "epsilon_start": 0.95,
-    "epsilon_end": 0.01,
-    "epsilon_decay": 300,
-    "lr": 0.1,
-    "device": "cpu",
-    "result_path": "/root/Desktop/rl-tutorials/codes/QLearning/outputs/CliffWalking-v0/20220802-163256/results/",
-    "model_path": "/root/Desktop/rl-tutorials/codes/QLearning/outputs/CliffWalking-v0/20220802-163256/models/",
-    "save_fig": true
-}

projects/codes/QLearning/task0.py

@@ -1,127 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2020-09-11 23:03:00
-LastEditor: John
-LastEditTime: 2022-08-10 11:25:56
-Discription: 
-Environment: 
-'''
-import sys
-import 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
-import argparse
-from envs.gridworld_env import CliffWalkingWapper
-from qlearning import QLearning
-from common.utils import plot_rewards,save_args
-from common.utils import save_results,make_dir
-
-def get_args():
-    """ 
-    """
-    curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
-    parser = argparse.ArgumentParser(description="hyperparameters")      
-    parser.add_argument('--algo_name',default='Q-learning',type=str,help="name of algorithm")
-    parser.add_argument('--env_name',default='CliffWalking-v0',type=str,help="name of environment")
-    parser.add_argument('--train_eps',default=400,type=int,help="episodes of training") # 训练的回合数
-    parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") # 测试的回合数
-    parser.add_argument('--gamma',default=0.90,type=float,help="discounted factor") # 折扣因子
-    parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon") #  e-greedy策略中初始epsilon
-    parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") # e-greedy策略中的终止epsilon
-    parser.add_argument('--epsilon_decay',default=300,type=int,help="decay rate of epsilon") # e-greedy策略中epsilon的衰减率
-    parser.add_argument('--lr',default=0.1,type=float,help="learning rate")
-    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
-    parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
-            '/' + curr_time + '/results/',type=str )
-    parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
-            '/' + curr_time + '/models/',type=str,help="path to save models")
-    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")           
-    args = parser.parse_args()                          
-    return args
-curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
-      
-def train(cfg,env,agent):
-    print('开始训练！')
-    print(f'环境:{cfg.env_name}, 算法:{cfg.algo_name}, 设备:{cfg.device}')
-    rewards = []  # 记录奖励
-    for i_ep in range(cfg.train_eps):
-        ep_reward = 0  # 记录每个回合的奖励
-        state = env.reset()  # 重置环境,即开始新的回合
-        while True:
-            action = agent.sample(state)  # 根据算法采样一个动作
-            next_state, reward, done, _ = env.step(action)  # 与环境进行一次动作交互
-            agent.update(state, action, reward, next_state, done)  # Q学习算法更新
-            state = next_state  # 更新状态
-            ep_reward += reward
-            if done:
-                break
-        rewards.append(ep_reward)
-        print(f"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.1f}，Epsilon：{agent.epsilon}")
-    print('完成训练！')
-    return {"rewards":rewards}
-    
-def test(cfg,env,agent):
-    print('开始测试！')
-    print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
-    rewards = []  # 记录所有回合的奖励
-    for i_ep in range(cfg.test_eps):
-        ep_reward = 0  # 记录每个episode的reward
-        state = env.reset()  # 重置环境, 重新开一局（即开始新的一个回合）
-        while True:
-            action = agent.predict(state)  # 根据算法选择一个动作
-            next_state, reward, done, _ = env.step(action)  # 与环境进行一个交互
-            state = next_state  # 更新状态
-            ep_reward += reward
-            if done:
-                break
-        rewards.append(ep_reward)
-        print(f"回合数：{i_ep+1}/{cfg.test_eps}, 奖励：{ep_reward:.1f}")
-    print('完成测试！')
-    return {"rewards":rewards}
-        
-def env_agent_config(cfg,seed=1):
-    '''创建环境和智能体
-    Args:
-        cfg ([type]): [description]
-        seed (int, optional): 随机种子. Defaults to 1.
-    Returns:
-        env [type]: 环境
-        agent : 智能体
-    '''    
-    env = gym.make(cfg.env_name)  
-    env = CliffWalkingWapper(env)
-    env.seed(seed) # 设置随机种子
-    n_states = env.observation_space.n # 状态维度
-    n_actions = env.action_space.n # 动作维度
-    print(f"状态数：{n_states}，动作数：{n_actions}")
-    agent = QLearning(n_actions,cfg)
-    return env,agent
-if __name__ == "__main__":
-    cfg = get_args()
-    # 训练
-    env, agent = env_agent_config(cfg)
-    res_dic = train(cfg, env, agent)
-    make_dir(cfg.result_path, cfg.model_path)  
-    save_args(cfg) # save parameters
-    agent.save(path=cfg.model_path)  # save model
-    save_results(res_dic, tag='train',
-                 path=cfg.result_path)  
-    plot_rewards(res_dic['rewards'], cfg, tag="train")  
-    # 测试
-    env, agent = env_agent_config(cfg)
-    agent.load(path=cfg.model_path)  # 导入模型
-    res_dic = test(cfg, env, agent)
-    save_results(res_dic, tag='test',
-                 path=cfg.result_path)  # 保存结果
-    plot_rewards(res_dic['rewards'], cfg, tag="test")  # 画出结果
-
-        
-    

projects/codes/SAC/sac.py

@@ -1,4 +0,0 @@
-
-class SAC:
-    def __init__(self,n_actions,model,memory,cfg):
-        pass

projects/codes/scripts/DQN_task2.sh

@@ -0,0 +1,15 @@
+# run DQN on Acrobot-v1, not the best tuned parameters
+
+# source conda, if you are already in proper conda environment, then comment the codes util "conda activate easyrl" 
+if [ -f "$HOME/anaconda3/etc/profile.d/conda.sh" ]; then
+    echo "source file at ~/anaconda3/etc/profile.d/conda.sh"
+    source ~/anaconda3/etc/profile.d/conda.sh 
+elif [ -f "$HOME/opt/anaconda3/etc/profile.d/conda.sh" ]; then
+    echo "source file at ~/opt/anaconda3/etc/profile.d/conda.sh"
+    source ~/opt/anaconda3/etc/profile.d/conda.sh 
+else 
+    echo 'please manually config the conda source path'
+fi
+conda activate easyrl # easyrl here can be changed to another name of conda env that you have created
+codes_dir=$(dirname $(dirname $(readlink -f "$0"))) # "codes" path
+python $codes_dir/DQN/main.py --env_name Acrobot-v1 --train_eps 100 --epsilon_decay 1500 --lr 0.002 --memory_capacity 200000 --batch_size 128 --device cuda