update DDPG

codes/ddpg/.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "/Users/jj/anaconda3/envs/py37/bin/python"
+}

codes/ddpg/README.md

@@ -0,0 +1,26 @@
+
+python 3.7.9
+
+pytorch 1.6.0
+
+tensorboard 2.3.0 
+
+torchvision 0.7.0 
+
+train: 
+
+```python
+python main.py 
+```
+
+eval: 
+
+```python
+python main.py --train 0 
+```
+
+open tensorboard:
+
+```python
+tensorboard --logdir logs 
+```

codes/ddpg/main.py

@@ -5,63 +5,76 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-11 20:58:21
 @LastEditor: John
-LastEditTime: 2020-09-02 01:24:50
+LastEditTime: 2020-10-15 21:23:39
 @Discription: 
 @Environment: python 3.7.7
 '''
+from token import NUMBER
+from typing import Sequence
 import torch
 import gym
-
-from ddpg import DDPG
+from agent import DDPG
 from env import NormalizedActions
 from noise import OUNoise
-from plot import plot
-
+import os
+import numpy as np
 import argparse
+from torch.utils.tensorboard import SummaryWriter
+import datetime
+
+SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/'
+RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/result/"+SEQUENCE+'/'
 
 def get_args():
     '''模型建立好之后只需要在这里调参
     '''
     parser = argparse.ArgumentParser()
-
-    parser.add_argument("--gamma", default=0.99, type=float) # q-learning中的gamma
-    parser.add_argument("--critic_lr", default=1e-3, type=float) # critic学习率
+    parser.add_argument("--train", default=1, type=int)  # 1 表示训练，0表示只进行eval
+    parser.add_argument("--gamma", default=0.99,
+                        type=float)  # q-learning中的gamma
+    parser.add_argument("--critic_lr", default=1e-3, type=float)  # critic学习率
     parser.add_argument("--actor_lr", default=1e-4, type=float)
-
-    parser.add_argument("--memory_capacity", default=10000, type=int,help="capacity of Replay Memory")
-
-    parser.add_argument("--batch_size", default=128, type=int,help="batch size of memory sampling")
+    parser.add_argument("--memory_capacity", default=10000,
+                        type=int, help="capacity of Replay Memory")
+    parser.add_argument("--batch_size", default=128, type=int,
+                        help="batch size of memory sampling")
     parser.add_argument("--train_eps", default=200, type=int)
     parser.add_argument("--train_steps", default=200, type=int)
-    parser.add_argument("--eval_eps", default=200, type=int) # 训练的最大episode数目
-    parser.add_argument("--eval_steps", default=200, type=int) # 训练每个episode的长度
-    parser.add_argument("--target_update", default=4, type=int,help="when(every default 10 eisodes) to update target net ")
+    parser.add_argument("--eval_eps", default=200, type=int)  # 训练的最大episode数目
+    parser.add_argument("--eval_steps", default=200,
+                        type=int)  # 训练每个episode的长度
+    parser.add_argument("--target_update", default=4, type=int,
+                        help="when(every default 10 eisodes) to update target net ")
     config = parser.parse_args()
     return config
 
-def train():
-    cfg = get_args()
+
+def train(cfg):
+    print('Start to train ! \n')
     env = NormalizedActions(gym.make("Pendulum-v0"))
-    
+
     # 增加action噪声
     ou_noise = OUNoise(env.action_space)
-    
-    n_states  = env.observation_space.shape[0] 
-    n_actions = env.action_space.shape[0] 
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    agent=DDPG(n_states,n_actions,device="cpu", critic_lr=1e-3,
-                 actor_lr=1e-4, gamma=0.99, soft_tau=1e-2, memory_capacity=100000, batch_size=128)
 
-    rewards  = []
+    n_states = env.observation_space.shape[0]
+    n_actions = env.action_space.shape[0]
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    agent = DDPG(n_states, n_actions, device="cpu", critic_lr=1e-3,
+                 actor_lr=1e-4, gamma=0.99, soft_tau=1e-2, memory_capacity=100000, batch_size=128)
+    rewards = []
     moving_average_rewards = []
     ep_steps = []
-    for i_episode in range(1,cfg.train_eps+1):
-        state=env.reset()
+    log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/train/" + SEQUENCE
+    writer = SummaryWriter(log_dir)
+    for i_episode in range(1, cfg.train_eps+1):
+        state = env.reset()
         ou_noise.reset()
         ep_reward = 0
-        for i_step in range(1,cfg.train_steps+1):
-            action = agent.select_action(state)   
-            action = ou_noise.get_action(action, i_step) # 即paper中的random process
+        for i_step in range(1, cfg.train_steps+1):
+            action = agent.select_action(state)
+            action = ou_noise.get_action(
+                action, i_step)  # 即paper中的random process
             next_state, reward, done, _ = env.step(action)
             ep_reward += reward
             agent.memory.push(state, action, reward, next_state, done)
@@ -69,7 +82,8 @@ def train():
             state = next_state
             if done:
                 break
-        print('Episode:', i_episode, ' Reward: %i' % int(ep_reward),'n_steps:', i_step)
+        print('Episode:', i_episode, ' Reward: %i' %
+              int(ep_reward), 'n_steps:', i_step)
         ep_steps.append(i_step)
         rewards.append(ep_reward)
         if i_episode == 1:
@@ -77,54 +91,43 @@ def train():
         else:
             moving_average_rewards.append(
                 0.9*moving_average_rewards[-1]+0.1*ep_reward)
-    print('Complete！')
-    # 保存模型
-    import os
-    import numpy as np
-    save_path = os.path.dirname(__file__)+"/saved_model/"
-    if not os.path.exists(save_path):
-        os.mkdir(save_path)
-    agent.save_model(save_path+'checkpoint.pth')
-    # 存储reward等相关结果
-    output_path = os.path.dirname(__file__)+"/result/"
-    # 检测是否存在文件夹
-    if not os.path.exists(output_path):
-        os.mkdir(output_path)
-    np.save(output_path+"rewards.npy", rewards)
-    np.save(output_path+"moving_average_rewards.npy", moving_average_rewards)
-    np.save(output_path+"steps.npy", ep_steps)
-    plot(rewards)
-    plot(moving_average_rewards,ylabel="moving_average_rewards")
-    plot(ep_steps, ylabel="steps_of_each_episode")
-    
-def eval():
-    cfg = get_args()
-    env = NormalizedActions(gym.make("Pendulum-v0"))
-    
-    # 增加action噪声
-    ou_noise = OUNoise(env.action_space)
-    
-    n_states  = env.observation_space.shape[0] 
-    n_actions = env.action_space.shape[0] 
-    agent=DDPG(n_states,n_actions, critic_lr=1e-3,
-                 actor_lr=1e-4, gamma=0.99, soft_tau=1e-2, memory_capacity=100000, batch_size=128)
+        writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode)
+        writer.add_scalar('steps_of_each_episode',
+                          ep_steps[-1], i_episode)
+    writer.close()
+    print('Complete training！')
+    ''' 保存模型 '''
+    if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
+        os.mkdir(SAVED_MODEL_PATH)
+    agent.save_model(SAVED_MODEL_PATH+'checkpoint.pth')
+    '''存储reward等相关结果'''
+    if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
+        os.mkdir(RESULT_PATH)
+    np.save(RESULT_PATH+'rewards_train.npy', rewards)
+    np.save(RESULT_PATH+'moving_average_rewards_train.npy', moving_average_rewards)
+    np.save(RESULT_PATH+'steps_train.npy', ep_steps)
 
-    import os
-    save_path = os.path.dirname(__file__)+"/saved_model/"
-    if not os.path.exists(save_path):
-        os.mkdir(save_path)
-    agent.load_model(save_path+'checkpoint.pth')
+def eval(cfg, saved_model_path = SAVED_MODEL_PATH):
+    print('start to eval ! \n')
+    env = NormalizedActions(gym.make("Pendulum-v0"))
+    n_states = env.observation_space.shape[0]
+    n_actions = env.action_space.shape[0]
+    agent = DDPG(n_states, n_actions, critic_lr=1e-3,
+                 actor_lr=1e-4, gamma=0.99, soft_tau=1e-2, memory_capacity=100000, batch_size=128)
+    agent.load_model(saved_model_path+'checkpoint.pth')
     rewards = []
     moving_average_rewards = []
     ep_steps = []
+    log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/eval/" + SEQUENCE
+    writer = SummaryWriter(log_dir)
     for i_episode in range(1, cfg.eval_eps+1):
-        state = env.reset() # reset环境状态
+        state = env.reset()  # reset环境状态
         ep_reward = 0
         for i_step in range(1, cfg.eval_steps+1):
-            action = agent.select_action(state) # 根据当前环境state选择action
-            next_state, reward, done, _ = env.step(action) # 更新环境参数
+            action = agent.select_action(state)  # 根据当前环境state选择action
+            next_state, reward, done, _ = env.step(action)  # 更新环境参数
             ep_reward += reward
-            state = next_state # 跳转到下一个状态
+            state = next_state  # 跳转到下一个状态
             if done:
                 break
         print('Episode:', i_episode, ' Reward: %i' %
@@ -137,11 +140,22 @@ def eval():
         else:
             moving_average_rewards.append(
                 0.9*moving_average_rewards[-1]+0.1*ep_reward)
-    plot(rewards,save_fig=False)
-    plot(moving_average_rewards, ylabel="moving_average_rewards",save_fig=False)
-    plot(ep_steps, ylabel="steps_of_each_episode",save_fig=False)
-    
+        writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode)
+        writer.add_scalar('steps_of_each_episode',
+                          ep_steps[-1], i_episode)
+    writer.close()
+    '''存储reward等相关结果'''
+    if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
+        os.mkdir(RESULT_PATH)
+    np.save(RESULT_PATH+'rewards_eval.npy', rewards)
+    np.save(RESULT_PATH+'moving_average_rewards_eval.npy', moving_average_rewards)
+    np.save(RESULT_PATH+'steps_eval.npy', ep_steps)
 
 if __name__ == "__main__":
-    # train()
-    eval()
+    cfg = get_args()
+    if cfg.train:
+        train(cfg)
+        eval(cfg)
+    else:
+        model_path = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"
+        eval(cfg,saved_model_path=model_path)

codes/ddpg/plot.py

@@ -5,17 +5,16 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-11 16:30:09
 @LastEditor: John
-LastEditTime: 2020-09-02 01:20:03
+LastEditTime: 2020-10-15 21:32:05
 @Discription: 
 @Environment: python 3.7.7
 '''
 import matplotlib.pyplot as plt
-import pandas as pd
-import seaborn as sns; 
+import seaborn as sns
 import numpy as np
 import os 
 
-def plot(item,ylabel='rewards',save_fig = True):
+def plot_results(item,ylabel='rewards_train', save_fig = True):
     '''plot using searborn to plot 
     '''
     sns.set()
@@ -24,25 +23,24 @@ def plot(item,ylabel='rewards',save_fig = True):
     plt.title(ylabel+' of DDPG') 
     plt.ylabel(ylabel)
     plt.xlabel('episodes')
-    plt.savefig(os.path.dirname(__file__)+"/result/"+ylabel+".png")
+    if save_fig:
+        plt.savefig(os.path.dirname(__file__)+"/result/"+ylabel+".png")
     plt.show()
 
-# def plot(item,ylabel='rewards'):
-#     
-#     df = pd.DataFrame(dict(time=np.arange(len(item)),value=item))
-#     g = sns.relplot(x="time", y="value", kind="line", data=df)
-#     # g.fig.autofmt_xdate()
-#     # sns.lineplot(time=time, data=item, color="r", condition="behavior_cloning")
-#     # # sns.tsplot(time=time, data=x2, color="b", condition="dagger")
-#     # plt.ylabel("Reward")
-#     # plt.xlabel("Iteration Number")
-#     # plt.title("Imitation Learning")
-
-    # plt.show()
 if __name__ == "__main__":
 
-    output_path = os.path.dirname(__file__)+"/result/"
-    rewards=np.load(output_path+"rewards.npy", )
-    moving_average_rewards=np.load(output_path+"moving_average_rewards.npy",)
-    plot(rewards)
-    plot(moving_average_rewards,ylabel='moving_average_rewards')
+    output_path = os.path.split(os.path.abspath(__file__))[0]+"/result/"
+    tag = 'train'
+    rewards=np.load(output_path+"rewards_"+tag+".npy", )
+    moving_average_rewards=np.load(output_path+"moving_average_rewards_"+tag+".npy",)
+    steps=np.load(output_path+"steps_"+tag+".npy")
+    plot_results(rewards)
+    plot_results(moving_average_rewards,ylabel='moving_average_rewards_'+tag)
+    plot_results(steps,ylabel='steps_'+tag)
+    tag = 'eval'
+    rewards=np.load(output_path+"rewards_"+tag+".npy", )
+    moving_average_rewards=np.load(output_path+"moving_average_rewards_"+tag+".npy",)
+    steps=np.load(output_path+"steps_"+tag+".npy")
+    plot_results(rewards,ylabel='rewards_'+tag)
+    plot_results(moving_average_rewards,ylabel='moving_average_rewards_'+tag)
+    plot_results(steps,ylabel='steps_'+tag)

codes/ddpg/utils.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-10-15 21:31:19
+LastEditor: John
+LastEditTime: 2020-10-15 21:31:25
+Discription: 
+Environment: 
+'''
+import os
+import numpy as np
+RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/result/"+SEQUENCE+'/'
+
+def save_results(rewards,moving_average_rewards,ep_steps,path=RESULT_PATH):
+    if not os.path.exists(path): # 检测是否存在文件夹
+            os.mkdir(path)
+    np.save(RESULT_PATH+'rewards_train.npy', rewards)
+    np.save(RESULT_PATH+'moving_average_rewards_train.npy', moving_average_rewards)
+    np.save(RESULT_PATH+'steps_train.npy',ep_steps )