Merge branch 'master' of github.com:datawhalechina/easy-rl

2022-08-23 16:20:16 +08:00
parent a0b4a68ad1 ad65dd17cd
commit d62a492e05
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--- a/projects/README.md
+++ b/projects/README.md
@@ -1,6 +1,6 @@
 ## 0、写在前面

-本项目用于学习RL基础算法，主要面向对象为RL初学者、需要结合RL的非专业学习者，尽量做到: **(中文)注释详细**，**结构清晰**。
+本项目用于学习RL基础算法，主要面向对象为RL初学者、需要结合RL的非专业学习者，尽量做到: **注释详细**，**结构清晰**。

 注意本项目为实战内容，建议首先掌握相关算法的一些理论基础，再来享用本项目，理论教程参考本人参与编写的[蘑菇书](https://github.com/datawhalechina/easy-rl)。

@@ -10,25 +10,65 @@

 项目内容主要包含以下几个部分：
 * [Jupyter Notebook](./notebooks/)：使用Notebook写的算法，有比较详细的实战引导，推荐新手食用
-* [codes](./assets/)：这些是基于Python脚本写的算法，风格比较接近实际项目的写法，推荐有一定代码基础的人阅读，下面会说明其具体的一些架构
+* [codes](./codes/)：这些是基于Python脚本写的算法，风格比较接近实际项目的写法，推荐有一定代码基础的人阅读，下面会说明其具体的一些架构
 * [parl](./PARL/)：应业务需求，写了一些基于百度飞浆平台和```parl```模块的RL实例
 * [附件](./assets/)：目前包含强化学习各算法的中文伪代码


 [codes](./assets/)结构主要分为以下几个脚本：
 * ```[algorithm_name].py```：即保存算法的脚本，例如```dqn.py```，每种算法都会有一定的基础模块，例如```Replay Buffer```、```MLP```(多层感知机)等等；
-* ```task.py```: 即保存任务的脚本，基本包括基于```argparse```模块的参数，训练以及测试函数等等；
+* ```task.py```: 即保存任务的脚本，基本包括基于```argparse```模块的参数，训练以及测试函数等等，其中训练函数即```train```遵循伪代码而设计，想读懂代码可从该函数入手；
 * ```utils.py```：该脚本用于保存诸如存储结果以及画图的软件，在实际项目或研究中，推荐大家使用```Tensorboard```来保存结果，然后使用诸如```matplotlib```以及```seabron```来进一步画图。
+## 2、算法列表

-## 2、运行环境
+注：点击对应的名称会跳到[codes](./codes/)下对应的算法中，其他版本还请读者自行翻阅

-python 3.7、pytorch 1.6.0-1.9.0、gym 0.21.0
+|         算法名称          |                           参考文献                           | 环境 | 备注 |
+| :-----------------------: | :----------------------------------------------------------: | :--: | :--: |
+|                           |                                                              |      |      |
+|          DQN-CNN          |                                                              |      | 待更 |
+|   [SoftQ](codes/SoftQ)    |  [Soft Q-learning paper](https://arxiv.org/abs/1702.08165)   |      |      |
+|     [SAC](codes/SAC)      |      [SAC paper](https://arxiv.org/pdf/1812.05905.pdf)       |      |      |
+| [SAC-Discrete](codes/SAC) |  [SAC-Discrete paper](https://arxiv.org/pdf/1910.07207.pdf)  |      |      |
+|           SAC-V           |       [SAC-V paper](https://arxiv.org/abs/1801.01290)        |      |      |
+|           DSAC            | [DSAC paper](https://paperswithcode.com/paper/addressing-value-estimation-errors-in) |      | 待更 |
+
+
+
+## 3、运行环境
+
+Python 3.7、PyTorch 1.10.0、Gym 0.21.0

 在项目根目录下执行以下命令复现环境：
 ```bash
 pip install -r requirements.txt
 ```
-## 3、使用说明
+如果需要使用CUDA，则需另外安装```cudatoolkit```，推荐```10.2```或者```11.3```版本的CUDA，如下：
+```bash
+conda install cudatoolkit=11.3 -c pytorch
+```
+如果conda需要镜像加速安装的话，点击[该清华镜像链接](https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/)，选择对应的操作系统，比如```win-64```，然后复制链接，执行如下命令：
+```bash
+conda install cudatoolkit=11.3 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/win-64/
+```
+执行以下Python脚本，如果返回True说明cuda安装成功:
+```python
+import torch
+print(torch.cuda.is_available())
+```
+如果还是不成功，可以使用pip安装：
+```bash
+pip install torch==1.10.0+cu113 torchvision==0.11.0+cu113 torchaudio==0.10.0 --extra-index-url https://download.pytorch.org/whl/cu113
+```
+## 4、使用说明

-直接运行带有```train```的py文件或ipynb文件会进行训练默认的任务；  
-也可以运行带有```task```的py文件训练不同的任务
+对于[codes](./codes/)：
+* 运行带有task的py脚本
+
+对于[Jupyter Notebook](./notebooks/)：
+
+* 直接运行对应的ipynb文件就行
+
+## 5、友情说明
+
+推荐使用VS Code做项目，入门可参考[VSCode上手指南](https://blog.csdn.net/JohnJim0/article/details/126366454)
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-[] []\TU/SongtiSCLight(0)/m/n/10.95 计 算 实 际 的 $\OML/cmm/m/it/10.95 Q$ \TU/SongtiSCLight(0)/m/n/10.95 值，|  即 $\OML/cmm/m/it/10.95 y[] \OT1/cmr/m/n/10.95 = []$ 
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+[] []\TU/SongtiSCLight(0)/m/n/10.95 计 算 实 际 的 $\OML/cmm/m/it/10.95 Q$ \TU/SongtiSCLight(0)/m/n/10.95 值，|  即 $\OML/cmm/m/it/10.95 y[] \OT1/cmr/m/n/10.95 =
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-] (./pseudocodes.aux) ) 
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+[][]$[]\OML/cmm/m/it/9 J[]\OT1/cmr/m/n/9 (\OML/cmm/m/it/9 ^^^\OT1/cmr/m/n/9 ) = \OMS/cmsy/m/n/9 r[]\OML/cmm/m/it/9 ^^K [] [] \OT1/cmr/m/n/9 + [] \OMS/cmsy/m/n/9 r[]\OML/cmm/m/it/9 f[] []$\TU/lmr/m/n/9 ,$[][] \OT1/cmr/m/n/9 =
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+Output written on pseudocodes.pdf (8 pages).
--- a/projects/assets/pseudocodes/pseudocodes.out
+++ b/projects/assets/pseudocodes/pseudocodes.out
@@ -0,0 +1,7 @@
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--- a/projects/assets/pseudocodes/pseudocodes.pdf
+++ b/projects/assets/pseudocodes/pseudocodes.pdf
--- a/projects/assets/pseudocodes/pseudocodes.synctex.gz
+++ b/projects/assets/pseudocodes/pseudocodes.synctex.gz
--- a/projects/assets/pseudocodes/pseudocodes.tex
+++ b/projects/assets/pseudocodes/pseudocodes.tex
@@ -4,17 +4,96 @@
 \usepackage{algorithmic}
 \usepackage{amssymb}
 \usepackage{amsmath}
-
-
+\usepackage{hyperref}
+% \usepackage[hidelinks]{hyperref} 去除超链接的红色框
+\usepackage{setspace}
+\usepackage{titlesec}
+\usepackage{float} % 调用该包能够使用[H]
+% \pagestyle{plain} % 去除页眉，但是保留页脚编号，都去掉plain换empty
 \begin{document}
-
-\begin{algorithm}
+\tableofcontents % 目录，注意要运行两下或者vscode保存两下才能显示
+% \singlespacing
+\clearpage
+\section{模版备用}
+\begin{algorithm}[H] % [H]固定位置
+    \floatname{algorithm}{{算法}} 
+	\renewcommand{\thealgorithm}{} % 去掉算法标号
+	\caption{} 
+	\begin{algorithmic}[1] % [1]显示步数
+		\STATE 测试
+	\end{algorithmic}
+\end{algorithm}
+\clearpage
+\section{Q learning算法}
+\begin{algorithm}[H] % [H]固定位置
+    \floatname{algorithm}{{Q-learning算法}\footnotemark[1]}
+	\renewcommand{\thealgorithm}{} % 去掉算法标号
+	\caption{} 
+	\begin{algorithmic}[1] % [1]显示步数
+		\STATE 初始化Q表$Q(s,a)$为任意值，但其中$Q(s_{terminal},)=0$，即终止状态对应的Q值为0
+		\FOR {回合数 = $1,M$}
+			\STATE 重置环境，获得初始状态$s_1$
+			\FOR {时步 = $1,t$}
+				\STATE 根据$\varepsilon-greedy$策略采样动作$a_t$
+				\STATE 环境根据$a_t$反馈奖励$r_t$和下一个状态$s_{t+1}$
+				\STATE {\bfseries 更新策略：}
+				\STATE $Q(s_t,a_t) \leftarrow Q(s_t,a_t)+\alpha[r_t+\gamma\max _{a}Q(s_{t+1},a)-Q(s_t,a_t)]$
+				\STATE 更新状态$s_{t+1} \leftarrow s_t$
+			\ENDFOR
+		\ENDFOR
+	\end{algorithmic}
+\end{algorithm}
+\footnotetext[1]{Reinforcement Learning: An Introduction}
+\clearpage
+\section{Sarsa算法}
+\begin{algorithm}[H] % [H]固定位置
+    \floatname{algorithm}{{Sarsa算法}\footnotemark[1]}
+	\renewcommand{\thealgorithm}{} % 去掉算法标号
+	\caption{} 
+	\begin{algorithmic}[1] % [1]显示步数
+		\STATE 初始化Q表$Q(s,a)$为任意值，但其中$Q(s_{terminal},)=0$，即终止状态对应的Q值为0
+		\FOR {回合数 = $1,M$}
+			\STATE 重置环境，获得初始状态$s_1$
+			\STATE 根据$\varepsilon-greedy$策略采样初始动作$a_1$
+			\FOR {时步 = $1,t$}
+				\STATE 环境根据$a_t$反馈奖励$r_t$和下一个状态$s_{t+1}$
+				\STATE 根据$\varepsilon-greedy$策略$s_{t+1}$和采样动作$a_{t+1}$
+				\STATE {\bfseries 更新策略：}
+				\STATE $Q(s_t,a_t) \leftarrow Q(s_t,a_t)+\alpha[r_t+\gamma Q(s_{t+1},a_{t+1})-Q(s_t,a_t)]$
+				\STATE 更新状态$s_{t+1} \leftarrow s_t$
+				\STATE 更新动作$a_{t+1} \leftarrow a_t$
+			\ENDFOR
+		\ENDFOR
+	\end{algorithmic}
+\end{algorithm}
+\footnotetext[1]{Reinforcement Learning: An Introduction}
+\clearpage
+\section{Policy Gradient算法}
+\begin{algorithm}[H] % [H]固定位置
+    \floatname{algorithm}{{REINFORCE算法：Monte-Carlo Policy Gradient}\footnotemark[1]} 
+	\renewcommand{\thealgorithm}{} % 去掉算法标号
+	\caption{} 
+	\begin{algorithmic}[1] % [1]显示步数
+		\STATE 初始化策略参数$\boldsymbol{\theta} \in \mathbb{R}^{d^{\prime}}($ e.g., to $\mathbf{0})$
+		\FOR {回合数 = $1,M$}
+			\STATE 根据策略$\pi(\cdot \mid \cdot, \boldsymbol{\theta})$采样一个(或几个)回合的transition
+			\FOR {时步 = $1,t$}
+				\STATE 计算回报$G \leftarrow \sum_{k=t+1}^{T} \gamma^{k-t-1} R_{k}$
+				\STATE 更新策略$\boldsymbol{\theta} \leftarrow {\boldsymbol{\theta}+\alpha \gamma^{t}} G \nabla \ln \pi\left(A_{t} \mid S_{t}, \boldsymbol{\theta}\right)$
+			\ENDFOR
+		\ENDFOR
+	\end{algorithmic}
+\end{algorithm}
+\footnotetext[1]{Reinforcement Learning: An Introduction}
+\clearpage
+\section{DQN算法}
+\begin{algorithm}[H] % [H]固定位置
    \floatname{algorithm}{{DQN算法}}  
    \renewcommand{\thealgorithm}{} % 去掉算法标号
 	\caption{} 
    \renewcommand{\algorithmicrequire}{\textbf{输入:}}  
    \renewcommand{\algorithmicensure}{\textbf{输出:}} 
-	\begin{algorithmic}
+	\begin{algorithmic}[1]
 		% \REQUIRE $n \geq 0 \vee x \neq 0$ % 输入
 		% \ENSURE $y = x^n$ % 输出
 		\STATE 初始化策略网络参数$\theta$ % 初始化
@@ -24,40 +103,85 @@
 			\STATE 重置环境，获得初始状态$s_t$
 			\FOR {时步 = $1,t$}
 				\STATE 根据$\varepsilon-greedy$策略采样动作$a_t$
-				\STATE 环境根据$a_t$反馈奖励$s_t$和下一个状态$s_{t+1}$
+				\STATE 环境根据$a_t$反馈奖励$r_t$和下一个状态$s_{t+1}$
 				\STATE 存储transition即$(s_t,a_t,r_t,s_{t+1})$到经验回放$D$中
 				\STATE 更新环境状态$s_{t+1} \leftarrow s_t$
 				\STATE {\bfseries 更新策略：}
 				\STATE 从$D$中采样一个batch的transition
 				\STATE 计算实际的$Q$值，即$y_{j}= \begin{cases}r_{j} & \text {对于终止状态} s_{j+1} \\ r_{j}+\gamma \max _{a^{\prime}} Q\left(s_{j+1}, a^{\prime} ; \theta\right) & \text {对于非终止状态} s_{j+1}\end{cases}$
 				\STATE 对损失 $\left(y_{j}-Q\left(s_{j}, a_{j} ; \theta\right)\right)^{2}$关于参数$\theta$做随机梯度下降
-				\STATE 每$C$步复制参数$\hat{Q} \leftarrow Q$
 			\ENDFOR
+			\STATE 每$C$个回合复制参数$\hat{Q}\leftarrow Q$(此处也可像原论文中放到小循环中改成每$C$步，但没有每$C$个回合稳定)
 		\ENDFOR
 	\end{algorithmic}
 \end{algorithm}
-
 \clearpage

-\begin{algorithm}
+\section{SoftQ算法}
+\begin{algorithm}[H]
    \floatname{algorithm}{{SoftQ算法}}  
    \renewcommand{\thealgorithm}{} % 去掉算法标号
 	\caption{}  
-	\begin{algorithmic}
+	\begin{algorithmic}[1]
 		\STATE 初始化参数$\theta$和$\phi$% 初始化
 		\STATE 复制参数$\bar{\theta} \leftarrow \theta, \bar{\phi} \leftarrow \phi$
 		\STATE 初始化经验回放$D$
 		\FOR {回合数 = $1,M$}
 			\FOR {时步 = $1,t$}
-				\STATE 根据$a_{t} \leftarrow f^{\phi}\left(\xi ; \mathbf{s}_{t}\right)$采样动作，其中$\xi \sim \mathcal{N}(\mathbf{0}, \boldsymbol{I})$
+				\STATE 根据$\mathbf{a}_{t} \leftarrow f^{\phi}\left(\xi ; \mathbf{s}_{t}\right)$采样动作，其中$\xi \sim \mathcal{N}(\mathbf{0}, \boldsymbol{I})$
 				\STATE 环境根据$a_t$反馈奖励$s_t$和下一个状态$s_{t+1}$
 				\STATE 存储transition即$(s_t,a_t,r_t,s_{t+1})$到经验回放$D$中
 				\STATE 更新环境状态$s_{t+1} \leftarrow s_t$
-				\STATE 待完善
+				\STATE {\bfseries 更新soft Q函数参数：}
+				\STATE 对于每个$s^{(i)}_{t+1}$采样$\left\{\mathbf{a}^{(i, j)}\right\}_{j=0}^{M} \sim q_{\mathbf{a}^{\prime}}$
+				\STATE 计算empirical soft values $V_{\mathrm{soft}}^{\theta}\left(\mathbf{s}_{t}\right)$\footnotemark[1]
+				\STATE 计算empirical gradient $J_{Q}(\theta)$\footnotemark[2]
+				\STATE 根据$J_{Q}(\theta)$使用ADAM更新参数$\theta$
+				\STATE {\bfseries 更新策略：}
+				\STATE  对于每个$s^{(i)}_{t}$采样$\left\{\xi^{(i, j)}\right\}_{j=0}^{M} \sim \mathcal{N}(\mathbf{0}, \boldsymbol{I})$
+				\STATE 计算$\mathbf{a}_{t}^{(i, j)}=f^{\phi}\left(\xi^{(i, j)}, \mathbf{s}_{t}^{(i)}\right)$
+				\STATE 使用经验估计计算$\Delta f^{\phi}\left(\cdot ; \mathbf{s}_{t}\right)$\footnotemark[3]
+				\STATE 计算经验估计$\frac{\partial J_{\pi}\left(\phi ; \mathbf{s}_{t}\right)}{\partial \phi} \propto \mathbb{E}_{\xi}\left[\Delta f^{\phi}\left(\xi ; \mathbf{s}_{t}\right) \frac{\partial f^{\phi}\left(\xi ; \mathbf{s}_{t}\right)}{\partial \phi}\right]$，即$\hat{\nabla}_{\phi} J_{\pi}$
+				\STATE 根据$\hat{\nabla}_{\phi} J_{\pi}$使用ADAM更新参数$\phi$
+				\STATE 
 			\ENDFOR
-		\ENDFOR
-			
+			\STATE 每$C$个回合复制参数$\bar{\theta} \leftarrow \theta, \bar{\phi} \leftarrow \phi$
+		\ENDFOR	
 	\end{algorithmic}
 \end{algorithm}
-
+\footnotetext[1]{$V_{\mathrm{soft}}^{\theta}\left(\mathbf{s}_{t}\right)=\alpha \log \mathbb{E}_{q_{\mathbf{a}^{\prime}}}\left[\frac{\exp \left(\frac{1}{\alpha} Q_{\mathrm{soft}}^{\theta}\left(\mathbf{s}_{t}, \mathbf{a}^{\prime}\right)\right)}{q_{\mathbf{a}^{\prime}}\left(\mathbf{a}^{\prime}\right)}\right]$}
+\footnotetext[2]{$J_{Q}(\theta)=\mathbb{E}_{\mathbf{s}_{t} \sim q_{\mathbf{s}_{t}}, \mathbf{a}_{t} \sim q_{\mathbf{a}_{t}}}\left[\frac{1}{2}\left(\hat{Q}_{\mathrm{soft}}^{\bar{\theta}}\left(\mathbf{s}_{t}, \mathbf{a}_{t}\right)-Q_{\mathrm{soft}}^{\theta}\left(\mathbf{s}_{t}, \mathbf{a}_{t}\right)\right)^{2}\right]$}
+\footnotetext[3]{$\begin{aligned} \Delta f^{\phi}\left(\cdot ; \mathbf{s}_{t}\right)=& \mathbb{E}_{\mathbf{a}_{t} \sim \pi^{\phi}}\left[\left.\kappa\left(\mathbf{a}_{t}, f^{\phi}\left(\cdot ; \mathbf{s}_{t}\right)\right) \nabla_{\mathbf{a}^{\prime}} Q_{\mathrm{soft}}^{\theta}\left(\mathbf{s}_{t}, \mathbf{a}^{\prime}\right)\right|_{\mathbf{a}^{\prime}=\mathbf{a}_{t}}\right.\\ &\left.+\left.\alpha \nabla_{\mathbf{a}^{\prime}} \kappa\left(\mathbf{a}^{\prime}, f^{\phi}\left(\cdot ; \mathbf{s}_{t}\right)\right)\right|_{\mathbf{a}^{\prime}=\mathbf{a}_{t}}\right] \end{aligned}$}
+\clearpage
+\section{SAC算法}
+\begin{algorithm}[H] % [H]固定位置
+    \floatname{algorithm}{{Soft Actor Critic算法}}  
+    \renewcommand{\thealgorithm}{} % 去掉算法标号
+	\caption{} 
+	\begin{algorithmic}[1]
+		\STATE 初始化两个Actor的网络参数$\theta_1,\theta_2$以及一个Critic网络参数$\phi$ % 初始化
+		\STATE 复制参数到目标网络$\bar{\theta_1} \leftarrow \theta_1,\bar{\theta_2} \leftarrow \theta_2,$
+		\STATE 初始化经验回放$D$
+		\FOR {回合数 = $1,M$}
+			\STATE 重置环境，获得初始状态$s_t$
+			\FOR {时步 = $1,t$}
+				\STATE 根据$\boldsymbol{a}_{t} \sim \pi_{\phi}\left(\boldsymbol{a}_{t} \mid \mathbf{s}_{t}\right)$采样动作$a_t$
+				\STATE 环境反馈奖励和下一个状态，$\mathbf{s}_{t+1} \sim p\left(\mathbf{s}_{t+1} \mid \mathbf{s}_{t}, \mathbf{a}_{t}\right)$
+				\STATE 存储transition到经验回放中，$\mathcal{D} \leftarrow \mathcal{D} \cup\left\{\left(\mathbf{s}_{t}, \mathbf{a}_{t}, r\left(\mathbf{s}_{t}, \mathbf{a}_{t}\right), \mathbf{s}_{t+1}\right)\right\}$
+				\STATE 更新环境状态$s_{t+1} \leftarrow s_t$
+				\STATE {\bfseries 更新策略：}
+				\STATE 更新$Q$函数，$\theta_{i} \leftarrow \theta_{i}-\lambda_{Q} \hat{\nabla}_{\theta_{i}} J_{Q}\left(\theta_{i}\right)$ for $i \in\{1,2\}$\footnotemark[1]\footnotemark[2]
+				\STATE 更新策略权重，$\phi \leftarrow \phi-\lambda_{\pi} \hat{\nabla}_{\phi} J_{\pi}(\phi)$ \footnotemark[3]
+				\STATE 调整temperature，$\alpha \leftarrow \alpha-\lambda \hat{\nabla}_{\alpha} J(\alpha)$ \footnotemark[4]
+				\STATE 更新目标网络权重，$\bar{\theta}_{i} \leftarrow \tau \theta_{i}+(1-\tau) \bar{\theta}_{i}$ for $i \in\{1,2\}$
+			\ENDFOR
+		\ENDFOR
+	\end{algorithmic}
+	
+\end{algorithm}
+\footnotetext[1]{$J_{Q}(\theta)=\mathbb{E}_{\left(\mathbf{s}_{t}, \mathbf{a}_{t}\right) \sim \mathcal{D}}\left[\frac{1}{2}\left(Q_{\theta}\left(\mathbf{s}_{t}, \mathbf{a}_{t}\right)-\left(r\left(\mathbf{s}_{t}, \mathbf{a}_{t}\right)+\gamma \mathbb{E}_{\mathbf{s}_{t+1} \sim p}\left[V_{\bar{\theta}}\left(\mathbf{s}_{t+1}\right)\right]\right)\right)^{2}\right]$}
+\footnotetext[2]{$\hat{\nabla}_{\theta} J_{Q}(\theta)=\nabla_{\theta} Q_{\theta}\left(\mathbf{a}_{t}, \mathbf{s}_{t}\right)\left(Q_{\theta}\left(\mathbf{s}_{t}, \mathbf{a}_{t}\right)-\left(r\left(\mathbf{s}_{t}, \mathbf{a}_{t}\right)+\gamma\left(Q_{\bar{\theta}}\left(\mathbf{s}_{t+1}, \mathbf{a}_{t+1}\right)-\alpha \log \left(\pi_{\phi}\left(\mathbf{a}_{t+1} \mid \mathbf{s}_{t+1}\right)\right)\right)\right)\right.$}
+\footnotetext[3]{$\hat{\nabla}_{\phi} J_{\pi}(\phi)=\nabla_{\phi} \alpha \log \left(\pi_{\phi}\left(\mathbf{a}_{t} \mid \mathbf{s}_{t}\right)\right)+\left(\nabla_{\mathbf{a}_{t}} \alpha \log \left(\pi_{\phi}\left(\mathbf{a}_{t} \mid \mathbf{s}_{t}\right)\right)-\nabla_{\mathbf{a}_{t}} Q\left(\mathbf{s}_{t}, \mathbf{a}_{t}\right)\right) \nabla_{\phi} f_{\phi}\left(\epsilon_{t} ; \mathbf{s}_{t}\right)$,$\mathbf{a}_{t}=f_{\phi}\left(\epsilon_{t} ; \mathbf{s}_{t}\right)$}
+\footnotetext[4]{$J(\alpha)=\mathbb{E}_{\mathbf{a}_{t} \sim \pi_{t}}\left[-\alpha \log \pi_{t}\left(\mathbf{a}_{t} \mid \mathbf{s}_{t}\right)-\alpha \overline{\mathcal{H}}\right]$}
+\clearpage
 \end{document}
--- a/projects/assets/pseudocodes/pseudocodes.toc
+++ b/projects/assets/pseudocodes/pseudocodes.toc
@@ -0,0 +1,7 @@
+\contentsline {section}{\numberline {1}模版备用}{2}{section.1}%
+\contentsline {section}{\numberline {2}Q learning算法}{3}{section.2}%
+\contentsline {section}{\numberline {3}Sarsa算法}{4}{section.3}%
+\contentsline {section}{\numberline {4}Policy Gradient算法}{5}{section.4}%
+\contentsline {section}{\numberline {5}DQN算法}{6}{section.5}%
+\contentsline {section}{\numberline {6}SoftQ算法}{7}{section.6}%
+\contentsline {section}{\numberline {7}SAC算法}{8}{section.7}%
--- a/projects/codes/DQN/README.md
+++ b/projects/codes/DQN/README.md
@@ -1,218 +0,0 @@
-# DQN
-
-## 原理简介
-
-DQN是Q-leanning算法的优化和延伸，Q-leaning中使用有限的Q表存储值的信息，而DQN中则用神经网络替代Q表存储信息，这样更适用于高维的情况，相关知识基础可参考[datawhale李宏毅笔记-Q学习](https://datawhalechina.github.io/easy-rl/#/chapter6/chapter6)。
-
-论文方面主要可以参考两篇，一篇就是2013年谷歌DeepMind团队的[Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)，一篇是也是他们团队后来在Nature杂志上发表的[Human-level control through deep reinforcement learning](https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf)。后者在算法层面增加target q-net，也可以叫做Nature DQN。
-
-Nature DQN使用了两个Q网络，一个当前Q网络𝑄用来选择动作，更新模型参数，另一个目标Q网络𝑄′用于计算目标Q值。目标Q网络的网络参数不需要迭代更新，而是每隔一段时间从当前Q网络𝑄复制过来，即延时更新，这样可以减少目标Q值和当前的Q值相关性。
-
-要注意的是，两个Q网络的结构是一模一样的。这样才可以复制网络参数。Nature DQN和[Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)相比，除了用一个新的相同结构的目标Q网络来计算目标Q值以外，其余部分基本是完全相同的。细节也可参考[强化学习（九）Deep Q-Learning进阶之Nature DQN](https://www.cnblogs.com/pinard/p/9756075.html)。
-
-https://blog.csdn.net/JohnJim0/article/details/109557173)
-
-## 伪代码
-
-<img src="assets/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0pvaG5KaW0w,size_16,color_FFFFFF,t_70.png" alt="img" style="zoom:50%;" />
-
-## 代码实现
-
-### RL接口
-
-首先是强化学习训练的基本接口，即通用的训练模式：
-```python
-for i_episode in range(MAX_EPISODES):
-	state = env.reset() # reset环境状态
-	for i_step in range(MAX_STEPS):
-    action = agent.choose_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
-    agent.memory.push(state, action, reward, next_state, done) # 将state等这些transition存入memory
-    agent.update() # 每步更新网络
-    state = next_state # 跳转到下一个状态
-    if done:
-    	break        
-```
-每个episode加一个MAX_STEPS，也可以使用while not done, 加这个max_steps有时是因为比如gym环境训练目标就是在200个step下达到200的reward，或者是当完成一个episode的步数较多时也可以设置，基本流程跟所有伪代码一致，如下：
-1. agent选择动作
-2. 环境根据agent的动作反馈出next_state和reward
-3. agent进行更新，如有memory就会将transition(包含state，reward，action等)存入memory中
-4. 跳转到下一个状态
-5. 如果done了，就跳出循环，进行下一个episode的训练。
-
-想要实现完整的算法还需要创建Qnet，Replaybuffer等类
-
-### 两个Q网络
-
-上文讲了Nature DQN中有两个Q网络，一个是policy_net，一个是延时更新的target_net，两个网络的结构是一模一样的，如下(见```model.py```)，注意DQN使用的Qnet就是全连接网络即FCH：
-```python
-import torch.nn as nn
-import torch.nn.functional as F
-
-class FCN(nn.Module):
-    def __init__(self, n_states=4, n_actions=18):
-        """ 初始化q网络，为全连接网络
-            n_states: 输入的feature即环境的state数目
-            n_actions: 输出的action总个数
-        """
-        super(FCN, self).__init__()
-        self.fc1 = nn.Linear(n_states, 128) # 输入层
-        self.fc2 = nn.Linear(128, 128) # 隐藏层
-        self.fc3 = nn.Linear(128, n_actions) # 输出层
-        
-    def forward(self, x):
-        # 各层对应的激活函数
-        x = F.relu(self.fc1(x)) 
-        x = F.relu(self.fc2(x))
-        return self.fc3(x)
-```
-输入为n_states，输出为n_actions，包含一个128维度的隐藏层，这里根据需要可增加隐藏层维度和数量，然后一般使用relu激活函数，这里跟深度学习的网路设置是一样的。
-
-### Replay Buffer
-
-然后就是Replay Memory了，其作用主要是是克服经验数据的相关性（correlated data）和非平稳分布（non-stationary distribution）问题，实现如下(见```memory.py```)：
-
-```python
-import random
-import numpy as np
-
-class ReplayBuffer:
-    
-    def __init__(self, capacity):
-        self.capacity = capacity
-        self.buffer = []
-        self.position = 0
-    
-    def push(self, state, action, reward, next_state, done):
-        if len(self.buffer) < self.capacity:
-            self.buffer.append(None)
-        self.buffer[self.position] = (state, action, reward, next_state, done)
-        self.position = (self.position + 1) % self.capacity
-    
-    def sample(self, batch_size):
-        batch = random.sample(self.buffer, batch_size)
-        state, action, reward, next_state, done =  zip(*batch)
-        return state, action, reward, next_state, done
-    
-    def __len__(self):
-        return len(self.buffer)
-```
-
-参数capacity表示buffer的容量，主要包括push和sample两个步骤，push是将transitions放到memory中，sample是从memory随机抽取一些transition。
-
-### Agent类
-
-在```agent.py```中我们定义强化学习算法类，包括```choose_action```(选择动作，使用e-greedy策略时会多一个```predict```函数，下面会将到)和```update```(更新)等函数。
-
-在类中建立两个网络，以及optimizer和memory，
-
-```python
-self.policy_net = MLP(n_states, n_actions,hidden_dim=cfg.hidden_dim).to(self.device)
-self.target_net = MLP(n_states, n_actions,hidden_dim=cfg.hidden_dim).to(self.device)
-for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # copy params from policy net
-    target_param.data.copy_(param.data)
-self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
-self.memory = ReplayBuffer(cfg.memory_capacity)
-```
-然后是选择action：
-
-```python
-def choose_action(self, state):
-        '''选择动作
-        '''
-    self.frame_idx += 1
-    if random.random() > self.epsilon(self.frame_idx):
-        action = self.predict(state)
-    else:
-        action = random.randrange(self.n_actions)
-    return action
-```
-
-这里使用e-greedy策略，即设置一个参数epsilon，如果生成的随机数大于epsilon，就根据网络预测的选择action，否则还是随机选择action，这个epsilon是会逐渐减小的，可以使用线性或者指数减小的方式，但不会减小到零，这样在训练稳定时还能保持一定的探索，这部分可以学习探索与利用(exploration and exploition)相关知识。
-
-上面讲到的预测函数其实就是根据state选取q值最大的action，如下：
-
-```python
-def predict(self,state):
-    with torch.no_grad():
-        state = torch.tensor([state], device=self.device, dtype=torch.float32)
-        q_values = self.policy_net(state)
-        action = q_values.max(1)[1].item()
-```
-
-然后是更新函数了：
-
-```python
-def update(self):
-
-        if len(self.memory) < self.batch_size:
-            return
-        # 从memory中随机采样transition
-        state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
-            self.batch_size)
-        '''转为张量
-        例如tensor([[-4.5543e-02, -2.3910e-01,  1.8344e-02,  2.3158e-01],...,[-1.8615e-02, -2.3921e-01, -1.1791e-02,  2.3400e-01]])'''
-        state_batch = torch.tensor(
-            state_batch, device=self.device, dtype=torch.float)
-        action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(
-            1)  # 例如tensor([[1],...,[0]])
-        reward_batch = torch.tensor(
-            reward_batch, device=self.device, dtype=torch.float)  # tensor([1., 1.,...,1])
-        next_state_batch = torch.tensor(
-            next_state_batch, device=self.device, dtype=torch.float)
-        done_batch = torch.tensor(np.float32(
-            done_batch), device=self.device)
-
-        '''计算当前(s_t,a)对应的Q(s_t, a)'''
-        '''torch.gather:对于a=torch.Tensor([[1,2],[3,4]]),那么a.gather(1,torch.Tensor([[0],[1]]))=torch.Tensor([[1],[3]])'''
-        q_values = self.policy_net(state_batch).gather(
-            dim=1, index=action_batch)  # 等价于self.forward
-        # 计算所有next states的V(s_{t+1})，即通过target_net中选取reward最大的对应states
-        next_q_values = self.target_net(next_state_batch).max(
-            1)[0].detach()  # 比如tensor([ 0.0060, -0.0171,...,])
-        # 计算 expected_q_value
-        # 对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
-        expected_q_values = reward_batch + \
-            self.gamma * next_q_values * (1-done_batch)
-        # self.loss = F.smooth_l1_loss(q_values,expected_q_values.unsqueeze(1)) # 计算 Huber loss
-        loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1))  # 计算 均方误差loss
-        # 优化模型
-        self.optimizer.zero_grad()  # zero_grad清除上一步所有旧的gradients from the last step
-        # loss.backward()使用backpropagation计算loss相对于所有parameters(需要gradients)的微分
-        loss.backward()
-        # for param in self.policy_net.parameters():  # clip防止梯度爆炸
-        #     param.grad.data.clamp_(-1, 1)
-        self.optimizer.step()  # 更新模型
-```
-
-更新遵循伪代码的以下部分：
-
-<img src="assets/image-20210507162813393.png" alt="image-20210507162813393" style="zoom:50%;" />
-
-首先从replay buffer中选取一个batch的数据，计算loss，然后进行minibatch SGD。
-
-然后是保存与加载模型的部分，如下：
-
-```python
-def save(self, path):
-        torch.save(self.target_net.state_dict(), path+'dqn_checkpoint.pth')
-def load(self, path):
-    self.target_net.load_state_dict(torch.load(path+'dqn_checkpoint.pth'))
-    for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
-        param.data.copy_(target_param.data)
-```
-
-
-
-### 实验结果
-
-训练结果如下：
-
-<img src="assets/train_rewards_curve.png" alt="train_rewards_curve" style="zoom: 67%;" />
-
-<img src="assets/eval_rewards_curve.png" alt="eval_rewards_curve" style="zoom:67%;" />
-
-## 参考
-
-[with torch.no_grad()](https://www.jianshu.com/p/1cea017f5d11)
-
--- a/projects/codes/DQN/assets/eval_rewards_curve.png
+++ b/projects/codes/DQN/assets/eval_rewards_curve.png
--- a/projects/codes/DQN/assets/image-20210507162813393.png
+++ b/projects/codes/DQN/assets/image-20210507162813393.png
--- a/projects/codes/DQN/assets/rewards_curve_train.png
+++ b/projects/codes/DQN/assets/rewards_curve_train.png
--- a/projects/codes/DQN/assets/train_rewards_curve.png
+++ b/projects/codes/DQN/assets/train_rewards_curve.png
--- a/projects/codes/DQN/assets/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0pvaG5KaW0w,size_16,color_FFFFFF,t_70.png
+++ b/projects/codes/DQN/assets/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0pvaG5KaW0w,size_16,color_FFFFFF,t_70.png
--- a/projects/codes/DQN/dqn.py
+++ b/projects/codes/DQN/dqn.py
@@ -5,7 +5,7 @@
@Email: johnjim0816@gmail.com
@Date: 2020-06-12 00:50:49
@LastEditor: John
-LastEditTime: 2022-08-11 09:52:23
+LastEditTime: 2022-08-18 14:27:18
@Discription: 
@Environment: python 3.7.7
 '''
@@ -23,10 +23,10 @@ class DQN:
    def __init__(self,n_actions,model,memory,cfg):

        self.n_actions = n_actions  
-        self.device = torch.device(cfg.device)  # cpu or cuda
-        self.gamma = cfg.gamma  # 奖励的折扣因子
-        # e-greedy策略相关参数
-        self.sample_count = 0  # 用于epsilon的衰减计数
+        self.device = torch.device(cfg.device) 
+        self.gamma = cfg.gamma  
+        ## e-greedy parameters
+        self.sample_count = 0  # sample count for epsilon decay
        self.epsilon = cfg.epsilon_start
        self.sample_count = 0  
        self.epsilon_start = cfg.epsilon_start
@@ -35,61 +35,78 @@ class DQN:
        self.batch_size = cfg.batch_size
        self.policy_net = model.to(self.device)
        self.target_net = model.to(self.device)
-        for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # 复制参数到目标网路targe_net
+        ## copy parameters from policy net to target net
+        for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): 
            target_param.data.copy_(param.data)
-        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr) # 优化器
-        self.memory = memory # 经验回放
+        # self.target_net.load_state_dict(self.policy_net.state_dict()) # or use this to copy parameters
+        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr) 
+        self.memory = memory 
+        self.update_flag = False 

-    def sample(self, state):
-        ''' 选择动作
+    def sample_action(self, state):
+        ''' sample action with e-greedy policy
        '''
        self.sample_count += 1
+        # epsilon must decay(linear,exponential and etc.) for balancing exploration and exploitation
        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
-            math.exp(-1. * self.sample_count / self.epsilon_decay) # epsilon是会递减的，这里选择指数递减
+            math.exp(-1. * self.sample_count / self.epsilon_decay) 
        if random.random() > self.epsilon:
            with torch.no_grad():
                state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(dim=0)
                q_values = self.policy_net(state)
-                action = q_values.max(1)[1].item() # 选择Q值最大的动作
+                action = q_values.max(1)[1].item() # choose action corresponding to the maximum q value
        else:
            action = random.randrange(self.n_actions)
        return action
-    def predict(self,state):
+    def predict_action(self,state):
+        ''' predict action
+        '''
        with torch.no_grad():
            state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(dim=0)
            q_values = self.policy_net(state)
-            action = q_values.max(1)[1].item() # 选择Q值最大的动作
+            action = q_values.max(1)[1].item() # choose action corresponding to the maximum q value
        return action
    def update(self):
-        if len(self.memory) < self.batch_size: # 当memory中不满足一个批量时，不更新策略
+        if len(self.memory) < self.batch_size: # when transitions in memory donot meet a batch, not update
            return
-        # 从经验回放中(replay memory)中随机采样一个批量的转移(transition)
-        
+        else:
+            if not self.update_flag:
+                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(
            self.batch_size)
-        state_batch = torch.tensor(np.array(state_batch), device=self.device, dtype=torch.float)
-        action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1)  
-        reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float)  
-        next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device, dtype=torch.float)
-        done_batch = torch.tensor(np.float32(done_batch), device=self.device)
-        q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch) # 计算当前状态(s_t,a)对应的Q(s_t, a)
-        next_q_values = self.target_net(next_state_batch).max(1)[0].detach() # 计算下一时刻的状态(s_t_,a)对应的Q值
-        # 计算期望的Q值，对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
-        expected_q_values = reward_batch + self.gamma * next_q_values * (1-done_batch)
-        loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1))  # 计算均方根损失
-        # 优化更新模型
+        state_batch = torch.tensor(np.array(state_batch), device=self.device, dtype=torch.float) # shape(batchsize,n_states)
+        action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1) # shape(batchsize,1)
+        reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float).unsqueeze(1) # shape(batchsize)
+        next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device, dtype=torch.float) # shape(batchsize,n_states)
+        done_batch = torch.tensor(np.float32(done_batch), device=self.device).unsqueeze(1) # shape(batchsize,1)
+        # print(state_batch.shape,action_batch.shape,reward_batch.shape,next_state_batch.shape,done_batch.shape)
+        # compute current Q(s_t,a), it is 'y_j' in pseucodes
+        q_value_batch = self.policy_net(state_batch).gather(dim=1, index=action_batch) # shape(batchsize,1),requires_grad=True
+        # print(q_values.requires_grad)
+        # compute max(Q(s_t+1,A_t+1)) respects to actions A, next_max_q_value comes from another net and is just regarded as constant for q update formula below, thus should detach to requires_grad=False
+        next_max_q_value_batch = self.target_net(next_state_batch).max(1)[0].detach().unsqueeze(1) 
+        # print(q_values.shape,next_q_values.shape)
+        # compute expected q value, for terminal state, done_batch[0]=1, and expected_q_value=rewardcorrespondingly
+        expected_q_value_batch = reward_batch + self.gamma * next_max_q_value_batch* (1-done_batch)
+        # print(expected_q_value_batch.shape,expected_q_value_batch.requires_grad)
+        loss = nn.MSELoss()(q_value_batch, expected_q_value_batch)  # shape same to 
+        # backpropagation
        self.optimizer.zero_grad()  
        loss.backward()
-        for param in self.policy_net.parameters():  # clip防止梯度爆炸
+        # clip to avoid gradient explosion
+        for param in self.policy_net.parameters():  
            param.grad.data.clamp_(-1, 1)
        self.optimizer.step() 

-    def save(self, path):
+    def save_model(self, path):
        from pathlib import Path
+        # create path
        Path(path).mkdir(parents=True, exist_ok=True)
-        torch.save(self.target_net.state_dict(), path+'checkpoint.pth')
+        torch.save(self.target_net.state_dict(), f"{path}/checkpoint.pt")

-    def load(self, path):
-        self.target_net.load_state_dict(torch.load(path+'checkpoint.pth'))
+    def load_model(self, path):
+        self.target_net.load_state_dict(torch.load(f"{path}/checkpoint.pt"))
        for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
            param.data.copy_(target_param.data)
--- a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/models/checkpoint.pth
+++ b/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/models/checkpoint.pth
--- a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/params.json
+++ b/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/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", "result_path": "/Users/jj/Desktop/rl-tutorials/codes/DQN/outputs/CartPole-v0/20220815-185119/results/", "model_path": "/Users/jj/Desktop/rl-tutorials/codes/DQN/outputs/CartPole-v0/20220815-185119/models/", "show_fig": false, "save_fig": true}
--- a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/test_rewards.npy
+++ b/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/test_rewards.npy
--- a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/train_rewards.npy
+++ b/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/train_rewards.npy
--- a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/training_curve.png
+++ b/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/training_curve.png
--- a/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/models/checkpoint.pt
+++ b/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/models/checkpoint.pt
--- a/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/params.json
+++ b/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/params.json
@@ -0,0 +1 @@
+{"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}
--- a/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/testing_curve.png
+++ b/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/testing_curve.png
--- a/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/testing_results.csv
+++ b/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/testing_results.csv
@@ -0,0 +1,21 @@
+episodes,rewards
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--- a/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/training_curve.png
+++ b/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/training_curve.png
--- a/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/training_results.csv
+++ b/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/training_results.csv
@@ -0,0 +1,201 @@
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--- a/projects/codes/DQN/task0.py
+++ b/projects/codes/DQN/task0.py
@@ -1,23 +1,23 @@
 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
+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
+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():
-    """ 超参数
+    """ hyperparameters
    """
-    curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
+    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")
@@ -33,102 +33,101 @@ def get_args():
    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/' )
+            '/' + curr_time + '/results' )
    parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
-            '/' + curr_time + '/models/' ) 
+            '/' + 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,seed=1):
-    ''' 创建环境和智能体
+def env_agent_config(cfg):
+    ''' create env and agent
    '''
-    env = gym.make(cfg.env_name)  # 创建环境
-    n_states = env.observation_space.shape[0]  # 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}")
+    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) # 经验回放
-    agent = DQN(n_actions,model,memory,cfg)  # 创建智能体
-    if seed !=0: # 设置随机种子
-        torch.manual_seed(seed)
-        env.seed(seed)
-        np.random.seed(seed)
+    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("开始训练！")
-    print(f"回合：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}")
-    rewards = []  # 记录所有回合的奖励
+    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  # 记录一回合内的奖励
+        ep_reward = 0  # reward per episode
        ep_step = 0
-        state = env.reset()  # 重置环境，返回初始状态
+        state = env.reset()  # reset and obtain initial state
        while True:
            ep_step += 1
-            action = agent.sample(state)  # 选择动作
-            next_state, reward, done, _ = env.step(action)  # 更新环境，返回transition
+            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)  # 保存transition
-            state = next_state  # 更新下一个状态
-            agent.update()  # 更新智能体
-            ep_reward += reward  # 累加奖励
+                              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:  # 智能体目标网络更新
+        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'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.2f}，Epislon：{agent.epsilon:.3f}')
-    print("完成训练！")
+            print(f'Episode: {i_ep+1}/{cfg.train_eps}, Reward: {ep_reward:.2f}: Epislon: {agent.epsilon:.3f}')
+    print("finish training!")
    env.close()
-    res_dic = {'rewards':rewards}
+    res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
    return res_dic

 def test(cfg, env, agent):
-    print("开始测试！")
-    print(f"回合：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}")
-    rewards = []  # 记录所有回合的奖励
+    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  # 记录一回合内的奖励
+        ep_reward = 0  # reward per episode
        ep_step = 0
-        state = env.reset()  # 重置环境，返回初始状态
+        state = env.reset()  # reset and obtain initial state
        while True:
            ep_step+=1
-            action = agent.predict(state)  # 选择动作
-            next_state, reward, done, _ = env.step(action)  # 更新环境，返回transition
-            state = next_state  # 更新下一个状态
-            ep_reward += reward  # 累加奖励
+            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'回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.2f}')
-    print("完成测试")
+        print(f'Episode: {i_ep+1}/{cfg.test_eps}，Reward: {ep_reward:.2f}')
+    print("finish testing!")
    env.close()
-    return {'rewards':rewards}
+    return {'episodes':range(len(rewards)),'rewards':rewards}


 if __name__ == "__main__":
    cfg = get_args()
-    # 训练
+    # training
    env, agent = env_agent_config(cfg)
    res_dic = train(cfg, env, agent)
-    save_args(cfg,path = cfg.result_path) # 保存参数到模型路径上
-    agent.save(path = cfg.model_path)  # 保存模型
-    save_results(res_dic, tag = 'train', path = cfg.result_path)  
-    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "train")  
-    # 测试
-    env, agent = env_agent_config(cfg) # 也可以不加，加这一行的是为了避免训练之后环境可能会出现问题，因此新建一个环境用于测试
-    agent.load(path = cfg.model_path)  # 导入模型
+    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)  # 保存结果
-    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "test")  # 画出结果
+                 path = cfg.result_path)  
+    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "test")  
--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/models/pg_checkpoint.pt
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/models/pg_checkpoint.pt
--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/results/test_ma_rewards.npy
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/results/test_ma_rewards.npy
--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/results/test_rewards.npy
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/results/test_rewards.npy
--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/results/test_rewards_curve.png
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/results/test_rewards_curve.png
--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/results/train_ma_rewards.npy
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/results/train_ma_rewards.npy
--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/results/train_rewards.npy
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/results/train_rewards.npy
--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/results/train_rewards_curve.png
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/results/train_rewards_curve.png
--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/models/checkpoint.pt
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/models/checkpoint.pt
--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/params.json
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/params.json
@@ -0,0 +1,16 @@
+{
+    "algo_name": "PolicyGradient",
+    "env_name": "CartPole-v0",
+    "train_eps": 200,
+    "test_eps": 20,
+    "gamma": 0.99,
+    "lr": 0.005,
+    "update_fre": 8,
+    "hidden_dim": 36,
+    "device": "cpu",
+    "seed": 1,
+    "result_path": "/Users/jj/Desktop/rl-tutorials/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/",
+    "model_path": "/Users/jj/Desktop/rl-tutorials/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/models/",
+    "save_fig": true,
+    "show_fig": false
+}
--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/testing_curve.png
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/testing_curve.png
--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/testing_results.csv
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/testing_results.csv
@@ -0,0 +1,21 @@
+episodes,rewards
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--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/training_curve.png
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/training_curve.png
--- a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/training_results.csv
+++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/training_results.csv
@@ -0,0 +1,201 @@
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--- a/projects/codes/PolicyGradient/pg.py
+++ b/projects/codes/PolicyGradient/pg.py
@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2020-11-22 23:27:44
 LastEditor: John
-LastEditTime: 2022-02-10 01:25:27
+LastEditTime: 2022-08-22 17:35:34
 Discription: 
 Environment: 
 '''
@@ -16,35 +16,27 @@ from torch.distributions import Bernoulli
 from torch.autograd import Variable
 import numpy as np

-class MLP(nn.Module):
-    
-    ''' 多层感知机
-        输入：state维度
-        输出：概率
-    '''
-    def __init__(self,input_dim,hidden_dim = 36):
-        super(MLP, self).__init__()
-        # 24和36为hidden layer的层数，可根据input_dim, n_actions的情况来改变
-        self.fc1 = nn.Linear(input_dim, hidden_dim)
-        self.fc2 = nn.Linear(hidden_dim,hidden_dim)
-        self.fc3 = nn.Linear(hidden_dim, 1)  # Prob of Left
-
-    def forward(self, x):
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        x = F.sigmoid(self.fc3(x))
-        return x
        
 class PolicyGradient:
    
-    def __init__(self, n_states,cfg):
+    def __init__(self, n_states,model,memory,cfg):
        self.gamma = cfg.gamma
-        self.policy_net = MLP(n_states,hidden_dim=cfg.hidden_dim)
+        self.device = torch.device(cfg.device) 
+        self.memory = memory
+        self.policy_net = model.to(self.device)
        self.optimizer = torch.optim.RMSprop(self.policy_net.parameters(), lr=cfg.lr)
-        self.batch_size = cfg.batch_size

-    def choose_action(self,state):
-        
+    def sample_action(self,state):
+
+        state = torch.from_numpy(state).float()
+        state = Variable(state)
+        probs = self.policy_net(state)
+        m = Bernoulli(probs) # 伯努利分布
+        action = m.sample()
+        action = action.data.numpy().astype(int)[0] # 转为标量
+        return action
+    def predict_action(self,state):
+
        state = torch.from_numpy(state).float()
        state = Variable(state)
        probs = self.policy_net(state)
@@ -53,7 +45,9 @@ class PolicyGradient:
        action = action.data.numpy().astype(int)[0] # 转为标量
        return action
        
-    def update(self,reward_pool,state_pool,action_pool):
+    def update(self):
+        state_pool,action_pool,reward_pool= self.memory.sample()
+        state_pool,action_pool,reward_pool = list(state_pool),list(action_pool),list(reward_pool)
        # Discount reward
        running_add = 0
        for i in reversed(range(len(reward_pool))):
@@ -83,7 +77,11 @@ class PolicyGradient:
            # print(loss)
            loss.backward()
        self.optimizer.step()
-    def save(self,path):
-        torch.save(self.policy_net.state_dict(), path+'pg_checkpoint.pt')
-    def load(self,path):
-        self.policy_net.load_state_dict(torch.load(path+'pg_checkpoint.pt')) 
+        self.memory.clear()
+    def save_model(self,path):
+        from pathlib import Path
+        # create path
+        Path(path).mkdir(parents=True, exist_ok=True)
+        torch.save(self.policy_net.state_dict(), path+'checkpoint.pt')
+    def load_model(self,path):
+        self.policy_net.load_state_dict(torch.load(path+'checkpoint.pt')) 
--- a/projects/codes/PolicyGradient/task0.py
+++ b/projects/codes/PolicyGradient/task0.py
@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2020-11-22 23:21:53
 LastEditor: John
-LastEditTime: 2022-07-21 21:44:00
+LastEditTime: 2022-08-22 17:40:07
 Discription: 
 Environment: 
 '''
@@ -19,10 +19,11 @@ import torch
 import datetime
 import argparse
 from itertools import count
-
+import torch.nn.functional as F
 from pg import PolicyGradient
-from common.utils import save_results, make_dir
-from common.utils import plot_rewards
+from common.utils import save_results, make_dir,all_seed,save_args,plot_rewards
+from common.models import MLP
+from common.memories import PGReplay


 def get_args():
@@ -32,112 +33,107 @@ def get_args():
    parser = argparse.ArgumentParser(description="hyperparameters")      
    parser.add_argument('--algo_name',default='PolicyGradient',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=300,type=int,help="episodes of training")
+    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=0.99,type=float,help="discounted factor")
-    parser.add_argument('--lr',default=0.01,type=float,help="learning rate")
-    parser.add_argument('--batch_size',default=8,type=int)
+    parser.add_argument('--lr',default=0.005,type=float,help="learning rate")
+    parser.add_argument('--update_fre',default=8,type=int)
    parser.add_argument('--hidden_dim',default=36,type=int)
    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
+    parser.add_argument('--seed',default=1,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/' ) # path to save models
-    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")           
-    args = parser.parse_args()                          
+    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")   
+    parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")           
+    args = parser.parse_args([])                          
    return args

+class PGNet(MLP):
+    ''' instead of outputing action, PG Net outputs propabilities of actions, we can use class inheritance from MLP here
+    '''
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = F.sigmoid(self.fc3(x))
+        return x

-def env_agent_config(cfg,seed=1):
+def env_agent_config(cfg):
    env = gym.make(cfg.env_name)  
-    env.seed(seed)
+    if cfg.seed !=0: # set random seed
+        all_seed(env,seed=cfg.seed)
    n_states = env.observation_space.shape[0]
-    agent = PolicyGradient(n_states,cfg)
+    n_actions = env.action_space.n  # action dimension
+    print(f"state dim: {n_states}, action dim: {n_actions}")
+    model = PGNet(n_states,1,hidden_dim=cfg.hidden_dim)
+    memory = PGReplay()
+    agent = PolicyGradient(n_states,model,memory,cfg)
    return env,agent

 def train(cfg,env,agent):
    print('Start training!')
-    print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
-    state_pool = [] # temp states pool per several episodes
-    action_pool = []
-    reward_pool = [] 
+    print(f'Env:{cfg.env_name}, Algo:{cfg.algo_name}, Device:{cfg.device}')
    rewards = []
-    ma_rewards = []
    for i_ep in range(cfg.train_eps):
        state = env.reset()
        ep_reward = 0
        for _ in count():
-            action = agent.choose_action(state) # 根据当前环境state选择action
+            action = agent.sample_action(state) # sample action
            next_state, reward, done, _ = env.step(action)
            ep_reward += reward
            if done:
                reward = 0
-            state_pool.append(state)
-            action_pool.append(float(action))
-            reward_pool.append(reward)
+            agent.memory.push((state,float(action),reward))
            state = next_state
            if done:
                print(f'Episode：{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
                break
-        if i_ep > 0 and i_ep % cfg.batch_size == 0:
-            agent.update(reward_pool,state_pool,action_pool)
-            state_pool = [] 
-            action_pool = []
-            reward_pool = []
+        if (i_ep+1) % cfg.update_fre == 0:
+            agent.update()
        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)
    print('Finish training!')
    env.close() # close environment
-    return rewards, ma_rewards
+    res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
+    return res_dic
            

 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}')
+    print("start testing!")
+    print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
    rewards = []
-    ma_rewards = []
    for i_ep in range(cfg.test_eps):
        state = env.reset()
        ep_reward = 0
        for _ in count():
-            action = agent.choose_action(state) # 根据当前环境state选择action
+            action = agent.predict_action(state)
            next_state, reward, done, _ = env.step(action)
            ep_reward += reward
            if done:
                reward = 0
            state = next_state
            if done:
-                print('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
+                print(f'Episode: {i_ep+1}/{cfg.test_eps}，Reward: {ep_reward:.2f}')
                break
        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)
-    print('完成测试！')
+    print("finish testing!")
    env.close()
-    return rewards, ma_rewards
+    return {'episodes':range(len(rewards)),'rewards':rewards}
    
 if __name__ == "__main__":
-    cfg = Config()
-    # 训练
+    cfg = get_args()
    env, agent = env_agent_config(cfg)
-    rewards, ma_rewards = train(cfg, env, agent)
-    make_dir(cfg.result_path, cfg.model_path)  # 创建保存结果和模型路径的文件夹
-    agent.save(path=cfg.model_path)  # 保存模型
-    save_results(rewards, ma_rewards, tag='train',
-                 path=cfg.result_path)  # 保存结果
-    plot_rewards(rewards, ma_rewards, cfg, tag="train")  # 画出结果
-    # 测试
-    env, agent = env_agent_config(cfg)
-    agent.load(path=cfg.model_path)  # 导入模型
-    rewards, ma_rewards = test(cfg, env, agent)
-    save_results(rewards, ma_rewards, tag='test',
-                 path=cfg.result_path)  # 保存结果
-    plot_rewards(rewards, ma_rewards, cfg, tag="test")  # 画出结果
+    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") 
+

--- a/projects/codes/SAC/sac.py
+++ b/projects/codes/SAC/sac.py
@@ -0,0 +1,4 @@
+
+class SAC:
+    def __init__(self,n_actions,model,memory,cfg):
+        pass
--- a/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/models/checkpoint.pth
+++ b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/models/checkpoint.pth
--- a/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/params.json
+++ b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/params.json
@@ -0,0 +1 @@
+{"algo_name": "SoftQ", "env_name": "CartPole-v0", "train_eps": 200, "test_eps": 20, "max_steps": 200, "gamma": 0.99, "alpha": 4, "lr": 0.0001, "memory_capacity": 50000, "batch_size": 128, "target_update": 2, "device": "cpu", "seed": 10, "result_path": "/Users/jj/Desktop/rl-tutorials/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/", "model_path": "/Users/jj/Desktop/rl-tutorials/codes/SoftQ/outputs/CartPole-v0/20220818-154333/models/", "show_fig": false, "save_fig": true}
--- a/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/testing_curve.png
+++ b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/testing_curve.png
--- a/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/testing_results.csv
+++ b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/testing_results.csv
@@ -0,0 +1,21 @@
+episodes,rewards
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--- a/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/training_curve.png
+++ b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/training_curve.png
--- a/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/training_results.csv
+++ b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/training_results.csv
@@ -0,0 +1,201 @@
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--- a/projects/codes/SoftQ/softq.py
+++ b/projects/codes/SoftQ/softq.py
@@ -0,0 +1,71 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from collections import deque
+import random
+from torch.distributions import Categorical
+import gym
+import numpy as np
+
+class SoftQ:
+    def __init__(self,n_actions,model,memory,cfg):
+        self.memory = memory
+        self.alpha = cfg.alpha
+        self.gamma = cfg.gamma  # discount factor
+        self.batch_size = cfg.batch_size
+        self.device = torch.device(cfg.device) 
+        self.policy_net = model.to(self.device)
+        self.target_net = model.to(self.device)
+        self.target_net.load_state_dict(self.policy_net.state_dict()) # copy parameters
+        self.optimizer = torch.optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
+        self.losses = [] # save losses
+
+    def sample_action(self,state):
+        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
+        with torch.no_grad():
+            q = self.policy_net(state)
+            v = self.alpha * torch.log(torch.sum(torch.exp(q/self.alpha), dim=1, keepdim=True)).squeeze()
+            dist = torch.exp((q-v)/self.alpha)
+            dist = dist / torch.sum(dist)
+            c = Categorical(dist)
+            a = c.sample()
+        return a.item()
+    def predict_action(self,state):
+        state = torch.tensor(np.array(state), device=self.device, dtype=torch.float).unsqueeze(0)
+        with torch.no_grad():
+            q = self.policy_net(state)
+            v = self.alpha * torch.log(torch.sum(torch.exp(q/self.alpha), dim=1, keepdim=True)).squeeze()
+            dist = torch.exp((q-v)/self.alpha)
+            dist = dist / torch.sum(dist)
+            c = Categorical(dist)
+            a = c.sample()
+        return a.item()
+    def update(self):
+        if len(self.memory) < self.batch_size: # when the memory capacity does not meet a batch, the network will not update
+            return
+        state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(self.batch_size)
+        state_batch = torch.tensor(np.array(state_batch), device=self.device, dtype=torch.float) # shape(batchsize,n_states)
+        action_batch = torch.tensor(np.array(action_batch), device=self.device, dtype=torch.float).unsqueeze(1) # shape(batchsize,1)
+        reward_batch = torch.tensor(np.array(reward_batch), device=self.device, dtype=torch.float).unsqueeze(1) # shape(batchsize,1)
+        next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device, dtype=torch.float) # shape(batchsize,n_states)
+        done_batch = torch.tensor(np.array(done_batch), device=self.device, dtype=torch.float).unsqueeze(1) # shape(batchsize,1)
+        # print(state_batch.shape,action_batch.shape,reward_batch.shape,next_state_batch.shape,done_batch.shape)
+        with torch.no_grad():
+            next_q = self.target_net(next_state_batch)
+            next_v = self.alpha * torch.log(torch.sum(torch.exp(next_q/self.alpha), dim=1, keepdim=True))
+            y = reward_batch + (1 - done_batch ) * self.gamma * next_v
+        loss = F.mse_loss(self.policy_net(state_batch).gather(1, action_batch.long()), y)
+        self.losses.append(loss)
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+    def save_model(self, path):
+        from pathlib import Path
+        # create path
+        Path(path).mkdir(parents=True, exist_ok=True)
+        torch.save(self.target_net.state_dict(), path+'checkpoint.pth')
+
+    def load_model(self, path):
+        self.target_net.load_state_dict(torch.load(path+'checkpoint.pth'))
+        for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
+            param.data.copy_(target_param.data)
--- a/projects/codes/SoftQ/task0.py
+++ b/projects/codes/SoftQ/task0.py
@@ -0,0 +1,142 @@
+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 argparse
+import datetime
+import gym
+import torch
+import random
+import numpy as np
+import torch.nn as nn
+from common.memories import ReplayBufferQue
+from common.models import MLP
+from common.utils import save_results,all_seed,plot_rewards,save_args
+from softq import SoftQ
+
+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='SoftQ',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('--max_steps',default=200,type=int,help="maximum steps per episode")
+    parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
+    parser.add_argument('--alpha',default=4,type=float,help="alpha")
+    parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
+    parser.add_argument('--memory_capacity',default=50000,type=int,help="memory capacity")
+    parser.add_argument('--batch_size',default=128,type=int)
+    parser.add_argument('--target_update',default=2,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
+
+class SoftQNetwork(nn.Module):
+    '''Actually almost same to common.models.MLP
+    '''
+    def __init__(self,input_dim,output_dim):
+        super(SoftQNetwork,self).__init__()
+        self.fc1 = nn.Linear(input_dim, 64)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(64, 256)
+        self.fc3 = nn.Linear(256, output_dim)
+        
+    def forward(self, x):
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+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)
+    model = SoftQNetwork(n_states,n_actions)
+    memory =  ReplayBufferQue(cfg.memory_capacity) # replay buffer
+    agent = SoftQ(n_actions,model,memory,cfg)  # create agent
+    return env, agent
+
+def train(cfg, env, agent):
+    ''' training
+    '''
+    print("start training!")
+    print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
+    rewards = []  # record rewards for all episodes
+    steps = [] # record steps for all episodes, sometimes need
+    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:
+        # for _ in range(cfg.max_steps):
+            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}')
+    print("finish training!")
+    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
+    for i_ep in range(cfg.test_eps):
+        ep_reward = 0  # reward per episode
+        state = env.reset()  # reset and obtain initial state
+        while True:
+            action = agent.predict_action(state)  # predict action
+            next_state, reward, done, _ = env.step(action)  
+            state = next_state  
+            ep_reward += reward 
+            if done:
+                break
+        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()
+    # 训练
+    env, agent = env_agent_config(cfg)
+    res_dic = train(cfg, env, agent)
+    save_args(cfg,path = cfg.result_path) # 保存参数到模型路径上
+    agent.save_model(path = cfg.model_path)  # 保存模型
+    save_results(res_dic, tag = 'train', path = cfg.result_path)  
+    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "train")  
+    # 测试
+    env, agent = env_agent_config(cfg) # 也可以不加，加这一行的是为了避免训练之后环境可能会出现问题，因此新建一个环境用于测试
+    agent.load_model(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, path = cfg.result_path,tag = "test")  # 画出结果
--- a/projects/codes/common/memories.py
+++ b/projects/codes/common/memories.py
@@ -5,11 +5,12 @@
@Email: johnjim0816@gmail.com
@Date: 2020-06-10 15:27:16
@LastEditor: John
-LastEditTime: 2021-09-15 14:52:37
+LastEditTime: 2022-08-22 17:23:21
@Discription: 
@Environment: python 3.7.7
 '''
 import random
+from collections import deque
 class ReplayBuffer:
    def __init__(self, capacity):
        self.capacity = capacity # 经验回放的容量
@@ -34,3 +35,40 @@ class ReplayBuffer:
        '''
        return len(self.buffer)

+class ReplayBufferQue:
+    def __init__(self, capacity: int) -> None:
+        self.capacity = capacity
+        self.buffer = deque(maxlen=self.capacity)
+    def push(self,trainsitions):
+        '''_summary_
+        Args:
+            trainsitions (tuple): _description_
+        '''
+        self.buffer.append(trainsitions)
+    def sample(self, batch_size: int, sequential: bool = False):
+        if batch_size > len(self.buffer):
+            batch_size = len(self.buffer)
+        if sequential: # sequential sampling
+            rand = random.randint(0, len(self.buffer) - batch_size)
+            batch = [self.buffer[i] for i in range(rand, rand + batch_size)]
+            return zip(*batch)
+        else:
+            batch = random.sample(self.buffer, batch_size)
+            return zip(*batch)
+    def clear(self):
+        self.buffer.clear()
+    def __len__(self):
+        return len(self.buffer)
+
+class PGReplay(ReplayBufferQue):
+    '''replay buffer for policy gradient based methods, each time these methods will sample all transitions
+    Args:
+        ReplayBufferQue (_type_): _description_
+    '''
+    def __init__(self):
+        self.buffer = deque()
+    def sample(self):
+        ''' sample all the transitions
+        '''
+        batch = list(self.buffer)
+        return zip(*batch)
--- a/projects/codes/common/utils.py
+++ b/projects/codes/common/utils.py
@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2021-03-12 16:02:24
 LastEditor: John
-LastEditTime: 2022-08-15 18:11:27
+LastEditTime: 2022-08-22 17:41:28
 Discription: 
 Environment: 
 '''
@@ -15,6 +15,7 @@ from pathlib import Path
 import matplotlib.pyplot as plt
 import seaborn as sns
 import json
+import pandas as pd

 from matplotlib.font_manager import FontProperties  # 导入字体模块

@@ -84,12 +85,12 @@ def plot_losses(losses, algo="DQN", save=True, path='./'):
        plt.savefig(path+"losses_curve")
    plt.show()

-def save_results(dic, tag='train', path = None):
+def save_results(res_dic, tag='train', path = None):
    ''' 保存奖励
    '''
    Path(path).mkdir(parents=True, exist_ok=True)
-    for key,value in dic.items():
-        np.save(path+'{}_{}.npy'.format(tag,key),value)
+    df = pd.DataFrame(res_dic)
+    df.to_csv(f"{path}/{tag}ing_results.csv",index=None)
    print('Results saved！')


@@ -115,4 +116,26 @@ def save_args(args,path=None):
    Path(path).mkdir(parents=True, exist_ok=True) 
    with open(f"{path}/params.json", 'w') as fp:
        json.dump(args_dict, fp)   
-    print("参数已保存！")
+    print("Parameters saved!")
+
+def all_seed(env,seed = 1):
+    ''' omnipotent seed for RL, attention the position of seed function, you'd better put it just following the env create function
+    Args:
+        env (_type_): 
+        seed (int, optional): _description_. Defaults to 1.
+    '''
+    import torch
+    import numpy as np
+    import random
+    print(f"seed = {seed}")
+    env.seed(seed) # env config
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.manual_seed(seed) # config for CPU
+    torch.cuda.manual_seed(seed) # config for GPU
+    os.environ['PYTHONHASHSEED'] = str(seed) # config for python scripts
+    # config for cudnn
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    torch.backends.cudnn.enabled = False
+    
--- a/projects/requirements.txt
+++ b/projects/requirements.txt
@@ -1,10 +1,11 @@
 gym==0.21.0
-torch==1.9.0 
-torchvision==0.10.0 
-torchaudio==0.9.0
+torch==1.10.0 
+torchvision==0.11.0 
+torchaudio==0.10.0
 ipykernel==6.15.1
 jupyter==1.0.0
 matplotlib==3.5.2
 seaborn==0.11.2
 dill==0.3.5.1
-argparse==1.4.0
+argparse==1.4.0
+pandas==1.3.5
				`@@ -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", "result_path": "/Users/jj/Desktop/rl-tutorials/codes/DQN/outputs/CartPole-v0/20220815-185119/results/", "model_path": "/Users/jj/Desktop/rl-tutorials/codes/DQN/outputs/CartPole-v0/20220815-185119/models/", "show_fig": false, "save_fig": true}
				`@@ -0,0 +1 @@`
				{"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}
				`@@ -0,0 +1 @@`
				`{"algo_name": "SoftQ", "env_name": "CartPole-v0", "train_eps": 200, "test_eps": 20, "max_steps": 200, "gamma": 0.99, "alpha": 4, "lr": 0.0001, "memory_capacity": 50000, "batch_size": 128, "target_update": 2, "device": "cpu", "seed": 10, "result_path": "/Users/jj/Desktop/rl-tutorials/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/", "model_path": "/Users/jj/Desktop/rl-tutorials/codes/SoftQ/outputs/CartPole-v0/20220818-154333/models/", "show_fig": false, "save_fig": true}`