diff --git a/projects/README.md b/projects/README.md index 2ad650f..c8c5d74 100644 --- 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 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out of 5000i,500n,10000p,200000b,80000s -Output written on pseudocodes.pdf (2 pages). +Output written on pseudocodes.pdf (8 pages). diff --git a/projects/assets/pseudocodes/pseudocodes.out b/projects/assets/pseudocodes/pseudocodes.out new file mode 100644 index 0000000..de023a5 --- /dev/null +++ b/projects/assets/pseudocodes/pseudocodes.out @@ -0,0 +1,7 @@ +\BOOKMARK [1][-]{section.1}{\376\377\152\041\162\110\131\007\165\050}{}% 1 +\BOOKMARK [1][-]{section.2}{\376\377\000Q\000\040\000l\000e\000a\000r\000n\000i\000n\000g\173\227\154\325}{}% 2 +\BOOKMARK [1][-]{section.3}{\376\377\000S\000a\000r\000s\000a\173\227\154\325}{}% 3 +\BOOKMARK [1][-]{section.4}{\376\377\000P\000o\000l\000i\000c\000y\000\040\000G\000r\000a\000d\000i\000e\000n\000t\173\227\154\325}{}% 4 +\BOOKMARK [1][-]{section.5}{\376\377\000D\000Q\000N\173\227\154\325}{}% 5 +\BOOKMARK [1][-]{section.6}{\376\377\000S\000o\000f\000t\000Q\173\227\154\325}{}% 6 +\BOOKMARK [1][-]{section.7}{\376\377\000S\000A\000C\173\227\154\325}{}% 7 diff --git a/projects/assets/pseudocodes/pseudocodes.pdf b/projects/assets/pseudocodes/pseudocodes.pdf index 06c1da7..e1852d6 100644 Binary files a/projects/assets/pseudocodes/pseudocodes.pdf and b/projects/assets/pseudocodes/pseudocodes.pdf differ diff --git a/projects/assets/pseudocodes/pseudocodes.synctex.gz b/projects/assets/pseudocodes/pseudocodes.synctex.gz index 8754760..07d025c 100644 Binary files a/projects/assets/pseudocodes/pseudocodes.synctex.gz and b/projects/assets/pseudocodes/pseudocodes.synctex.gz differ diff --git a/projects/assets/pseudocodes/pseudocodes.tex b/projects/assets/pseudocodes/pseudocodes.tex index 4db2296..3cc47ab 100644 --- 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} \ No newline at end of file diff --git a/projects/assets/pseudocodes/pseudocodes.toc b/projects/assets/pseudocodes/pseudocodes.toc new file mode 100644 index 0000000..85e0526 --- /dev/null +++ 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}% diff --git a/projects/codes/DQN/README.md b/projects/codes/DQN/README.md deleted file mode 100644 index 33e7397..0000000 --- a/projects/codes/DQN/README.md +++ /dev/null @@ -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 - -## 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： - -image-20210507162813393 - -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： - -train_rewards_curve - -eval_rewards_curve - -## 参考 - -[with torch.no_grad()](https://www.jianshu.com/p/1cea017f5d11) - diff --git a/projects/codes/DQN/assets/eval_rewards_curve.png b/projects/codes/DQN/assets/eval_rewards_curve.png deleted file mode 100644 index 0327b47..0000000 Binary files a/projects/codes/DQN/assets/eval_rewards_curve.png and /dev/null differ diff --git a/projects/codes/DQN/assets/image-20210507162813393.png 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deleted file mode 100644 index c55ac87..0000000 Binary files a/projects/codes/DQN/assets/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0pvaG5KaW0w,size_16,color_FFFFFF,t_70.png and /dev/null differ diff --git a/projects/codes/DQN/dqn.py b/projects/codes/DQN/dqn.py index 2b28757..de071cc 100644 --- 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) diff --git a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/models/checkpoint.pth b/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/models/checkpoint.pth deleted file mode 100644 index 8172745..0000000 Binary files a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/models/checkpoint.pth and /dev/null differ diff --git a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/params.json b/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/params.json deleted file mode 100644 index 7749c42..0000000 --- a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/params.json +++ /dev/null @@ -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} \ No newline at end of file diff --git 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 deleted file mode 100644 index 14bca8b..0000000 Binary files a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/test_rewards.npy and /dev/null differ diff --git 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 deleted file mode 100644 index b96ce50..0000000 Binary files a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/train_rewards.npy and /dev/null differ diff --git a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/training_curve.png 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"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} \ No newline at end of file diff --git a/projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/testing_curve.png b/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/testing_curve.png similarity index 100% rename from projects/codes/DQN/outputs/CartPole-v0/20220815-185119/results/testing_curve.png rename to projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/testing_curve.png diff --git 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 new file mode 100644 index 0000000..fb73fd6 --- /dev/null +++ b/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/testing_results.csv @@ -0,0 +1,21 @@ +episodes,rewards +0,200.0 +1,200.0 +2,200.0 +3,200.0 +4,200.0 +5,200.0 +6,200.0 +7,200.0 +8,200.0 +9,200.0 +10,200.0 +11,200.0 +12,200.0 +13,200.0 +14,200.0 +15,200.0 +16,200.0 +17,200.0 +18,200.0 +19,200.0 diff --git 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 new file mode 100644 index 0000000..0e7b997 Binary files /dev/null and b/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/training_curve.png differ diff --git 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 new file mode 100644 index 0000000..4429b6a --- /dev/null +++ b/projects/codes/DQN/outputs/CartPole-v0/20220818-143132/results/training_results.csv @@ -0,0 +1,201 @@ +episodes,rewards +0,38.0 +1,16.0 +2,37.0 +3,15.0 +4,22.0 +5,34.0 +6,20.0 +7,12.0 +8,16.0 +9,14.0 +10,13.0 +11,21.0 +12,14.0 +13,12.0 +14,17.0 +15,12.0 +16,10.0 +17,14.0 +18,10.0 +19,10.0 +20,16.0 +21,9.0 +22,14.0 +23,13.0 +24,10.0 +25,9.0 +26,12.0 +27,12.0 +28,14.0 +29,11.0 +30,9.0 +31,8.0 +32,9.0 +33,11.0 +34,12.0 +35,10.0 +36,11.0 +37,10.0 +38,10.0 +39,18.0 +40,13.0 +41,15.0 +42,10.0 +43,9.0 +44,14.0 +45,14.0 +46,23.0 +47,17.0 +48,15.0 +49,15.0 +50,20.0 +51,28.0 +52,36.0 +53,36.0 +54,23.0 +55,27.0 +56,53.0 +57,19.0 +58,35.0 +59,62.0 +60,57.0 +61,38.0 +62,61.0 +63,65.0 +64,58.0 +65,43.0 +66,67.0 +67,56.0 +68,91.0 +69,128.0 +70,71.0 +71,126.0 +72,100.0 +73,200.0 +74,200.0 +75,200.0 +76,200.0 +77,200.0 +78,200.0 +79,200.0 +80,200.0 +81,200.0 +82,200.0 +83,200.0 +84,200.0 +85,200.0 +86,200.0 +87,200.0 +88,200.0 +89,200.0 +90,200.0 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+182,200.0 +183,200.0 +184,200.0 +185,200.0 +186,200.0 +187,200.0 +188,200.0 +189,200.0 +190,200.0 +191,200.0 +192,200.0 +193,200.0 +194,200.0 +195,200.0 +196,200.0 +197,200.0 +198,200.0 +199,200.0 diff --git a/projects/codes/DQN/task0.py b/projects/codes/DQN/task0.py index 8985103..5a6f9a5 100644 --- 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") diff --git 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 deleted file mode 100644 index 64c6702..0000000 Binary files a/projects/codes/PolicyGradient/outputs/CartPole-v0/20220210-061325/models/pg_checkpoint.pt and /dev/null differ diff --git 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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 +} \ No newline at end of file diff --git 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 new file mode 100644 index 0000000..a38dd4b Binary files /dev/null and b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/testing_curve.png differ diff --git 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 new file mode 100644 index 0000000..958b0ef --- /dev/null +++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/testing_results.csv @@ -0,0 +1,21 @@ +episodes,rewards +0,200.0 +1,200.0 +2,165.0 +3,200.0 +4,200.0 +5,200.0 +6,200.0 +7,200.0 +8,200.0 +9,200.0 +10,200.0 +11,168.0 +12,200.0 +13,200.0 +14,200.0 +15,115.0 +16,198.0 +17,200.0 +18,200.0 +19,200.0 diff --git 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 new file mode 100644 index 0000000..3e0db7c Binary files /dev/null and b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/training_curve.png differ diff --git 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 new file mode 100644 index 0000000..daeb8f2 --- /dev/null +++ b/projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/training_results.csv @@ -0,0 +1,201 @@ +episodes,rewards +0,26.0 +1,53.0 +2,10.0 +3,37.0 +4,22.0 +5,21.0 +6,12.0 +7,34.0 +8,38.0 +9,40.0 +10,23.0 +11,14.0 +12,16.0 +13,25.0 +14,15.0 +15,23.0 +16,11.0 +17,28.0 +18,21.0 +19,62.0 +20,33.0 +21,27.0 +22,15.0 +23,17.0 +24,26.0 +25,35.0 +26,26.0 +27,14.0 +28,42.0 +29,45.0 +30,34.0 +31,39.0 +32,31.0 +33,17.0 +34,42.0 +35,41.0 +36,31.0 +37,39.0 +38,28.0 +39,12.0 +40,36.0 +41,33.0 +42,47.0 +43,40.0 +44,63.0 +45,36.0 +46,64.0 +47,79.0 +48,49.0 +49,40.0 +50,65.0 +51,47.0 +52,51.0 +53,30.0 +54,26.0 +55,41.0 +56,86.0 +57,61.0 +58,38.0 +59,200.0 +60,49.0 +61,70.0 +62,61.0 +63,101.0 +64,200.0 +65,152.0 +66,108.0 +67,46.0 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+164,177.0 +165,200.0 +166,200.0 +167,200.0 +168,200.0 +169,200.0 +170,200.0 +171,200.0 +172,200.0 +173,200.0 +174,200.0 +175,200.0 +176,200.0 +177,200.0 +178,200.0 +179,200.0 +180,200.0 +181,200.0 +182,200.0 +183,200.0 +184,200.0 +185,200.0 +186,200.0 +187,200.0 +188,200.0 +189,200.0 +190,200.0 +191,200.0 +192,200.0 +193,200.0 +194,200.0 +195,200.0 +196,190.0 +197,200.0 +198,189.0 +199,200.0 diff --git a/projects/codes/PolicyGradient/pg.py b/projects/codes/PolicyGradient/pg.py index 688895f..8cd8688 100644 --- 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')) \ No newline at end of file + 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')) \ No newline at end of file diff --git a/projects/codes/PolicyGradient/task0.py b/projects/codes/PolicyGradient/task0.py index b9e11a0..8f42f25 100644 --- 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") + diff --git a/projects/codes/SAC/sac.py b/projects/codes/SAC/sac.py new file mode 100644 index 0000000..e907520 --- /dev/null +++ b/projects/codes/SAC/sac.py @@ -0,0 +1,4 @@ + +class SAC: + def __init__(self,n_actions,model,memory,cfg): + pass \ No newline at end of file diff --git a/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/models/checkpoint.pth b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/models/checkpoint.pth new file mode 100644 index 0000000..fc80e6f Binary files /dev/null and b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/models/checkpoint.pth differ diff --git a/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/params.json b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/params.json new file mode 100644 index 0000000..988c303 --- /dev/null +++ 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} \ No newline at end of file diff --git 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 new file mode 100644 index 0000000..83750e7 Binary files /dev/null and b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/testing_curve.png differ diff --git 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 new file mode 100644 index 0000000..b74878b --- /dev/null +++ b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/testing_results.csv @@ -0,0 +1,21 @@ +episodes,rewards +0,200.0 +1,200.0 +2,200.0 +3,200.0 +4,200.0 +5,200.0 +6,200.0 +7,200.0 +8,199.0 +9,200.0 +10,200.0 +11,200.0 +12,200.0 +13,200.0 +14,200.0 +15,200.0 +16,200.0 +17,200.0 +18,200.0 +19,200.0 diff --git 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 new file mode 100644 index 0000000..9f3164b Binary files /dev/null and b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/training_curve.png differ diff --git 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 new file mode 100644 index 0000000..0f52c1c --- /dev/null +++ b/projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/training_results.csv @@ -0,0 +1,201 @@ +episodes,rewards +0,21.0 +1,23.0 +2,24.0 +3,27.0 +4,33.0 +5,18.0 +6,47.0 +7,18.0 +8,18.0 +9,21.0 +10,26.0 +11,31.0 +12,11.0 +13,17.0 +14,22.0 +15,16.0 +16,17.0 +17,34.0 +18,20.0 +19,11.0 +20,50.0 +21,15.0 +22,11.0 +23,39.0 +24,11.0 +25,28.0 +26,37.0 +27,26.0 +28,63.0 +29,18.0 +30,17.0 +31,13.0 +32,9.0 +33,15.0 +34,13.0 +35,21.0 +36,17.0 +37,22.0 +38,20.0 +39,31.0 +40,9.0 +41,10.0 +42,11.0 +43,15.0 +44,18.0 +45,10.0 +46,30.0 +47,14.0 +48,36.0 +49,26.0 +50,21.0 +51,15.0 +52,9.0 +53,14.0 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= 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) \ No newline at end of file diff --git a/projects/codes/SoftQ/task0.py b/projects/codes/SoftQ/task0.py new file mode 100644 index 0000000..fd67aa4 --- /dev/null +++ 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 \ No newline at end of file diff --git a/projects/codes/common/memories.py b/projects/codes/common/memories.py index a238696..255333a 100644 --- 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) \ No newline at end of file diff --git a/projects/codes/common/utils.py b/projects/codes/common/utils.py index dd21163..e63d1e6 100644 --- 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 + \ No newline at end of file diff --git a/projects/requirements.txt b/projects/requirements.txt index 42e65c6..8db643c 100644 --- 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 \ No newline at end of file +argparse==1.4.0 +pandas==1.3.5 \ No newline at end of file