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diff --git a/courses/CS243/meta.json b/courses/CS243/meta.json
index f5db6bc2..17dfa0be 100644
--- a/courses/CS243/meta.json
+++ b/courses/CS243/meta.json
@@ -2,6 +2,22 @@
     "code": "CS243",
     "name": "Introduction to Algorithmic Game Theory",
     "semesters": [
+        {
+            "season": "Spring",
+            "year": "2025",
+            "course_id": "CS243.01",
+            "department": "信息科学与技术学院",
+            "credit": "4",
+            "hours": "64",
+            "prerequisite": "无",
+            "evaluation": "Classroom Quizzes (10%); Homework (25%); Project (25%); Exam (40%)",
+            "teacher": [
+                "zhaodj"
+            ],
+            "ta": [
+                "unknown"
+            ]
+        },
         {
             "course_id": "CS243.01",
             "season": "Spring",
@@ -27,7 +43,11 @@
                 "zhaodj"
             ],
             "ta": [
-                "zhangty", "yangty2", "zhangyao1", "lianxy", "gexu"
+                "zhangty",
+                "yangty2",
+                "zhangyao1",
+                "lianxy",
+                "gexu"
             ],
             "department": "信息科学与技术学院",
             "credit": "4",
diff --git "a/courses/CS245/CS245.01_Spring_2025/Homework \344\275\234\344\270\232/CS245_2025_hw1.pdf" "b/courses/CS245/CS245.01_Spring_2025/Homework \344\275\234\344\270\232/CS245_2025_hw1.pdf"
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diff --git "a/courses/CS245/CS245.01_Spring_2025/Homework \344\275\234\344\270\232/CS245_2025_hw1.tex" "b/courses/CS245/CS245.01_Spring_2025/Homework \344\275\234\344\270\232/CS245_2025_hw1.tex"
new file mode 100644
index 00000000..79d657d5
--- /dev/null
+++ "b/courses/CS245/CS245.01_Spring_2025/Homework \344\275\234\344\270\232/CS245_2025_hw1.tex"	
@@ -0,0 +1,154 @@
+\documentclass[11pt,oneside,a4paper]{article}
+\usepackage[top=1 in, bottom=1 in, left=0.85 in, right=0.85 in]{geometry}
+\usepackage{hyperref}
+ \usepackage{float}
+\usepackage{subcaption}
+
+\usepackage{amsmath,amssymb,graphicx,url, algorithm2e}
+\usepackage{thmtools,thm-restate,wrapfig,enumitem,mathabx}
+\newtheorem{assumption}{Assumption}
+\newtheorem{theorem}{Theorem}
+\newtheorem{remark}{Remark}
+\newtheorem{lemma}{Lemma}
+\newtheorem{corollary}{Corollary}
+\newtheorem{proof}{Proof}
+\usepackage{color}
+
+\newcommand{\red}[1]{\textcolor{red}{#1}}
+\newcommand{\blue}[1]{\textcolor{blue}{#1}}
+\newcommand{\ceil}[1]{\lceil #1 \rceil}
+\DeclareMathOperator*{\argmax}{arg\,max}
+\DeclareMathOperator*{\argmin}{arg\,min}
+
+
+\title{Online Optimization and Learning (CS245)}
+\author{Name: ~~~~~~~~~~~~ID: ~~~~~~~~~~~~Email: ~~~~~~~~~~~~}
+\date{}
+
+\begin{document}
+
+\maketitle
+\noindent
+\rule{\linewidth}{0.4pt}
+{\bf {\large Rules:}}
+\begin{enumerate} 
+    \item Deadline: \textcolor{red}{\textbf{2025-03-29/23:59:59}}.\\ The grade of the late submission subjects to the decaying policy $(75\%, 50\%, 25\%)$.
+    \item Please do latex your homework and no handwriting is accepted.
+    \item Submit your homework to TA(guohq@shanghaitech.edu.cn), including your PDF and Code, with filename ``{\sf name+id+CS245HW1.zip}''.
+    \item \textcolor{red}{Plagiarism is not allowed.} You will fail this homework if any plagiarism is detected.
+    \end{enumerate}
+    \vspace{-0.3cm}
+\rule{\linewidth}{0.4pt}
+
+\newpage
+
+\noindent  {\bf Problem 1: Adaptive Online Learning.}
+\vspace{0.5cm}
+
+\vspace{0.1in}
+\hrule
+\vspace{0.05in}
+\noindent{\bf Online Mirror Desecent}
+\vspace{0.05in}
+\hrule
+\vspace{0.05in}
+
+\noindent {\bf Initialization:} $x_1\in \mathcal K,$ learning rate $\eta_t$ and regularizer $\psi(\cdot).$  
+
+\noindent For $t=1,\cdots, T:$ 
+\begin{itemize}
+\item \noindent {\bf Learner:}  Submit $x_t.$
+\item {\bf Environment:} Observe the loss gradient $l_t$.
+\item {\bf Update:} $x_{t+1} = \argmin_{x\in \mathcal K} ~\langle l_t,x \rangle + \frac{1}{\eta_t} B_\psi(x;x_{t}).$
+
+\end{itemize}
+\vspace{0.02in}
+\hrule
+\vspace{0.1in}
+
+\vspace{0.1in}
+\hrule
+\vspace{0.05in}
+\noindent{\bf Follow-The-Regularized-Leader}
+\vspace{0.05in}
+\hrule
+\vspace{0.05in}
+
+\noindent {\bf Initialization:} $x_1\in \mathcal K,$ learning rate $\eta_t$ and regularizer $R(\cdot).$
+
+\noindent For $t=1,\cdots, T:$ 
+\begin{itemize}
+\item \noindent {\bf Learner:}  Submit $x_t.$
+\item {\bf Environment:} Observe the loss function $f_t(x) = \langle l_t,x \rangle$
+\item {\bf Update:} $x_{t+1} = \argmin_{x\in \mathcal K} ~ \sum_{s=1}^t f_s(x) + \frac{1}{\eta_t} R(x).$
+
+\end{itemize}
+\vspace{0.02in}
+\hrule
+\vspace{0.1in}
+Here we introduce the Online Mirror Descent (OMD) and Follow-The-Regularized-Leader (FTRL) algorithms for linear loss function $f_t(x) = \langle l_t, x\rangle \geq 0$. Let's consider the following problem (please provide the detailed steps to justify your answers):
+\begin{itemize}
+    \item Let regularizer $\psi(x) = R(x) = \frac{1}{2} \|x\|^2$, please design adaptive learning rate for $\eta_t$ to prove that OMD and FTRL achieves sub-linear regret, where the regret is defined as usually $$\mathcal R(T)=\sum_{t=1}^T f_t(x_t) - \min_{x\in \mathcal K} \sum_{t=1}^T f_t(x).$$
+    \item FTRL and OMD both have regularization terms, can you explain the connections between these two algorithms?
+\end{itemize}
+
+
+
+\newpage
+
+\noindent  {\bf Problem 2: Online Mirror Descent for LLM Preference Alignment.}
+\vspace{0.5cm}
+
+\noindent 
+Reinforcement learning from human feedback (RLHF) has effectively aligned large language models (LLMs) with human preferences. Given a prompt $x_t$ sampled from the dataset according to a specific distribution, the LLM model samples a pair of responses $(y^1_t,y^2_t)$ from the policy distribution $\pi_t \in \Pi$ (where $\Pi$ represents the set of all possible response distributions). A human evaluator then selects the preferred response, and the LLM is fine-tuned based on this preference feedback.
+
+Here, we consider a simplified formulation of the problem. After choosing the policy $\pi_t$, the LLM model has access to the reward function $r_t(y) = \mathbb{E}_{x_t}[ \mathbb{P}(y \succ \pi_t)|x_t] = \mathbb{E}_{y'\sim\pi_t}[\mathbb{P}(y \succ y') ]$ (which denotes the expected win rate of response $y$ against the current policy $\pi_t$). Below, we present a Mirror Descent Algorithm designed to address this problem.
+\vspace{0.1in}
+\hrule
+\vspace{0.05in}
+\noindent{\bf Online Mirror Descent}
+\vspace{0.05in}
+\hrule
+\vspace{0.05in}
+
+\noindent {\bf Initialization:} $\pi_1 \in \Pi$, Number of iterations $T$, learning rate $\eta$, preference oracle $\mathbb{P}$.
+
+\noindent For $t=1,\cdots, T:$ 
+\begin{itemize}
+\item \noindent {\bf Learner:}  Submit $\pi_t$ and construct response pairs.
+\item {\bf Environment:} The preference oracle $\mathbb{P}$ outputs the reward function $r_t$.
+\item {\bf Algorithm:} (Given the prediction $M_{t+1}$,) Conduct the following (Optimistic) Online Mirror Descent to output the next-step policy $\pi_{t+1}$:
+\begin{align*}
+    \pi_{t+1} = \argmax_{\pi \in \Pi} \langle \pi, r_t \rangle - \frac{1}{\eta} \text{KL}(\pi\|\pi_t). 
+\end{align*}
+\end{itemize}
+\vspace{0.02in}
+\hrule
+\vspace{0.1in}
+
+\noindent Let's consider the following problem (please provide the detailed steps to justify your answers):
+\begin{itemize}
+    \item Can you provide the closed-form updates for the above Online Mirror Descent algorithm?
+    \item To judge the performance of the algorithm, let $\bar \pi := \frac{1}{T}\sum_{t=1}^T \pi_t$, $J(\pi_1,\pi_2):= \mathbb{E}_{x,y^1\sim \pi_1,~y^2\sim \pi_2}[\mathbb{P}(y^1\succ y^2) |x] =\mathbb{E}_{y^1\sim \pi_1,~y^2\sim \pi_2}[\mathbb{P}(y^1\succ y^2)] $, the corresponding DualGap is defined as
+    \begin{align*}
+        \text{DualGap}(\bar \pi):= \max_{\pi_1} J(\pi_1, \pi) - \min_{\pi_2}J(\pi,\pi_2).
+    \end{align*}
+    When $\text{DualGap}(\bar \pi)=0$, it indicates that the Nash equilibrium has been reached. Consequently, the objective is to minimize the DualGap, as it serves as a measure of how closely a policy approximates the Nash equilibrium. Please choose proper $\eta$ and prove the DualGap of \textbf{Online Mirror Descent}.
+    \item For this problem, please write up the  \textbf{Optimistic Online Mirror Descent} algorithm, set proper learning rates, and prove the DualGap.
+\end{itemize}
+(Hint: Find the relationship between \textit{Regret} and \textit{Dual Gap}, and recall the symmetric nature of the game.)
+\newpage
+\noindent  {\bf Problem 3: (Adaptive) Gradient Descent Algorithms for Linear Regression.}
+\vspace{0.5cm}
+
+\noindent In this problem, you will train a linear regression model to predict estate prices.
+\vspace{0.1cm}
+
+\noindent The dataset is available at: \url{https://archive.ics.uci.edu/dataset/477/real+estate+valuation+data+set}, where the first six fields are the features of the estate and the last field is the price. For this dataset, you need to:
+\begin{itemize}
+    \item Split the data into train and test sets in a proper way and utilize a linear regression model to predict the evaluation level. You are asked to implement \red{Gradient Descent Algorithm}, \red{Adaptive Gradient Descent Algorithm}, \red{Root Mean Square Propagation Algorithm}, and \red{Adam Algorithm} in training your model. (You may need to preprocess the data before training to ensure convergence.)
+    \item Please plot your training losses, test your models on your test dataset, and compare their performance.
+\end{itemize}
+\noindent Please implement algorithm code with Python $3$, and make sure your code works.
+
+\end{document}
\ No newline at end of file
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--- /dev/null
+++ "b/courses/CS245/CS245.01_Spring_2025/Homework \344\275\234\344\270\232/CS245_hw2.tex"	
@@ -0,0 +1,162 @@
+\documentclass[11pt,oneside,a4paper]{article}
+\usepackage[top=1 in, bottom=1 in, left=0.85 in, right=0.85 in]{geometry}
+\usepackage{hyperref}
+ \usepackage{float}
+\usepackage{subcaption}
+
+\usepackage{amsmath,amssymb,graphicx,url, algorithm2e}
+\usepackage{thmtools,thm-restate,wrapfig,enumitem,mathabx}
+\newtheorem{assumption}{Assumption}
+\newtheorem{theorem}{Theorem}
+\newtheorem{remark}{Remark}
+\newtheorem{lemma}{Lemma}
+\newtheorem{corollary}{Corollary}
+\newtheorem{proof}{Proof}
+\usepackage{color}
+
+\newcommand{\red}[1]{\textcolor{red}{#1}}
+\newcommand{\blue}[1]{\textcolor{blue}{#1}}
+\newcommand{\ceil}[1]{\lceil #1 \rceil}
+\DeclareMathOperator*{\argmax}{arg\,max}
+\DeclareMathOperator*{\argmin}{arg\,min}
+
+
+\title{Online Optimization and Learning (CS245)}
+\author{Name: ~~~~~~~~~~~~ID: ~~~~~~~~~~~~Email: ~~~~~~~~~~~~}
+\date{}
+
+\begin{document}
+
+\maketitle
+\noindent
+\rule{\linewidth}{0.4pt}
+{\bf {\large Rules:}}
+\begin{enumerate} 
+    \item Deadline: \textcolor{red}{\textbf{2025/4/14/23:59:59}}.\\ The grade of the late submission subjects to the decaying policy $(75\%, 50\%, 25\%)$.
+    \item Please do latex your homework and no handwriting is accepted.
+    \item Submit your homework to TA(guohq@shanghaitech.edu.cn), including your PDF and Code, with filename ``{\sf name+id+CS245HW2.zip}''.
+    \item \textcolor{red}{Plagiarism is not allowed.} You will fail this homework if any plagiarism is detected.
+    \end{enumerate}
+    \vspace{-0.3cm}
+\rule{\linewidth}{0.4pt}
+
+\newpage
+\noindent  {\bf Problem 1: Explore-then-Exploit in Bandits}
+
+\vspace{0.5cm}
+\noindent 
+Explore-then-Exploit is a simple and efficient algorithm for K-armed bandit problems.
+\vspace{0.1in}
+\hrule
+\vspace{0.05in}
+\noindent{\bf Explore-then-Exploit}
+\vspace{0.05in}
+\hrule
+\vspace{0.05in}
+
+\noindent {\bf Initialization:} Time horizon $T$, exploration times $N$.
+
+\noindent {\bf Exploration:} \noindent In the first phase, the choice of arms does not depend on the observed rewards, and each arm is played for $N$ times.
+
+\noindent {\bf 
+Exploitation:} In all remaining rounds, the algorithm selects the arm with the highest empirical mean reward based on the exploration phase.
+
+\vspace{0.02in}
+\hrule
+\vspace{0.5cm}
+\noindent Consider the stochastic multi-armed bandit setting:
+\begin{itemize}
+    \item After $N$ times explorations, for any arm $a$,derive an upper bound on the expected estimation error $|\mu_a - \bar \mu_a|$, where $\mu_a$ is the true mean reward and $\bar \mu_a$ is its empirical mean estimate.
+    \item Choose proper $N$, and prove an \textbf{upper bound} on the regret for the above algorithm.
+    \item Specialize the analysis to the two-armed case and establish a \textbf{lower bound} on the regret of the algorithm.
+\end{itemize}
+
+\vspace{0.5cm}
+{\noindent \bf Solution:}
+
+
+\newpage
+\noindent  {\bf Problem 2: Online Mirror Descent for Adversarial/Stochastic Bandits}
+\vspace{0.5cm}
+
+\noindent We have discussed Online Mirror Descent is possible to achieve good performance for Adversarial/Stochastic Bandits. 
+\vspace{0.05in}
+\hrule
+\vspace{0.05in}
+\noindent{\bf Online Mirror Descent for Adversarial/Stochastic Bandits}
+\vspace{0.05in}
+\hrule
+\vspace{0.05in}
+
+\noindent {\bf Initialization:} $x_1 = [1/K,...,1/K]$ and learning rate $\eta_t$. \\
+\noindent For each round $t=1,\cdots, T:$ 
+\begin{itemize}
+\item {\bf Learner:}  Sample an arm $i$ from $x_t$.
+\item {\bf Environment:} Observe the reward of arm $i$ : $r_t(i)$.
+\item {\bf Estimator:} $\hat{r}_t(i) = r_t(i)/x_t(i)$ and 0 otherwise.
+\item {\bf Update:} $x_{t+1} = \argmin_{x \in \mathcal K} \langle x, -\hat{r}_t \rangle + \frac{1}{\eta_t} B_{\Psi}(x;x_t).$
+\end{itemize}
+\vspace{0.02in}
+\hrule
+\vspace{0.1in}
+
+\noindent If the regularizer $\Psi(x)$ is the negative entropy function, $B_{\psi}$ is the KL divergence and the algorithm is the classical EXP3 algorithm. 
+\vspace{0.1cm}
+
+\noindent Now we consider a different regularizer $\psi(x) = -\sum_{i=1}^K \sqrt{x_i}.$
+\begin{itemize}
+    \item For adversarial bandits, please provide the regret analysis of the algorithm with a proper adaptive learning rate $\eta_t$ and compare it with the regret of EXP3.
+    \item For stochastic bandits, please try to provide a possible problem dependent regret analysis of the algorithm with a proper adaptive learning rate $\eta_t$.
+\end{itemize}
+\vspace{0.5cm}
+{\noindent \bf Solution:}
+
+
+\newpage
+\noindent  {\bf Problem 3: Bandit Algorithms}
+\vspace{0.5cm}
+
+\noindent Consider the following protocol of Bandits problem.
+\vspace{0.05in}
+\hrule
+\vspace{0.05in}
+\noindent{\bf Learning in Bandits}
+\vspace{0.05in}
+\hrule
+\vspace{0.05in}
+
+\noindent {\bf Initialization:} $K$ arms. \\
+\noindent For each round $t=1,\cdots, T:$ 
+\begin{itemize}
+\item \noindent {\bf Learner:}  Choose an arm $i \in [K].$
+\item {\bf Environment:} Observe the loss of picked arm $\ell_{t,i}.$
+\end{itemize}
+\vspace{0.02in}
+\hrule
+\vspace{0.1in}
+
+\noindent In this problem, we provide an environment with $K=32$ arms and $T=5000$ rounds, where each round you will receive a \textbf{loss} of your picked arm (note to be consistent with Homework $1,$ the environment returns the loss instead of reward). 
+\vspace{0.2cm}
+
+\noindent Let's apply the following  algorithms: 
+\begin{itemize}
+    \item Explore-then-exploit Algorithm in Problem $1$.
+    \item UCB Algorithm: A classical algorithm for stochastic bandits. 
+    \item Thompson Sampling Algorithm: A classical algorithm for stochastic bandits. 
+    \item EXP3 Algorithm: A classical algorithm for adversarial bandits.
+    \item Online Mirror Descent with a log-barrier regularizer.
+    \item Online Mirror Descent with $\Psi(x) = -\sum_i \sqrt{x_i}$ in Problem $2$.
+\end{itemize}
+
+\noindent Like in Homework $1,$ you are supposed to choose the proper learning rates and plot the trajectories of algorithms. 
+\vspace{0.2cm}
+
+\noindent  
+Please read the code sample and implement the algorithms with Python $3$. 
+
+\vspace{0.2cm}
+\noindent Note after you submit the code, we will also test your algorithm in other environments. 
+
+\vspace{0.5cm}
+
+\end{document}
\ No newline at end of file
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+++ "b/courses/CS245/CS245.01_Spring_2025/Homework \344\275\234\344\270\232/CS245_hw3.tex"	
@@ -0,0 +1,113 @@
+\documentclass[11pt,oneside,a4paper]{article}
+\usepackage[top=1 in, bottom=1 in, left=0.85 in, right=0.85 in]{geometry}
+\usepackage{hyperref}
+ \usepackage{float}
+\usepackage{subcaption}
+
+\usepackage{amsmath,amssymb,graphicx,url, algorithm2e}
+\usepackage{thmtools,thm-restate,wrapfig,enumitem,mathabx}
+\newtheorem{assumption}{Assumption}
+\newtheorem{theorem}{Theorem}
+\newtheorem{remark}{Remark}
+\newtheorem{lemma}{Lemma}
+\newtheorem{corollary}{Corollary}
+\newtheorem{proof}{Proof}
+\usepackage{color}
+
+\newcommand{\red}[1]{\textcolor{red}{#1}}
+\newcommand{\blue}[1]{\textcolor{blue}{#1}}
+\newcommand{\ceil}[1]{\lceil #1 \rceil}
+\DeclareMathOperator*{\argmax}{arg\,max}
+\DeclareMathOperator*{\argmin}{arg\,min}
+
+
+\title{Online Optimization and Learning (CS245)}
+\author{Name: ~~~~~~~~~~~~ID: ~~~~~~~~~~~~Email: ~~~~~~~~~~~~}
+\date{}
+
+\begin{document}
+
+\maketitle
+\noindent
+\rule{\linewidth}{0.4pt}
+{\bf {\large Rules:}}
+\begin{enumerate} 
+    \item Deadline: \textcolor{red}{\textbf{2025/4/30/00:00:00}}.\\ The grade of the late submission subjects to the decaying policy $(75\%, 50\%, 25\%)$.
+    \item Please do latex your homework and no handwriting is accepted.
+    \item Submit your homework to TA(guohq@shanghaitech.edu.cn), including your PDF and Code, with filename ``{\sf name+id+CS245HW3.zip}''.
+    \item \textcolor{red}{Plagiarism is not allowed.} You will fail this homework if any plagiarism is detected.
+    \end{enumerate}
+    \vspace{-0.3cm}
+\rule{\linewidth}{0.4pt}
+
+\newpage
+
+\noindent  {\bf Problem 1: Policy Gradient for Multi-armed Bandits} \\
+
+\noindent The policy gradient method is a central technique in reinforcement learning, aiming to directly optimize the policy by estimating the gradient of the expected reward with respect to policy parameters. Specifically, given a parameterized stochastic policy $\pi_\theta(a|s)$, the objective is to maximize the expected reward:
+\[
+J(\theta) = \mathbb{E}_{a \sim \pi_\theta(\cdot|s)}[r(a)].
+\]
+
+\vspace{0.1in}
+\hrule
+\vspace{0.05in}
+\noindent{\bf Policy Gradient for Multi-armed Bandits}
+\vspace{0.05in}
+\hrule
+\vspace{0.05in}
+\noindent {\bf Setting:} Consider a finite-armed stochastic bandit problem with finite action set $\mathcal{A}$, and a fixed/dummy state $s_0$. At each round, the learner selects an action $a \in \mathcal{A}$ from a parameterized policy $\pi_\theta(a|s_0)$ or just $\pi_\theta(a)$, and observes a stochastic reward $R(a)$ with mean value $r(a) = \mathbb E[R(a)].$
+
+We study two parameterizations of the policy:
+\begin{itemize}
+    \item[(a)] {\bf Direct probability parameterization:} $\pi_\theta(a) = \theta(a).$
+    \item[(b)] {\bf Softmax parameterization:} $\pi_\theta(a) = \frac{e^{\theta(a)}}{\sum_{a' \in \mathcal{A}} e^{\theta(a')}}$.
+\end{itemize}
+
+\vspace{0.02in}
+\hrule
+\vspace{0.5cm}
+
+\noindent Let $a_t \sim \pi_\theta(\cdot)$ be the action sampled at round $t$, and $R(a_t)$ the observed reward. Please answer the following questions (please provide the detailed steps to justify your answers):
+\begin{itemize}
+    \item[(1)] (Direct Parameterization) Derive the policy gradient update rule for case (a).
+    \item[(2)] (Softmax Parameterization) Derive the policy gradient update rule for case (b).
+\end{itemize}
+
+\vspace{0.5cm}
+{\noindent \bf Solution:}
+
+\newpage
+
+
+
+\noindent {\bf Problem 2: Reinforcement Learning in the MountainCar Environment} 
+
+{\bf Task:}  
+The MountainCar environment is a classic control problem where the agent (a car) is placed in a valley and needs to build up momentum to reach the top of the hill on the right. However, the engine is not powerful enough to drive the car up the hill directly. The agent must learn to leverage gravity and swing back and forth to gain enough momentum.
+
+{\bf Environment Description:}  
+At each time step, the agent receives a state $s_t = (\text{position}, \text{velocity})$ and selects one of three discrete actions:
+\begin{itemize}
+    \item 0: push left
+    \item 1: no push
+    \item 2: push right
+\end{itemize}
+
+The reward is $-1$ for each time step until the goal is reached (position $\geq 0.5$), which encourages the agent to solve the task in as few steps as possible. The episode ends when the goal is reached or after 200 time steps.
+
+You can view a detailed description in the \href{https://gymnasium.farama.org/environments/classic_control/mountain_car/}{Gymnasium Documentation}. You are asked to design:
+\begin{itemize}
+    \item Policy-based algorithm
+    \item Value-based algorithm
+    \item Your algorithms (optional)
+\end{itemize}
+Please:
+\begin{itemize}
+    \item Plot the learning curves of accumulated reward per episode.
+    \item Describe the algorithm you implemented and the challenges specific to this environment (e.g., sparse reward, exploration).
+\end{itemize}
+
+Please implement the algorithm code in Python $3$ and ensure your code can be run and evaluated.
+
+\end{document}
\ No newline at end of file
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diff --git "a/courses/CS245/CS245.01_Spring_2025/Project \350\257\276\347\250\213\350\256\276\350\256\241/CS245_project.rar" "b/courses/CS245/CS245.01_Spring_2025/Project \350\257\276\347\250\213\350\256\276\350\256\241/CS245_project.rar"
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diff --git "a/courses/CS245/CS245.01_Spring_2025/Readings \346\226\207\347\214\256\351\230\205\350\257\273/LLM Post Training.md" "b/courses/CS245/CS245.01_Spring_2025/Readings \346\226\207\347\214\256\351\230\205\350\257\273/LLM Post Training.md"
new file mode 100644
index 00000000..9bdbd807
--- /dev/null
+++ "b/courses/CS245/CS245.01_Spring_2025/Readings \346\226\207\347\214\256\351\230\205\350\257\273/LLM Post Training.md"	
@@ -0,0 +1,9 @@
+# LLM Post-Training
+
+We maintain a list of online learning for LLM post training. You guys can also add something related by replying to this post. 
+
+1. Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning. 
+
+2. Improving LLM General Preference Alignment via Optimistic Online Mirror Descent
+
+3. The Interface Between Reinforcement Learning Theory and Language Model Post-Training https://www.let-all.com/blog/2025/03/05/the-interface-between-reinforcement-learning-theory-and-language-model-post-training/
\ No newline at end of file
diff --git a/courses/CS245/meta.json b/courses/CS245/meta.json
index 52dbc786..23c3069c 100644
--- a/courses/CS245/meta.json
+++ b/courses/CS245/meta.json
@@ -2,6 +2,22 @@
     "code": "CS245",
     "name": "Online Optimization and Learning",
     "semesters": [
+        {
+            "season": "Spring",
+            "year": "2025",
+            "course_id": "CS245.01",
+            "department": "信息科学与技术学院",
+            "credit": "3",
+            "hours": "48",
+            "prerequisite": "CS101, Probability and Statistics, SI251 = Algorithms and data structures, Probability and statistics, and Convex optimization",
+            "evaluation": "Three problem sets (30%), Final project (60%): report (45%) and presentation (15%), Participation and quiz: 10%.",
+            "teacher": [
+                "liuxin7"
+            ],
+            "ta": [
+                "guohq"
+            ]
+        },
         {
             "season": "Spring",
             "year": "2024",
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diff --git "a/courses/CS270B/CS270B.01_Spring_2025/Project \350\257\276\347\250\213\350\256\276\350\256\241/Final Project.md" "b/courses/CS270B/CS270B.01_Spring_2025/Project \350\257\276\347\250\213\350\256\276\350\256\241/Final Project.md"
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--- /dev/null
+++ "b/courses/CS270B/CS270B.01_Spring_2025/Project \350\257\276\347\250\213\350\256\276\350\256\241/Final Project.md"	
@@ -0,0 +1,65 @@
+# **Final Project Guidelines**
+
+**Submission & Presentation Deadline: May 7，23：59** 
+
+------
+
+### **1. Project Submission Requirements**
+
+#### **Submission Contents**
+
+- **Source Code**: Fully executable code (submit as `.zip`).
+
+- 
+
+  Report
+
+   (The latex template has been uploaded to Resource)
+
+  : Must include:
+
+  - Project title, name, and student ID(Ex. "title_ name_studentID" )
+  - Problem description & objectives
+  - Methodology (algorithms, technical details)
+  - Experimental results (image comparisons, data analysis, etc.)
+  - Conclusions & potential improvements
+  - 6~8 pages
+
+- **Presentation Slides**: PPT (submit in advance).
+
+#### **Submission Method**
+
+- Upload via ***https://epan.shanghaitech.edu.cn/l/fFDxCX*** .
+- **Subject format**: ProjectTitle_ name_studentID
+- **Deadline**: Before class on **May 7, 23:59**. Late submissions will not be accepted.
+
+#### **Grading Criteria**
+
+- Report clarity (30%)
+- Visualization of results (20%)
+- Innovation/practical application (20%)
+- Presentation (30%)
+
+------
+
+### **2. Presentation Guidelines**
+
+#### **Time & Location**
+
+- **Date**: May 8 (1:00~3:00 pm)
+- **Duration**: **8-10 minutes per group** (including **2 min** Q&A).
+
+#### **Suggested Structure**
+
+1. Background & objectives
+2. Methodology
+3. Experimental results (comparisons, performance metrics, etc.)
+
+#### **Important Notes**
+
+- Please send the PPT to **wangxiao2024@shanghaitech.edu.cn** in advance
+
+------
+
+**Good luck with your projects!**
+​**​TA Contact​**: **wangxiao2024@shanghaitech.edu.cn**
\ No newline at end of file
diff --git "a/courses/CS270B/CS270B.01_Spring_2025/Project \350\257\276\347\250\213\350\256\276\350\256\241/project.tex" "b/courses/CS270B/CS270B.01_Spring_2025/Project \350\257\276\347\250\213\350\256\276\350\256\241/project.tex"
new file mode 100644
index 00000000..57da9403
--- /dev/null
+++ "b/courses/CS270B/CS270B.01_Spring_2025/Project \350\257\276\347\250\213\350\256\276\350\256\241/project.tex"	
@@ -0,0 +1,89 @@
+\documentclass[12pt]{article}
+\usepackage[utf8]{inputenc}
+\usepackage{graphicx}   % For figures
+\usepackage{amsmath}    % For math
+\usepackage{booktabs}   % For professional tables
+\usepackage{hyperref}   % For hyperlinks
+\usepackage[labelfont=bf]{caption} % Bold figure captions
+
+\title{Project Title}
+\author{Author Name \\ \texttt{email@example.com}}
+\date{\today}
+
+\begin{document}
+
+\maketitle
+
+\begin{abstract}
+    Briefly summarize the problem, methodology, key results, and implications (4-5 sentences). 
+\end{abstract}
+
+\section{Problem Description \& Objectives}
+\label{sec:problem}
+State the problem clearly, its significance, and objectives. Example:
+\begin{itemize}
+    \item \textbf{Problem}: Existing methods for [task] suffer from [limitation]. 
+    \item \textbf{Motivation}: Addressing this improves [application domain]. 
+    \item \textbf{Objectives}: (1) Improve [metric], (2) Reduce [cost], (3) Extend to [scenario].
+\end{itemize}
+
+\section{Methodology}
+\label{sec:method}
+\subsection{Approach Overview}
+Describe the high-level solution (1 paragraph). 
+
+\subsection{Technical Details}
+Detail the algorithms/methods. For example:
+\begin{equation}
+    L = \sum_{i=1}^N (y_i - f(x_i))^2 + \lambda \|\mathbf{w}\|^2 \quad \text{(Regularized Loss)}
+\end{equation}
+
+\subsection{Implementation}
+\begin{itemize}
+    \item \textbf{Dataset}: [Name], size, preprocessing steps.
+    \item \textbf{Baselines}: Compared with [Method A, B].
+    \item \textbf{Setup}: Framework (PyTorch/TensorFlow), hardware (GPU/TPU).
+\end{itemize}
+
+\section{Experimental Results}
+\label{sec:results}
+\subsection{Quantitative Analysis}
+Present results in tables (see Table~\ref{tab:results}).
+
+\begin{table}[h]
+    \centering
+    \caption{Performance comparison (higher is better).}
+    \label{tab:results}
+    \begin{tabular}{lcc}
+        \toprule
+        Method & Accuracy & F1-Score \\
+        \midrule
+        Baseline & 0.85 & 0.82 \\
+        Ours & \textbf{0.91} & \textbf{0.89} \\
+        \bottomrule
+    \end{tabular}
+\end{table}
+
+\subsection{Qualitative Analysis}
+Include visual results (see Figure~\ref{fig:comparison}).
+
+\begin{figure}[h]
+    \centering
+    \includegraphics[width=0.8\linewidth]{example-image} % Replace with actual image
+    \caption{Qualitative comparison between input (left) and output (right).}
+    \label{fig:comparison}
+\end{figure}
+
+\section{Conclusions \& Future Work}
+\label{sec:conclusion}
+Summarize key contributions and limitations. Example:
+\begin{itemize}
+    \item \textbf{Conclusions}: Our method improves [metric] by [X\%] over baselines.
+    \item \textbf{Limitations}: Requires [resource/computation].
+    \item \textbf{Future Work}: Extend to [other domains], optimize [component].
+\end{itemize}
+
+\bibliographystyle{plain} % Choose style (e.g., IEEEtran)
+\bibliography{references} % Replace with your .bib file
+
+\end{document}
\ No newline at end of file
diff --git a/courses/CS270B/meta.json b/courses/CS270B/meta.json
index 5e5b20b4..fd5081f2 100644
--- a/courses/CS270B/meta.json
+++ b/courses/CS270B/meta.json
@@ -2,6 +2,23 @@
     "code": "CS270B",
     "name": "Advanced Digital Image Processing",
     "semesters": [
+        {
+            "season": "Spring",
+            "year": "2025",
+            "course_id": "CS270B.01",
+            "department": "信息科学与技术学院",
+            "credit": "3",
+            "hours": "48",
+            "prerequisite": "无",
+            "evaluation": "Homework 50%, Project 50%",
+            "teacher": [
+                "zhangyy8"
+            ],
+            "ta": [
+                "unknown"
+            ],
+            "mainpage": "https://piazza.com/class/m78oxj3mb3u42v"
+        },
         {
             "season": "Spring",
             "year": "2022",
diff --git "a/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/CS290T_Project2.pdf" "b/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/CS290T_Project2.pdf"
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diff --git "a/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/CS290T_Project3.pdf" "b/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/CS290T_Project3.pdf"
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diff --git "a/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P1_Part_A.pdf" "b/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P1_Part_A.pdf"
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diff --git "a/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P1_Part_B.pdf" "b/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P1_Part_B.pdf"
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index 00000000..33421a56
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diff --git "a/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P1_README.md" "b/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P1_README.md"
new file mode 100644
index 00000000..b1d352a0
--- /dev/null
+++ "b/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P1_README.md"	
@@ -0,0 +1,32 @@
+# Project1   
+# Deadline: 2025/03/21  23:59
+
+The Project1 consists of two parts.
+
+## PartA (30 pts)
+There are 2 problems.
+Please print it out and complete the question on it, and submit the paper version of the question to TA.
+
+Every problem is 15 pts.
+
+## PartB (70 pts)
+**Create a virtual environment using [Anaconda](https://www.anaconda.com/download), with Python 3.6.13 and gym 0.9.4:**
+```bash
+conda create -n gym_094 python==3.6.13
+conda activate gym_094
+pip install gym==0.9.4
+```
+
+Please carefully read Part_B.pdf and complete the 5 questions Q1-5. 
+
+For Q3 and Q4, please write your answer below:
+
+Q3:  In the Cliff Walk environment, the final policy obtained by Policy Iteration and Value Iteration is the same because both aim to solve for the optimal policy of the Markov Decision Process (MDP), and in the Cliff Walk environment, the optimal policy is unique.
+
+Q4: Value Iteration converges faster than Policy Iteration, because it directly computes the optimal value function by repeatedly updating state values using the Bellman optimality equation, without needing a separate policy evaluation step. Policy Iteration, on the other hand, alternates between evaluating the current policy until convergence and then improving it, making each iteration computationally heavier. Consequently, Value Iteration typically requires fewer iterations and converges more quickly, especially in environments with large state spaces like the Cliff Walk environment.
+
+
+(Q1)20 + (Q2)20 + (Q3)10 + (Q4)10 + (Q5)10 = 70 pts
+
+
+Finally, compress the entire folder into a zip file (e.g. 张三_2025233111.zip) and send it to wangyc2023@shanghaitech.edu.cn
\ No newline at end of file
diff --git "a/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P2_README.md" "b/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P2_README.md"
new file mode 100644
index 00000000..ecd53459
--- /dev/null
+++ "b/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P2_README.md"	
@@ -0,0 +1,25 @@
+# Project2   
+# Deadline: 2025/04/11  23:59
+
+
+## Coding for DRL (100 pts)
+
+**For Q1 and Q3, you could use conda env gym_094 in project1.**
+
+
+**For Q2, create a virtual environment using [Anaconda](https://www.anaconda.com/download), with Python 3.8:**
+```bash
+conda create -n gym_0231 python==3.8
+conda activate gym_0231
+pip install -r requirements/requirements.txt
+pip install -r requirements/requirements-atari.txt
+```
+
+Please carefully read CS290T_Project2.pdf and complete the 3 questions Q1-3. 
+
+
+
+(Q1)30 + (Q2)40 + (Q3)30 = 100 pts
+
+
+Finally, compress the entire folder into a zip file (e.g. 张三_2025233111.zip) and send it to wangyc2023@shanghaitech.edu.cn
\ No newline at end of file
diff --git "a/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P3_README.md" "b/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P3_README.md"
new file mode 100644
index 00000000..62fa2073
--- /dev/null
+++ "b/courses/CS290T/CS290T.01_Fall_2025/Homework \344\275\234\344\270\232/P3_README.md"	
@@ -0,0 +1,24 @@
+# Project3   
+# Deadline: 2025/05/09  23:59
+
+
+
+
+## Coding for Multi agent reinforcement learning (100 pts)
+
+**you could use conda env gym_0231 in project2, Just need to install the following packages**
+
+```bash
+conda activate gym_0231
+pip install dill
+pip install setproctitle
+pip install pettingzoo  
+pip install tensorboardX
+pip install scipy
+```
+
+Please carefully read CS290T_Project3.pdf and complete the 3 questions Q1-3. 
+(Q1)20 + (Q2)40 + (Q3)40 = 100 pts
+
+
+Finally, compress the entire folder into a zip file (e.g. 张三_2025233111.zip) and send it to wangyc2023@shanghaitech.edu.cn
\ No newline at end of file
diff --git a/courses/CS290T/cover.jpg b/courses/CS290T/cover.jpg
new file mode 100644
index 00000000..abfa2a71
Binary files /dev/null and b/courses/CS290T/cover.jpg differ
diff --git a/courses/CS290T/meta.json b/courses/CS290T/meta.json
new file mode 100644
index 00000000..7676cf2f
--- /dev/null
+++ b/courses/CS290T/meta.json
@@ -0,0 +1,22 @@
+{
+    "code": "CS290T",
+    "name": "Project Practice for Reinforcement Learning",
+    "semesters": [
+        {
+            "season": "Fall",
+            "year": "2025",
+            "course_id": "CS290T.01",
+            "department": "信息科学与技术学院",
+            "credit": "3",
+            "hours": "48",
+            "prerequisite": "CS280",
+            "evaluation": "未知",
+            "teacher": [
+                "tianzheng"
+            ],
+            "ta": [
+                "unknown"
+            ]
+        }
+    ]
+}
\ No newline at end of file
diff --git a/people/meta.json b/people/meta.json
index 43f377d2..5f0657a7 100644
--- a/people/meta.json
+++ b/people/meta.json
@@ -648,6 +648,12 @@
             ]
         ],
         "teach-courses": [
+            [
+                "Spring",
+                "2025",
+                "CS243",
+                "Introduction to Algorithmic Game Theory"
+            ],
             [
                 "Fall",
                 "2024",
@@ -1333,6 +1339,12 @@
             ]
         ],
         "teach-courses": [
+            [
+                "Spring",
+                "2025",
+                "CS270B",
+                "Advanced Digital Image Processing"
+            ],
             [
                 "Fall",
                 "2024",
@@ -8939,6 +8951,12 @@
             ]
         ],
         "teach-courses": [
+            [
+                "Spring",
+                "2025",
+                "CS245",
+                "Online Optimization and Learning"
+            ],
             [
                 "Fall",
                 "2024",
@@ -8988,6 +9006,12 @@
             ]
         ],
         "ta-courses": [
+            [
+                "Spring",
+                "2025",
+                "CS245",
+                "Online Optimization and Learning"
+            ],
             [
                 "Spring",
                 "2024",
@@ -16082,5 +16106,23 @@
                 "Deep Learning"
             ]
         ]
+    },
+    "tianzheng": {
+        "name": "田政",
+        "info": [
+            [
+                "envelope",
+                "tianzheng@shanghaitech.edu.cn",
+                "mailto:tianzheng@shanghaitech.edu.cn"
+            ]
+        ],
+        "teach-courses": [
+            [
+                "Fall",
+                "2025",
+                "CS290T",
+                "Project Practice for Reinforcement Learning"
+            ]
+        ]
     }
 }
\ No newline at end of file