---
slug: rl-tennis
title: Reinforcement Learning for Tennis Strategy Optimization
type: Academic Project
description: An academic project exploring the application of reinforcement learning to optimize tennis strategies. The project involves training RL agents on Atari Tennis (ALE) to evaluate strategic decision-making through competitive self-play and baseline benchmarking.
shortDescription: Reinforcement learning algorithms applied to Atari tennis matches for strategy optimization and competitive benchmarking.
publishedAt: 2026-03-13
readingTime: 3
status: In progress
tags:
  - Reinforcement Learning
  - Python
  - Gymnasium
  - Atari
  - ALE
icon: i-ph-lightning-duotone
---

## Overview

This project serves as a practical application of theoretical Reinforcement Learning (RL) principles. The goal is to develop and train autonomous agents capable of mastering the complex dynamics of **Atari Tennis**, using the **Arcade Learning Environment (ALE)** via Farama Foundation's Gymnasium.

Instead of simply reaching a high score, this project focuses on **strategy optimization** and **comparative performance** through a multi-stage tournament architecture.

## Technical Objectives

The project is divided into three core phases:

### 1. Algorithm Implementation
I am implementing several key RL algorithms covered during my academic curriculum to observe their behavioral differences in a high-dimensional state space:
* **Value-Based Methods:** Deep Q-Networks (DQN) and its variants (Double DQN, Dueling DQN).
* **Policy Gradient Methods:** Proximal Policy Optimization (PPO) for more stable continuous action control.
* **Exploration Strategies:** Implementing epsilon-greedy and entropy-based exploration to handle the sparse reward signals in tennis rallies.

### 2. The "Grand Slam" Tournament (Self-Play)
To determine the most robust strategy, I developed a competitive framework:
* **Agent vs. Agent:** Different algorithms (e.g., PPO vs. DQN) are pitted against each other in head-to-head matches.
* **Evolutionary Ranking:** Success is measured not just by points won, but by the ability to adapt to the opponent's playstyle (serve-and-volley vs. baseline play).
* **Winner Identification:** The agent with the highest win rate and most stable policy is crowned the "Optimal Strategist."

### 3. Benchmarking Against Atari Baselines
The final "Boss Level" involves taking my best-performing trained agent and testing it against the pre-trained, high-performance algorithms provided by the Atari/ALE benchmarks. This serves as a validation step to measure the efficiency of my custom implementations against industry-standard baselines.

## Tech Stack & Environment

* **Environment:** [ALE (Arcade Learning Environment) - Tennis](https://ale.farama.org/environments/tennis/)
* **Frameworks:** Python, Gymnasium, PyTorch (for neural network backends).
* **Key Challenges:** Handling the long-horizon dependency of a tennis match and the high-frequency input of the Atari RAM/Pixels.

---

*This project is currently in the training phase. I am fine-tuning the reward function to discourage "passive" play and reward aggressive net approaches.*
