Reinforcement Learning for Tennis Strategy Optimization
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.
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
- 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.