Project Overview
This research project explores the application of reinforcement learning techniques to develop an intelligent agent capable of playing checkers at a competitive level. The study evaluates various RL algorithms, including Q-learning and deep reinforcement learning approaches, comparing their effectiveness in learning optimal strategies for the game.
The project investigates how different reward structures, state representations, and training methodologies impact the agent's performance. Through systematic evaluation against baseline opponents and analysis of learned strategies, this research provides insights into the strengths and limitations of reinforcement learning in board game applications.
Research Paper
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Key Features
- Implementation of multiple reinforcement learning algorithms
- Comparative analysis of Q-learning vs. deep RL approaches
- Custom checkers game environment built from scratch
- Comprehensive evaluation metrics and performance analysis
- Visualization of learned strategies and decision-making patterns