Chess Game Reinforcement Learning
Self-learning chess AI
Every chess match is a dialogue - a test of strategy, creativity, and learning. "Chess Game" isn't just a web-based app; it's a bold AI experiment that blends traditional gameplay with Deep-Q Learning to create an ever-evolving digital opponent.
1 / 210,000+ self-play games
<200ms inference latency
5× faster learning compared to baseline
SnapshotAt a glance
AI Engineer & Frontend Developer
4 months
10,000+ self-play games
<200ms inference latency
5× faster learning compared to baseline
Technologies Used
6 Technologies Integrated
Impact
Key Features
Feature Implementation
Project Vision
Create an AI that improves through play.
Core Process
The process of Developing it.
Experience replay, reward shaping, iterative training.
Build notesWhat I built
03Implemented a Deep-Q Learning reinforcement learning agent using TensorFlow and PyTorch that learns optimal chess strategies through self-play, training on 10,000+ games with experience replay and reward shaping techniques, achieving 5× faster learning efficiency compared to baseline approaches.
Built a real-time chess gameplay system using React frontend and WebSockets for instant move communication, integrating Chess.js for game logic validation and move generation, enabling seamless human-AI interaction with adaptive difficulty that adjusts based on player skill level.
Developed an intelligent move prediction system with optimized replay buffers and MCTS (Monte Carlo Tree Search) integration, reducing inference latency to under 200ms while maintaining strategic depth, creating an ever-evolving digital opponent that improves through each game played.
Exploring intelligence through strategic learning.
Features
- Deep-Q Learning agent
- Adaptive difficulty
- Move prediction
- Real-time gameplay
- Learning from each game played
Challenges
- Training stability
- Real-time inference
- Balancing difficulty progression
