Deep learning and chess
This paper investigates the application of neural networks and Monte Carlo Tree Search (MCTS) for the development of a chess-playing agent. Our experiments include both full-game and isolated-position testing environments. Through rigorous evaluation, we show that the integration of neural networ...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/181411 |