Human Like Chess Engine
Deep Reinforcement Learning
We used supervised training to create a series of chess engines based on humans play at different levels of skill. We compared them to other engines and to human players and found that self-play trained engines would sometimes behave more human-like than the supervised ones, although we believe this may be due to improper hyperparameter selection. The three methods we used for comparing to humans present a novel set of tools for evaluating human-like behaviour in complex reinforcement learning systems and hope to develop them further.
To make the chess engine that acts more like a human, through supervised training and modification of it’s risk sensitivity
Reinforcement Learning Chess Engines
- AlphaZero - Neural Network that evaluates on it’s own, and uses the network to do a tree search based on Predictive + Upper Bound Tree Search (modification of UCB 1).
- AlphaZero has been reimplemented in the open source Leela Chess project
- All games from database.lichess.org
- Using 432,335,939 games as a training set extracted games between similar ELO players
- 22,971,939 as a holdout for later use used ranges of 100 to segregate the training data
- During training 10% was held out to get a training error
- Training error quickly converged to 30% on all runs, while MSE slowly decreased