Experimenting applying Meta's V-JEPA [0] architecture for representation learning to chess. One of the challenges is that validating if the model is learning useful dynamics of the game, so I'm using it as an excuse to learn some reinforcement learning by using the representations generated by the JEPA model to approximate useful Q-values [1]. This method currently has no search so I'm planning on comparing with this paper [2] which achieves GM level chess without any search. Honestly, Im unsure if the full pipeline is stable enough to even converge, but it's fun experimenting. I'm bad at chess so I really want to make a bot that challenges the best bots on lichess.
[0]: https://ai.meta.com/research/publications/revisiting-feature... [1]: https://en.wikipedia.org/wiki/Q-learning [2]: https://arxiv.org/abs/2402.04494