Towards the Automatic Synthesis of Interpretable Chess Tactics

Excerpt

arXiv:2610.07640v1 Announce Type: cross Abstract: State-of-the-art reinforcement learning agents are capable of outperforming human experts at games like chess, Go and StarCraft II. These agents do not simply take advantage of their digital hardware in being able to react and calculate faster than humans, but employ better strategies that lead to more victories. Interpreting these strategies would give human players valuable insight into how to improve their play. In this preliminary work, we pr