Understanding Game-Playing Agents with Natural Language Annotations. (arXiv:2204.07531v1 [cs.CL])

We present a new dataset containing 10K human-annotated games of Go and show
how these natural language annotations can be used as a tool for model
interpretability. Given a board state and its associated comment, our approach
uses linear probing to predict mentions of domain-specific terms (e.g., ko,
atari) from the intermediate state representations of game-playing agents like
AlphaGo Zero. We find these game concepts are nontrivially encoded in two
distinct policy networks, one trained via imitation learning and another …

agents annotations arxiv game language natural natural language playing