EpiAgent gives LLM agents a trainable belief layer for games with hidden information
EpiAgent splits an agent's decisions into two steps: update its beliefs about other players' private information from the interaction so far, then plan an action using those beliefs. Both steps are trained by self-play, with a reward for how well the beliefs predict what happens next. Across mixed and competitive scenarios, it beat prompting-based and reinforcement-learning baselines.