Reinforcement Learning in Perfect-Information Games∗

This paper studies action-based reinforcement learning in finite perfectioninformation games. Restrictions on the valuation updating rule that guarantee that the play eventually converges to a subgame-perfect Nash equilibrium (SPNE) are identified. These conditions are mild enough to contain interesting and plausible learning behavior. We provide two examples of such updating rule that suggest that the extent of knowledge and rationality assumptions needed to support a SPNE outcome in finite perfect-information games may be minimal.