  One practical requirement in solving dynamic games is to ensure that the
players play well from any decision point onward. To satisfy this requirement,
existing efforts focus on equilibrium refinement, but the scalability and
applicability of existing techniques are limited. In this paper, we propose
Temporal-Induced Self-Play (TISP), a novel reinforcement learning-based
framework to find strategies with decent performances from any decision point
onward. TISP uses belief-space representation, backward induction, policy
learning, and non-parametric approximation. Building upon TISP, we design a
policy-gradient-based algorithm TISP-PG. We prove that TISP-based algorithms
can find approximate Perfect Bayesian Equilibrium in zero-sum one-sided
stochastic Bayesian games with finite horizon. We test TISP-based algorithms in
various games, including finitely repeated security games and a grid-world
game. The results show that TISP-PG is more scalable than existing mathematical
programming-based methods and significantly outperforms other learning-based
methods.
