Reinforcement Learning for Average Reward Zero-Sum Games
We consider Reinforcement Learning for average reward zero-sum stochastic games. We present and analyze two algorithms. The first is based on relative Q-learning and the second on Q-learning for stochastic shortest path games. Convergence is proved using the ODE (Ordinary Differential Equation) method. We further discuss the case where not all the actions are played by the opponent with comparable frequencies and present an algorithm that converges to the optimal Q-function, given the observed play of the opponent.