Online training is a promising technique for training reinforcement learning agents to play strategy board games over the internet against human opponents. But the limited training experience that can be generated by playing against real humans online means that learning must be data-efficient. Data-efficiency has been achieved in other domains by augmenting reinforcement learning with a model: model-based reinforcement learning. In this paper the Minimax-MBTD algorithm is presented, which extends model-based reinforcement learning to deterministic alternating Markov games, a generalisation of two-player zero-sum strategy board games like chess and Go. By using a minimax measure of optimality the strategy learnt generalises to arbitrary opponents, unlike approaches that explicitly model specific opponents. Minimax-MBTD is applied to Tic-Tac-Toe and found to converge faster than direct reinforcement learning, but focussing planning on successors to the current state resulted in slower convergence than unfocussed random planning.
['Drew Mellor']
Model-based reinforcement learning for alternating Markov games