Monte Carlo Q-learning for General Game Playing

Hui Wang; Michael Emmerich; Aske Plaat

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arXiv · 1802.05944

Monte Carlo Q-learning for General Game Playing

Hui Wang·Michael Emmerich·Aske Plaat

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Abstract

After the recent groundbreaking results of AlphaGo, we have seen a strong interest in reinforcement learning in game playing. General Game Playing (GGP) provides a good testbed for reinforcement learning. In GGP, a specification of games rules is given. GGP problems can be solved by reinforcement learning. Q-learning is one of the canonical reinforcement learning methods, and has been used by (Banerjee & Stone, IJCAI 2007) in GGP. In this paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe, Connect Four, Hex), to allow comparison to Banerjee et al. As expected, Q-learning converges, although much slower than MCTS. Borrowing an idea from MCTS, we enhance Q-learning with Monte Carlo Search, to give QM-learning. This enhancement improves the performance of pure Q-learning. We believe that QM-learning can also be used to improve performance of reinforcement learning further for larger games, something which we will test in future work.

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Hui Wang, Michael Emmerich, Aske Plaat. 2018-02-16. Monte Carlo Q-learning for General Game Playing. https://arxiv.org/abs/1802.05944

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