  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.
