2018/02/16 by Hui Wang, Wang, Hui, Michael Emmerich +3 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Reinforcement Learning in Robotics #cs.AI
paper · pdf · doi:10.48550/arxiv.1802.05944
openalex publication_date 2018/02/16 · arxiv created 2018/05/21 · arxiv updated 2018/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.