Reinforcement learning in games

In reinforcement learning (RL), agents improve their ability through interaction with an environment. The goal of RL in games is to make good agents without giving domain knowledge. AlphaZero is such an application of RL into games.

Domains

with LLMs

  • Zhu, H. and Kaneko, T. “Enhancing cost-effective large language models with reinforcement learning for multi-turn text-based environments,” Advances in computer games, acg pp. 65-76, 2025, doi: 10.1007/978-3-032-23657-9_6

Exploration

  • Wan, S. , Tang, Y. , Tian, Y. and Kaneko, T. “DEIR: Efficient and robust exploration through discriminative-model-based episodic intrinsic rewards,” IJCAI, pp. 4289-4298, 2023, doi: 10.24963/ijcai.2023/477
  • Xu, F. and Kaneko, T. “Enhanced imagination and feature balancing for cooperative multi-agent reinforcement learning,” Neural information processing, iconip 2024 2284 pp. 153-170, 2025, doi: 10.1007/978-981-96-6954-7_11
  • Xu, F. and Kaneko, T. “Curiosity-driven exploration for cooperative multi-agent reinforcement learning” IEEE international joint conference on neural networks pp. 1-8, 2023, doi: 10.1109/IJCNN54540.2023.10191336

AlphaZero

  • Nakayashiki, T. and Kaneko, T. “Maximum entropy reinforcement learning in two-player perfect information games,” IEEE SSCI, pp. 1-8. 2021 doi 10.1109/SSCI50451.2021.9659991

Option/skill

Atari

  • Zhu, H. and T. Kaneko “Residual Network for Deep Reinforcement Learning with Attention Mechanism,” J. Inf. Sci. Eng., Vol. 37, No. 3, pp. 517–533 (2021), DOI: 10.6688/JISE.20210537(3) .0002.
  • Hyunwoo, O. and T. Kaneko “Deep Recurrent Q-Network with Truncated History,” in IEEE Technologies and Applications of Artificial Intelligence, pp. 34–39 (2018), DOI: 10.1109/TAAI.2018. 00017.

note

for general introduction for RL studies, see e.g., openai’s documents