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

• HFO
• Rogue, Catan, StarCraft II

Exploration

DEIR

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

• Kanagawa, Y. and T. Kaneko “Diverse Exploration via InfoMax Options,” Arxiv, Vol. 2010.02756, pp. 1–21 (2020), https://arxiv.org/abs/2010.02756.

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

• https://spinningup.openai.com/en/latest/spinningup/spinningup.html