JACIII Vol.28 No.2 pp. 380-392
doi: 10.20965/jaciii.2024.p0380

Research Paper:

Inverse Reinforcement Learning with Agents’ Biased Exploration Based on Sub-Optimal Sequential Action Data

Fumito Uwano*,† ORCID Icon, Satoshi Hasegawa**, and Keiki Takadama*** ORCID Icon

*Faculty of Environmental, Life, Natural Science and Technology, Okayama University
3-1-1 Tsushima-naka, Kita-ku, Okayama 700-8530, Japan

Corresponding author

**Konica Minolta, Inc.
2-7-2 Marunouchi, Chiyoda-ku, Tokyo 100-0005, Japan

***Graduate School of Informatics and Engineering, The University of Electro-Communications
1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan

June 16, 2023
December 4, 2023
March 20, 2024
inverse reinforcement learning, data generation, reward design, sub-optimal data

Inverse reinforcement learning (IRL) estimates a reward function for an agent to behave along with expert data, e.g., as human operation data. However, expert data usually have redundant parts, which decrease the agent’s performance. This study extends the IRL to sub-optimal action data, including lack and detour. The proposed method searches for new actions to determine optimal expert action data. This study adopted maze problems with sub-optimal expert action data to investigate the performance of the proposed method. The experimental results show that the proposed method finds optimal expert data better than the conventional method, and the proposed search mechanisms perform better than random search.

Cite this article as:
F. Uwano, S. Hasegawa, and K. Takadama, “Inverse Reinforcement Learning with Agents’ Biased Exploration Based on Sub-Optimal Sequential Action Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.2, pp. 380-392, 2024.
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Last updated on Jul. 12, 2024