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

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Bibliographic Details
Published in:Journal of advanced computational intelligence and intelligent informatics 2024-03, Vol.28 (2), p.380-392
Main Authors: Uwano, Fumito, Hasegawa, Satoshi, Takadama, Keiki
Format: Article
Language:English
Subjects:
Bias
Engineering
Entropy
Informatics
Methods
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Summary: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.
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2024.p0380