2024 Volume 28 Issue 2 Pages 380-392
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.
This article cannot obtain the latest cited-by information.