June 11, 2024Open Access

Integrating Domain Knowledge for handling Limited Data in Offline RL

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Performance increases by at least 27% using a new regularization technique in offline reinforcement learning, with a clear edge in constrained data situations.
Key evidence shows that offline RL algorithms struggle in regions with limited data, where few observations occur, leading to suboptimal actions.
The proposed method employs a domain knowledge-based regularization technique to refine actions for rare observations in the state space effectively and efficiently in evaluations across various datasets. Though performance improves significantly, further exploration may be needed to validate these findings across diverse scenarios beyond the tested standard datasets.

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Novel domain knowledge-based regularization improves action selection in offline reinforcement learning, indicating effective use of limited data.

Gangopadhyay et al. (2024) studied this question.

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Novel domain knowledge-based regularization improves action selection in offline reinforcement learning, indicating effective use of limited data.