New research paper questions intrinsic rewards in reinforcement learning

A new research paper titled "When Do Intrinsic Rewards Lead to Exploration?" proposes a formal criterion for evaluating exploration in reinforcement learning. The paper argues that maximizing intrinsic rewards does not always lead to the most informative experiences for an agent. It introduces a method to compare policies based on the counterfactual information they acquire and demonstrates in a simple environment that common intrinsic reward objectives can be Pareto-suboptimal in this regard. The research also establishes conditions under which existing intrinsic rewards effectively encourage optimal exploration and presents a new objective designed to improve exploration based on the proposed criterion. AI

IMPACT Proposes a new theoretical framework for evaluating exploration strategies in reinforcement learning, potentially leading to more efficient agent training.

RANK_REASON Research paper published on arXiv detailing a new theoretical criterion for exploration in reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Hugging Face
• reinforcement learning
• When Do Intrinsic Rewards Lead to Exploration?

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