New research explores Bellman residual minimization for control tasks in reinforcement learning

This paper introduces foundational results for Bellman residual minimization applied to policy optimization in Markov decision problems. While dynamic programming is more common, Bellman residual minimization offers advantages like stable convergence with function approximation. The research focuses on extending this method to control tasks, which have been less explored than policy evaluation. AI

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IMPACT Advances theoretical understanding of control algorithms, potentially improving reinforcement learning stability.

RANK_REASON This is a research paper published on arXiv detailing theoretical advancements in control algorithms for Markov decision problems.

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• Bellman residual minimization
• Markov decision problems
• dynamic programming
• policy evaluation
• reinforcement learning
• Donghwan Lee

• paper
• other