No AI summary available for this article.
Why It Matters
Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic.
Provenance
Discovered via ArXiv and published by ArXiv.
Key Claims
Original description
Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic. However, critics are typically trained to predict returns, and accurate value predictions do not necessarily yield accurate action derivatives, potentially leading to unreliable policy updates. We propose Forward Entropy-Regularized Policy Optimization (FERPO), an on-policy maximum entropy reinforcement learning algorithm that performs policy improvement using critic values without differentiating the critic with respect to actions. FERPO derives...
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2610.02198v1 · Indexed about 1 hour ago