arXiv:2606. 06227v2 Announce Type: replace-cross Abstract: A reinforcement-learning agent maximises its reward, which can diverge from the outcome its designer intended.
By Giorgio Maria Cavallazzi, Miguel P\'erez-Cuadrado, Alfredo Pinelli
arXiv:2607. 12626v1 Announce Type: cross Abstract: Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based policies are trained on small periodic boxes and exhibit reduced performance when carried over to a larger domain.
By Giorgio Maria Cavallazzi, Miguel P\'erez Cuadrado, Alfredo Pinelli
Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based policies are trained on small periodic boxes and exhibit reduced performance when carried over to a larger domain. We recently showed that such policies are also prone to saturated bang-bang actuations that collapse into standing streamwise waves whose scale is set by the computational box rather than by the near-wall cycle, and proposed architectural fixes that avoid these degeneracies.
arXiv:2606. 00949v1 Announce Type: cross Abstract: We propose a method combining Multi-Agent Deep Reinforcement Learning (MARL) and eXplainable Deep Learning (XDL) to reduce drag in wall-bounded turbulent flows.
By Federica Tonti, Ricardo Vinuesa
arXiv:2608. 02508v1 Announce Type: new Abstract: Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges.
By Yi Yang, Zhennan Chen, Yihong Zhuang, Tiehan Fan, Yinan Chen, Jian Li, Jian Yang, Ying Tai
arXiv:2512.08463v2 Announce Type: replace
Abstract: We study how privileged information about a physical system affects the discovery of high-performing policies when training a reinforcement learnin...
By Antonio Terpin, Raffaello D'Andrea