arXiv:2601. 19612v3 Announce Type: replace-cross Abstract: Safe exploration is a key requirement for reinforcement learning (RL) agents to learn and adapt online, beyond controlled (e.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2605. 29032v2 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss.
arXiv:2606. 10228v1 Announce Type: cross Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains.
arXiv:2606. 01363v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data efficient and safe learning in robotics.
arXiv:2510. 19528v2 Announce Type: replace-cross Abstract: We investigate the fundamental problem of leveraging offline data to accelerate online reinforcement learning - a direction with strong potential but limited theoretical grounding.
The article outlines a Ph.D. research agenda aimed at creating provably efficient and practical algorithms for data‑driven sequential decision‑making under uncertainty. It focuses on reinforcement learning and multi‑armed bandits, targeting applications such as recommendation systems, computer networks, video analytics, and large language models. The work seeks to overcome limitations of existing methods—such as reliance on idealized models, lack of robustness to adversarial perturbations, and poor instance‑dependent performance—by developing algorithms that are more efficient, robust, instance‑adaptive, and generalizable to new environments.