arXiv:2512. 18336v2 Announce Type: replace-cross Abstract: This paper explores the impact of dynamic entropy tuning in Reinforcement Learning (RL) algorithms that train a stochastic policy.
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:2607. 23726v1 Announce Type: cross Abstract: Exploration in sparse-reward long-horizon tasks poses significant challenges for reinforcement learning.
arXiv:2601. 19624v3 Announce Type: replace-cross Abstract: Real-world reinforcement learning often faces environment drift, but most existing methods rely on static entropy coefficients/target entropy, causing over-exploration during stable periods and under-exploration after drift, and leaving unanswered the principled question of how exploration intensity should scale with drift magnitude.
arXiv:2512. 18333v2 Announce Type: replace-cross Abstract: This paper proposes a new Reinforcement Learning (RL) based control architecture for quadrotors.
arXiv:2606. 05888v1 Announce Type: new Abstract: Retry-based objectives such as pass@K and max@K optimize the best return obtained from multiple sampled trajectories, and recent work has shown that they can promote exploration without explicit exploration bonuses.
arXiv:2610.02198v1 Announce Type: cross Abstract: Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic....
The paper introduces IR‑LQR, an optimistic online linear quadratic regulator that incorporates intrinsic rewards and variance regularization to encourage exploration while maintaining the standard LQR structure. By only adjusting the cost function, IR‑LQR remains computationally simple yet achieves the optimal worst‑case regret rate of √T. The authors validate the method with numerical experiments on aircraft pitch angle control and a UAV example, comparing it to state‑of‑the‑art online LQR algorithms.