arXiv:2601. 23075v2 Announce Type: replace Abstract: On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies, often leading to brittle optimization when gradients are noisy, and policy updates must be conservative.
By Yuexin Bian, Jie Feng, Tao Wang, Yijiang Li, Sicun Gao, Yuanyuan Shi
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.
By Soichiro Nishimori, Paavo Parmas
arXiv:2505.04193v2 Announce Type: replace
Abstract: Simplicity is a critical inductive bias for designing data-driven controllers, especially when robustness is important. Despite the impressive resu...
By Bang You, Chenxu Wang, Wenju Yang, Di Guo, Huaping Liu
arXiv:2602. 12643v2 Announce Type: replace-cross Abstract: We present Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that unifies the efficiency of model-free methods with the representational strengths of model-based approaches, without incurring planning overhead.
By Jashaswimalya Acharjee, Balaraman Ravindran
arXiv:2408. 02295v4 Announce Type: replace Abstract: Conventional uncertainty-aware temporal difference (TD) learning often models TD errors as zero-mean Gaussian.
By Seyeon Kim, Joonhun Lee, Namhoon Cho, Sungjun Han, Wooseop Hwang
Deep Reinforcement Learning policies can produce nonsmooth action oscillations that hinder deployment on physical robots. Existing architectural and penalty-based approaches seek spatial smoothness by...