arXiv:2609.09059v1 Announce Type: new
Abstract: While many video-game environments (VGEs) have played crucial roles in advancing reinforcement learning (RL), developing novel VGEs or modifying existi...
By Ryan Truong, Lance Ying, Samuel J. Gershman, Kazuki Irie
arXiv:2609.05576v1 Announce Type: new
Abstract: The paradigm of LLMs has rapidly shifted from passive language interfaces to autonomous Claw-like agents that execute long-horizon tasks across statefu...
By Yirong Zeng, Shen You, Jinhang Feng, Yufei Liu, Xiao Ding, Yutai Hou, Hao Cong, Yuxian Wang, Wu Ning, Wang Xu, Bibo Cai
arXiv:2606. 20376v1 Announce Type: cross Abstract: Safety is a core concern for deploying reinforcement learning (RL) agents in real-world domains such as robotics and autonomous driving.
By Tristan Tomilin, Mourad Boustani, Mickey Beurskens, Thiago D. Sim\~ao
arXiv:2410.14606v3 Announce Type: replace
Abstract: Learning from a stream of experience as it arrives, also known as streaming learning, is a core part of natural learning. However, reliable streami...
By Mohamed Elsayed, Elena Sorina Lupu, Gautham Vasan, A. Rupam Mahmood
arXiv:2609.24456v1 Announce Type: cross
Abstract: Distributed reinforcement learning (RL) scales training by parallelizing actors and learners around an Experience Buffer. As RL workloads grow, howev...
By Sitong Zhang, Tuo Shi, Mario Di Francesco, Zeke Wang, Bo Zhao
arXiv:2609.38733v1 Announce Type: new
Abstract: Recent LLM-based approaches to control either invoke a language model to select actions or synthesize world models that require planning at every decis...
By Zergham Ahmed, Joshua B. Tenenbaum, Chris Bates, Samuel J. Gershman