arXiv:2606. 10580v1 Announce Type: cross Abstract: The asymptotic behaviour of Monte Carlo optimistic policy iteration (MC-O-PI) is a long-standing open question.
By Octave Oliviers, Glenn Vinnicombe
arXiv:2609.06489v1 Announce Type: cross
Abstract: Monte Carlo Tree Search (MCTS) has demonstrated success in online planning for deterministic environments, yet significant challenges remain in adapt...
By Tuan Dam
arXiv:2606. 15978v1 Announce Type: new Abstract: Tsitsiklis proved convergence of Monte Carlo optimistic policy iteration under a uniform update structure and identified nonuniform update frequencies as a delicate obstruction.
By Yuanlong Chen
arXiv:2607. 29593v1 Announce Type: new Abstract: This paper studies the policy gradient update for a multi-arm bandit problem in diffusion environment that is described by a stochastic differential equation (SDE) under the continuous-time reinforcement learning framework by Wang et al.
By Yanwei Jia, Du Ouyang
arXiv:2610.00911v1 Announce Type: new
Abstract: We study an endogenous nonstationary stochastic bandit problem with latent linear dynamics, where actions affect both immediate rewards and the future...
By Taehyun Hwang, Hyunjun Choi, Heesang Ann, Min-hwan Oh
arXiv:2606. 21528v2 Announce Type: replace-cross Abstract: We study first-order methods for solving monotone variational inequalities arising in min-max optimization.
By Motahareh Sohrabi, Jianxin You, Simon Lacoste-Julien, Eduard Gorbunov, Gauthier Gidel