In this paper, we present a generalized temporal-difference (TD) reinforcement learning framework based on the theory of conditional expectations. The value and action-value (Q-value) functions are treated as uncertain quantities, and their estimation is formulated as a stochastic inference problem.
arXiv:2607. 20010v1 Announce Type: new Abstract: In this paper, we present a generalized temporal-difference (TD) reinforcement learning framework based on the theory of conditional expectations.
By Vasos Arnaoutis, Eric Lutters, Bojana Rosi\'c
arXiv:2606. 05967v1 Announce Type: cross Abstract: In this paper, we study the finite-time behavior of the TD(0) temporal-difference method with linear function approximation (LFA).
By Ziad Kobeissi (L2S), \'Elo\"ise Berthier (U2IS)
arXiv:2503. 18607v2 Announce Type: replace-cross Abstract: We introduce the Switching Non-Stationary Markov Decision Process (SNS-MDP) framework, in which the environment transitions among a finite set of MDPs governed by a latent Markov chain while the agent observes only the external state.
By Mohsen Amiri, Sindri Magn\'usson
arXiv:2609.06882v1 Announce Type: cross
Abstract: Diffusion policies offer a powerful and expressive parameterization for continuous control. Yet, their integration with reinforcement learning remain...
By Mahmoud Selim, Cristina Cipriani, Karl H. Johansson
arXiv:2505.01361v3 Announce Type: replace
Abstract: Temporal difference (TD) learning is a foundational algorithm in reinforcement learning (RL). For nearly forty years, TD learning has served as a w...
By Hwanwoo Kim, Panos Toulis, Eric Laber