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该论文提出了一种统一的 actor-critic 方法，将岭正则化的路径最小二乘 LSTD critic 与重要性校正动量 (ICM) actor 相结合，并首次给出了 ICM actor-critic 在单条 Markov 轨迹上归一化与非归一化步长下的有限时间收敛界，同时将结果特化到 Muon、带动量的 SGD 及 AdaNorm 等优化器进行比较。
This paper introduces MI-SARSA, a reinforcement learning algorithm that models bounded rationality by incorporating mutual-information regularization to balance policy complexity and reaction time under cognitive constraints.
This paper proposes efficient online learning algorithms for policy evaluation in MDPs with dynamic utility-based shortfall risk (UBSR) measures under linear function approximation, introducing the UBSR-TD algorithm and demonstrating its convergence and practical effectiveness.
Introduces Temporal Difference in Vision (TDV), a new paradigm for representation learning that relies solely on causality, eliminating the need for augmentations, masking, or cropping, and matches state-of-the-art methods like DINO and iBOT on dense spatial tasks.
Introduces Temporal Difference in Vision (TDV), a novel visual representation learning paradigm that learns useful representations without augmentations, masking, cropping, or reconstruction, and matches state-of-the-art methods on dense spatial tasks.
The paper introduces Temporal Difference in Vision (TDV), a self-supervised learning method for video that relies only on a causal assumption that past causes future, avoiding strong inductive biases while matching state-of-the-art on dense spatial tasks.
This paper proposes behavior-aware auxiliary corrections for off-policy temporal-difference prediction, introducing BA-TDC and BA-TDRC algorithms that replace the auxiliary covariance matrix with the behavior Bellman matrix to improve stability and convergence. Theoretical analysis and experiments on standard benchmarks validate the effectiveness of the proposed methods.
This paper presents a finite-iteration theory for asynchronous categorical distributional temporal-difference learning, bridging the gap between existing theoretical frameworks and practical online implementations.