arXiv:2607. 18077v1 Announce Type: cross Abstract: What gives the Bellman equation its form?
arXiv:2607. 17316v1 Announce Type: new Abstract: The softmax policy $\pi(a \mid s) \propto \exp(\beta Q(s,a))$ is the default model of stochastic choice in reinforcement learning (RL).
The paper introduces a Bellman optimality equation for optimizing plasticity in Markov decision processes, extending recent work that reframes the stability‑plasticity dilemma as an empowerment‑plasticity tradeoff. It builds on Abel et al. (2025), which defined plasticity as the generalized directed information from observations to actions and empowerment as the reverse. This work is the first to address plasticity optimization under that new definition, providing a theoretical foundation similar to existing empowerment‑based approaches.
arXiv:2602. 03778v2 Announce Type: replace-cross Abstract: Tail-end risk measures such as static conditional value-at-risk (CVaR) are used in safety-critical applications to prevent rare, yet catastrophic events.
arXiv:2603. 23461v2 Announce Type: replace Abstract: We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying \emph{linear Bellman completeness} -- a fundamental setting where the Bellman backup of any linear value function remains linear.
In many real-world planning tasks, agents must tackle uncertainty about the environment's state and variability in the outcomes of any chosen policy. We address both forms of uncertainty as a first st...
arXiv:2609.24103v1 Announce Type: new Abstract: In many real-world planning tasks, agents must tackle uncertainty about the environment's state and variability in the outcomes of any chosen policy. W...
arXiv:2603. 06946v2 Announce Type: replace Abstract: Many distributional quantities in reinforcement learning are intrinsically joint across actions, including distributions of gaps and probabilities of superiority.
The paper introduces a vector Bellman theory for multichain robust average‑reward Markov decision processes, addressing the state‑dependent optimal long‑run rewards that arise under uncertainty. It develops a gain‑first, bias‑second optimization principle for finite models with compact, post‑action $(s,a)$‑rectangular ambiguity, yielding a coupled vector gain‑bias system and stationary saddle strategies from all initial states. The authors also characterize solvability conditions, provide certificates for asymptotically affine trajectories of the robust Bellman operator, and design a robust approximately shifted Halpern planning algorithm that converges to the optimal gain vector and produces average‑optimal greedy controllers.
arXiv:2607. 16717v1 Announce Type: cross Abstract: This paper proposes a causal independence principle for value -- the value Causal Markov Condition (v-CMC) -- and develops the conceptual and mathematical foundations of a "causal value theory" linking causality and utility.
arXiv:2606. 00151v1 Announce Type: cross Abstract: In reinforcement learning (RL), agents benefit from exploration only because they repeatedly encounter similar states: trying different actions can improve performance or reduce uncertainty; without such retries, a greedy policy is optimal.
arXiv:2601. 18840v4 Announce Type: replace Abstract: Markov decision problems are most commonly solved via dynamic programming.
arXiv:2605. 30694v2 Announce Type: replace Abstract: Many theories of decision making -- planning, reinforcement learning, causal intervention, online learning, and game-theoretic equilibrium -- turn local information into globally coherent behavior.