arXiv:2602. 23242v3 Announce Type: replace Abstract: In general reinforcement learning, all established optimal agents, including AIXI, are model-based, explicitly maintaining and using environment models.
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The paper investigates regression with bounded responses, comparing two learning frameworks: model selection aggregation, which requires improper algorithms to achieve minimax excess risk, and universal learning, where empirical risk minimization suffices for exponential learning rates. For finite hypothesis classes, the authors show that the $Q$-aggregation estimator simultaneously attains minimax optimal tails and exponential universal rates, while other common estimators fail to do so. For countably infinite classes, they prove an inherent trade‑off between exponential universal and minimax uniform rates, resolved by combining optimal algorithms from each framework via $Q$-aggregation.
arXiv:2609.09981v1 Announce Type: new Abstract: We study offline inference for the optimal value in reinforcement learning. Two new nuisances are derived as fixed points of a self-induced Bellman equ...
arXiv:2511. 22226v2 Announce Type: replace Abstract: The standard theory of model-free reinforcement learning assumes that the environment dynamics are stationary and that agents are decoupled from their environment, such that policies are treated as being separate from the world they inhabit.
arXiv:2607. 17823v1 Announce Type: new Abstract: Reinforcement Learning is a cornerstone technique for modern large reasoning models.
arXiv:2605. 23146v3 Announce Type: replace-cross Abstract: Classical reinforcement learning assumes the agent interacts with a fixed environment whose behavior does not depend on the agent's policy.
The paper investigates how to infer agents’ underlying incentives in strategic multi‑agent settings, where traditional inverse reinforcement learning (IRL) may fail to produce rewards that guide optimal play. It introduces the concept of strategic identifiability and shows that learning from a fixed pair of policies can be insufficient regardless of sample size. To address this, the authors propose an active IRL algorithm for two‑player repeated Stackelberg games, combining maximum‑likelihood estimation with experimental design to achieve finite‑sample guarantees on sub‑optimality, and validate the approach in an anti‑poaching scenario.