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arXiv Machine Learning

Consistent Model Chasing Is Minimax Optimal: The Exact Value of Scalar Adversarial Adaptive Control under Large Parametric Uncertainty

arXiv:2608. 13651v1 Announce Type: cross Abstract: We solve exactly a fundamental problem of adaptive control against adversarial disturbances: regulate the scalar system $x_{t+1} = ax_t + u_t + w_t$, $x_0=0$, $\|w\|_\infty \le 1$, where the constant pole $a \in [-\Delta, \Delta]$ is unknown in sign and magnitude and $\Delta$ is arbitrarily large.

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arXiv AI
Sep 24

ANO: Robust Policy Optimization via Bounded, Redescending Gain Fields

The paper introduces Anchored Neighborhood Optimization (ANO), a new policy‑optimization method that directly designs a smooth, bounded gain field for the probability‑ratio surrogate objective. ANO anchors the identity map at a ratio of one, peaks at a specified trust‑region boundary, and limits the influence of extreme off‑policy samples while providing a bounded, redescending pull on outliers. Empirical results show ANO consistently outperforms existing methods on Atari and MuJoCo benchmarks, and it remains robust under aggressive learning‑rate settings.

By Yiheng Zhang, Yiming Wang, Kaiyan Zhao, Zhenglin Wan, Jiayu Chen, Leong Hou U
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arXiv Machine Learning
Sep 17

A Convergence Framework for Deep $V$-Learning: Error Propagation and Sharp Action-Gap Bounds

The paper presents a convergence framework for deep $V$‑learning over a finite horizon $H$, deriving explicit bounds on policy loss by decomposing the Bellman update error into six residuals. It shows how $L^s$ concentrability controls expected $L^1$ loss, quantifies the impact of shared sampling across horizon levels, and provides optimal and near‑optimal sample allocations for statistical error rates. The work also establishes sharp action‑gap bounds under a margin condition, transfers optimal‑gap results to frozen‑iterate gaps, and offers consistency guarantees for generative‑reset approximate‑ERM procedures with exact action scores.

By Yury Kolomeytsev
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arXiv Machine Learning
Jul 21

The Behavioral Credibility Trilemma: When Calibrated Autonomy Becomes Impossible

arXiv:2605. 25739v2 Announce Type: replace Abstract: We prove that no reinforcement learning policy with confidence-gated autonomy can simultaneously achieve maximum helpfulness, optimal calibration, and full autonomy under rational oversight, whenever some tasks exceed the agent's reliable competence: the Behavioral Credibility Trilemma.

By Lauri Lov\'en, Nam Do, Hassan Mehmood, Dinesh Kumar Sah, Sasu Tarkoma
agentsreinforcement-learning
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arXiv AI
Sep 21

Taming the Adversary: A Cost-to-Disturbance Ratio Approach to Adversarial Reinforcement Learning

The paper introduces CoDRA, a cost-to-disturbance ratio approach for adversarial reinforcement learning that balances controller performance and disturbance exposure without extra penalty terms. CoDRA uses a self‑normalized actor–critic update, scaling value terms by a stop‑gradient normalization constant derived from the current batch. Experiments on MuJoCo pendulum tasks show that CoDRA achieves the lowest cost across a range of forces and masses, outperforming other methods especially on the more challenging InvertedDoublePendulum environment.

By Taeho Lee, Donghwan Lee
reinforcement-learningsafety
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arXiv Machine Learning
Jul 28

Finite-Time Analysis of the Natural Policy Gradient in Finite-Horizon Markov Decision Processes

arXiv:2607. 22982v1 Announce Type: new Abstract: Natural Policy Gradient (NPG) is a well-established Reinforcement Learning algorithm that underlies widely used methods such as Trust Region Policy Optimization and Proximal Policy Optimization, both of which have demonstrated strong empirical success.

By Asha Barua, Sajad Khodadadian
reinforcement-learning
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