The paper introduces RATTL (Risk-Adversarial Total-Reward Learning), a framework that adjusts an agent’s caution based on epistemic uncertainty by using a Bayesian posterior over dynamics and a Wasserstein ambiguity set whose radius depends on that posterior. As evidence accumulates, the radius shrinks, smoothly transitioning the agent’s behavior from worst-case robustness to risk-neutral reward maximization. The authors prove a Safety Sandwich theorem showing RATTL’s value lies between the uninformed robust value and the full-knowledge optimum, and demonstrate the method on a binary-hazard example where the criterion reduces to Conditional Value-at-Risk.
By Deep Kumar Ganguly, Jan Kretinsky
arXiv:2604. 26836v3 Announce Type: replace Abstract: Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploration, yet their reliance on first-principles models or Gaussian processes limits scalability and broader applicability.
By Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank, Henrik Hose, Sebastian Trimpe
arXiv:2606. 01363v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data efficient and safe learning in robotics.
By Bernd Frauenknecht, Devdutt Subhasish, Artur Eisele, Friedrich Solowjow, Sebastian Trimpe
arXiv:2607. 15003v1 Announce Type: new Abstract: The deployment of autonomous cyber-physical systems in safety-critical environments requires closed-loop control strategies (i.
By Riccardo Curcio, Toni Mancini, Enrico Tronci
arXiv:2607. 14407v1 Announce Type: cross Abstract: Many signal processing systems ultimately exist to {act}.
By Osvaldo Simeone
arXiv:2606. 10228v1 Announce Type: cross Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains.
By Kaustubh Mani, Yann Pequignot, Vincent Mai, Liam Paull
The paper introduces Exchange Policy Optimization (EPO), a framework for semi‑infinite safe reinforcement learning that handles infinitely many constraints by iteratively solving finite subproblems. EPO expands or deletes constraints based on tolerance violations and Lagrange multipliers, maintaining computational tractability while converging to an optimal policy with bounded safety violations. The authors prove finite convergence, provide iteration bounds, and quantify the suboptimality gap under mild assumptions.
By Jiaming Zhang, Yujie Yang, Haoning Wang, Liping Zhang, Shengbo Eben Li
arXiv:2606. 04812v1 Announce Type: cross Abstract: Guaranteeing safety is critical to the deployment of reinforcement learning (RL) agents in the real-world, especially as policies learned using deep RL may demonstrate susceptibility to transition perturbations that result in unknown or unsafe behaviour.
By Mohit Prashant, Arvind Easwaran
The paper investigates model‑free robust Q‑learning with χ² uncertainty sets and linear function approximation, using data from a single trajectory of an unknown nominal MDP. It introduces a variational reformulation of the robust Bellman target and a blockwise frozen‑target scheme to overcome estimation and non‑contractivity challenges, and proves a finite‑time error bound for every discount factor γ in (0,1). A neural‑network experiment demonstrates the practical use of the variational target in a continuous‑state nonlinear‑control task.
By Saptarshi Mandal, Yashaswini Murthy, R. Srikant
The paper introduces Continual Uncertainty Learning (CUL), a curriculum-based continual learning framework that decomposes robust control of nonlinear systems with multiple heterogeneous uncertainties into a sequence of tasks. By progressively expanding and diversifying plant uncertainties and applying memory-efficient anti-forgetting regularization, CUL enables a policy to acquire strategies for each uncertainty sequentially while a model-based controller provides a shared baseline performance. Applied to an active vibration controller for automotive powertrains, the approach demonstrates robustness to structural nonlinearities and dynamic variations, improving control performance and sample efficiency.
By Heisei Yonezawa, Ansei Yonezawa, Itsuro Kajiwara
arXiv:2608. 10332v1 Announce Type: cross Abstract: Differentiable predictive control (DPC), a self-supervised learning approach for approximating explicit model predictive control (MPC) policies, offers significant computational advantages over online optimization-based MPC.
By Guangyu Wu, J\'an Drgo\v{n}a
arXiv:2608. 09303v1 Announce Type: cross Abstract: The deployment of autonomous robotic systems in chemistry laboratories is accelerating experimental workflows and providing the foundational data for AI-driven scientific discovery.
By Laura Jones, Shazil Shahzad, Ayesha Sana, Gabriella Pizzuto