The paper introduces Robust Fed-Q, a federated Q‑learning algorithm designed for settings where multiple agents interact with a shared Markov Decision Process and communicate through a central server. It combines model‑based and model‑free reinforcement learning techniques with a median‑of‑means strategy from robust statistics to handle a small fraction of adversarial agents. The authors prove that Robust Fed-Q achieves exact convergence to the optimal value function with high probability, attains near‑optimal finite‑time rates that benefit from collaboration, and requires only “~O(1)” communication rounds per guarantee.
By Sreejeet Maity, Aritra Mitra
arXiv:2506. 07040v4 Announce Type: replace-cross Abstract: We study model-free methods for distributionally robust infinite-horizon average-reward Markov decision processes (MDPs).
By Yang Xu, Swetha Ganesh, Vaneet Aggarwal
arXiv:2605. 28276v2 Announce Type: replace Abstract: Reinforcement learning algorithms are commonly analyzed (and designed) under the Markov assumption.
By Onno Eberhard, Claire Vernade, Michael Muehlebach
arXiv:2605. 01752v4 Announce Type: replace Abstract: We study linear dueling bandits in volatile environments characterized by the simultaneous presence of post-serving contexts, delayed feedback, and adversarial corruption.
By Youngmin Oh
arXiv:2402. 06734v2 Announce Type: replace-cross Abstract: We study data corruption robustness for reinforcement learning with human feedback (RLHF) in an offline setting.
By Debmalya Mandal, Andi Nika, Parameswaran Kamalaruban, Adish Singla, Goran Radanovi\'c
arXiv:2606. 03521v1 Announce Type: cross Abstract: To improve the real-world applicability of reinforcement learning (RL), the field of adversarially robust RL studies how to train agents under adversarial environment perturbations.
By Siemen Herremans, Ali Anwar, Siegfried Mercelis
arXiv:2506. 06891v3 Announce Type: replace Abstract: We study the corruption-robustness of in-context reinforcement learning (ICRL), focusing on the Decision-Pretrained Transformer (DPT, Lee et al.
By Paulius Sasnauskas, Yi\u{g}it Yal{\i}n, Goran Radanovi\'c
To improve the real-world applicability of reinforcement learning (RL), the field of adversarially robust RL studies how to train agents under adversarial environment perturbations. In this setting, a protagonist agent optimizes a policy under environmental perturbations from an adversary, resulting in a zero-sum Markov game.
arXiv:2608. 03069v1 Announce Type: new Abstract: Deep Q-Networks (DQNs) learn value functions through bootstrapped temporal-difference updates, where future returns are approximated using a greedy maximization over next-state action values.
By Lipeng Zu, Xiaonan Zhang
arXiv:2506. 18020v3 Announce Type: replace Abstract: Robust distributed learning algorithms aim to maintain reliable performance despite the presence of misbehaving workers.
By Thomas Boudou, Batiste Le Bars, Nirupam Gupta, Aur\'elien Bellet
The paper introduces an adversarial reinforcement learning framework that learns the sparsest Denial-of-Service (DoS) attack schedule capable of destabilizing self‑triggered reinforcement learning controllers (RL‑STC). It proves a lower bound on the minimum number of jamming actions needed to force a crash and demonstrates that the learned adversary consistently defeats four different defenders—one LQR and three RL‑STC—across Pendulum, CartPole, and Quadrotor2D environments, outperforming greedy and periodic baselines in jam‑time‑per‑failure. The study also shows that the adversary remains effective under Gaussian observation noise and limited state information.
By Adam Haroon, Erick J. Rodr\'iguez-Seda, Tristan Schuler, Cody Fleming
arXiv:2605. 16103v2 Announce Type: replace Abstract: Q-learning is known to suffer from overestimation bias: because the Bellman update maximizes noisy or imperfect action-value estimates, positive errors can be selected and propagated, causing learned values to exceed the true optimal values.
By Donghwan Lee