Regret-Aware Policy Optimization: Environment-Level Memory for Replay Suppression under Delayed Harm
Department of Computer Science and Engineering, VTU, Belagavi, India（印度贝尔高姆VTU计算机科学与工程系）；Aliens on Earth (AoE) Autonomous Research Group, Belagavi, India（印度贝尔高姆地球外星人自主研究小组）
Prakul Sunil Hiremath
备注：
18 pages, 3 figures. Includes theoretical analysis and experiments on graph diffusion environments
Safety in reinforcement learning (RL) is typically enforced through objective shaping while keeping environment dynamics stationary with respect to observable state-action pairs. Under delayed harm, this can lead to replay: after a washout period, reintroducing the same stimulus under matched observable conditions reproduces a similar harmful cascade.
We introduce the Replay Suppression Diagnostic (RSD), a controlled exposure-decay-replay protocol that isolates this failure mode under frozen-policy evaluation. We show that, under stationary observable transition kernels, replay cannot be structurally suppressed without inducing a persistent shift in replay-time action distributions.
Motivated by platform-mediated systems, we propose Regret-Aware Policy Optimization (RAPO), which augments the environment with persistent harm-trace and scar fields and applies a bounded, mass-preserving transition reweighting to reduce reachability of historically harmful regions.
On graph diffusion tasks (50-1000 nodes), RAPO suppresses replay, reducing re-amplification gain (RAG) from 0.98 to 0.33 on 250-node graphs while retaining 82\% of task return. Disabling transition deformation only during replay restores re-amplification (RAG 0.91), isolating environment-level deformation as the causal mechanism.
National Key Laboratory of Novel Software Technology（计算机软件新技术国家重点实验室）；Nanjing University（南京大学）
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Warwick Manufacturing Group (WMG), University of Warwick（华威大学华威制造集团（WMG））；University of Warwick（华威大学）；United Kingdom（英国（注意：英国是国家，非机构，这里可能是原文本中地址信息，按要求不提取））
Policy gradient reinforcement learning (RL) algorithms have achieved impressive performance in challenging learning tasks such as continuous control, but suffer from high sample complexity. Experience replay is a commonly used approach to improve sample efficiency, but gradient estimators using past trajectories typically have high variance. Existing sampling strategies for experience replay like uniform sampling or prioritised experience replay do not explicitly try to control the variance of the gradient estimates. In this paper, we propose an online learning algorithm, adaptive experience s…
In this work, we study algorithms for learning in infinite-horizon undiscounted Markov decision processes (MDPs) with function approximation. We first show that the regret analysis of the Politex algorithm (a version of regularized policy iteration) can be sharpened from $O(T^{3/4})$ to $O(\sqrt{T})$ under nearly identical assumptions, and instantiate the bound with linear function approximation. Our result provides the first high-probability $O(\sqrt{T})$ regret bound for a computationally efficient algorithm in this setting. The exact implementation of Politex with neural network function ap…
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Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；University of Oxford（牛津大学）；Barcelona Supercomputing Center（巴塞罗那超级计算中心）；Institut de Ciències del Mar（海洋科学研究所）
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Price of Fairness in Bandits: A Tight Minimax Characterization
Indian Institute of Technology Kharagpur（印度理工学院卡拉格浦尔分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Indian Institute of Technology Kanpur（印度理工学院坎浦尔分校）
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Fraunhofer Heinrich-Hertz-Institut（弗劳恩霍夫海因里希-赫兹研究所）；Nokia Bell Laboratories（诺基亚贝尔实验室）；Technische Universität Berlin（柏林工业大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（BIFOLD – 柏林学习与数据基础研究所）
Provably efficient Model-Based Reinforcement Learning (MBRL) based on optimism or posterior sampling (PSRL) is ensured to attain the global optimality asymptotically by introducing the complexity measure of the model. However, the complexity might grow exponentially for the simplest nonlinear models, where global convergence is impossible within finite iterations. When the model suffers a large generalization error, which is quantitatively measured by the model complexity, the uncertainty can be large. The sampled model that current policy is greedily optimized upon will thus be unsettled, res…
Stanford University（斯坦福大学）；University of Pennsylvania（宾夕法尼亚大学）
A key challenge to deploying reinforcement learning in practice is avoiding excessive (harmful) exploration in individual episodes. We propose a natural constraint on exploration -- \textit{uniformly} outperforming a conservative policy (adaptively estimated from all data observed thus far), up to a per-episode exploration budget. We design a novel algorithm that uses a UCB reinforcement learning policy for exploration, but overrides it as needed to satisfy our exploration constraint with high probability. Importantly, to ensure unbiased exploration across the state space, our algorithm adapti…
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Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong（香港中文大学系统工程与工程管理系）；Rotman School of Management, University of Toronto（多伦多大学罗特曼管理学院）
Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms
Department of Computing Science, University of Alberta（阿尔伯塔大学计算机科学系）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）；Advanced Technology R&D Center, Mitsubishi Electric Corporation（三菱电机株式会社先进技术研发中心）；Information Technology R&D Center, Mitsubishi Electric Corporation（三菱电机株式会社信息技术研发中心）
Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving
Chair of Econometrics and Statistics Corresponding author esp. in the Transport Sector（计量经济学与统计学教授职位（交通运输领域特别对应作者））；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（可扩展数据与人工智能研究中心（ScaDS.AI））
