arXiv Machine Learning

Safe In-Context Reinforcement Learning

arXiv:2509. 25582v4 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks without any parameter updates, instead relying on an expanding context of interaction history.

arXiv Machine Learning

Jun 15

Utility-Constrained Policy Optimization

arXiv:2606. 14029v1 Announce Type: new Abstract: Constrained MDPs (CMDPs) are a widely adopted framework for incorporating safety into RL agents; however, the framework does not support risk-sensitive constraints.

By Mehrdad Moghimi, Bernardo Avila Pires

agents reinforcement-learning benchmarks safety

arXiv AI

Jun 15

CSPO: Constraint-Sensitive Policy Optimization for Safe Reinforcement Learning

arXiv:2606. 14415v1 Announce Type: new Abstract: Safe reinforcement learning (Safe RL) aims to maximize expected return while satisfying safety constraints, typically modeled as Constrained Markov Decision Processes (CMDPs).

By Ayoub Belouadah, Sylvain Kubler, Yves Le Traon

reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Jul 7

CDCP: Conditional Diffusion Model with Contextual Prompts for Multi-task Offline Safe Reinforcement Learning

arXiv:2607. 03903v1 Announce Type: new Abstract: Multi-task offline safe reinforcement learning (RL) promises to learn a shared optimal safe policy from offline data across multiple tasks.

By Jiayi Guan, Tianle Zhang, Li Shen, Ruiqi Zhang, Ao Zhou, Lusong Li, Guai Chen, Mengjie Li, Alois Knoll, Xiaodong He, Changjun Jiang

llms diffusion reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 27

Adjustment Speed as a Safety Constraint for Nonstationary Reinforcement Learning

arXiv:2607. 21646v1 Announce Type: new Abstract: Ensuring safety in reinforcement learning under nonstationarity requires determining whether a learning system can safely adapt to forecasted environmental change within the required recovery horizon.

By Timothy Tomashevskiy

agents reinforcement-learning benchmarks safety

arXiv AI

Jun 10

SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration

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

agents reinforcement-learning safety

arXiv Machine Learning

Jul 17

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems

arXiv:2506. 02255v2 Announce Type: replace Abstract: Most existing safe reinforcement learning (RL) benchmarks focus on robotics and control tasks, offering limited relevance to high-stakes domains that involve structured constraints, mixed-integer decisions, and industrial complexity.

By Asha Ramanujam (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN), Adam Elyoumi (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN), Hao Chen (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN), Sai Madhukiran Kompalli (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN), Akshdeep Singh Ahluwalia (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN), Shraman Pal (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN), Dimitri J. Papageorgiou (Energy Sciences, ExxonMobil Technology and Engineering Company, Annandale, NJ), Can Li (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN)

reinforcement-learning robotics benchmarks

arXiv AI

Aug 12

Threat-guided Policy-aware Scene Perturbation for Safe Autonomous Driving with Online Reinforcement Learning

arXiv:2608. 10403v1 Announce Type: new Abstract: Reinforcement learning (RL) has shown promising performance in autonomous driving, yet ensuring the safety of online RL policies remains challenging due to insufficient exposure to safety-critical driving scenes.

By Xincong Hu (Nanjing University), Lei Ou (Nanjing University), Maosen Li (Yinwang Intelligent Technology Co., Ltd), Jingtao Zhang (Yinwang Intelligent Technology Co., Ltd), Liguo Hou (Yinwang Intelligent Technology Co., Ltd), Zongzhang Zhang (Nanjing University)

agents reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Jun 9

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks

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

llms reinforcement-learning safety

arXiv Machine Learning

Jul 28

Constrained Reinforcement Learning Using Successor Representations

arXiv:2607. 24057v1 Announce Type: new Abstract: Real-world Reinforcement Learning depends on the ability to formulate safety constraints into a policy.

By Michael Girstl, Alexander Mattick, Christopher Mutschler

agents reinforcement-learning safety

arXiv AI

Jul 16

Learning Safe Agent Behaviour from Human Preferences and Justifications via World Models

arXiv:2607. 13172v1 Announce Type: new Abstract: We address the problem of safely training an agent policy and deploying a good and safe policy, in settings where the environment dynamics are unknown and no suitable reward function is available.

By Ilias Kazantzidis, Timothy J. Norman, Yali Du, Christopher T. Freeman

agents reinforcement-learning safety

arXiv AI

Jun 4

Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees

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

agents reinforcement-learning safety

arXiv AI

Jul 10

Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies

arXiv:2603. 15136v2 Announce Type: replace-cross Abstract: Offline safe reinforcement learning (RL) seeks reward-maximizing policies from static datasets under strict safety constraints.

By Mumuksh Tayal, Manan Tayal, Ravi Prakash

diffusion reinforcement-learning efficiency safety

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