arXiv AI

Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient

arXiv AI

Jul 3

Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates

arXiv:2605. 11020v2 Announce Type: replace-cross Abstract: Inverse reinforcement learning (IRL) is typically formulated as maximizing entropy subject to matching the distribution of expert trajectories.

By Anish Diwan, Davide Tateo, Christopher E. Mower, Haitham Bou-Ammar, Jan Peters, Oleg Arenz

reinforcement-learning benchmarks safety

arXiv AI

Jun 8

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach

arXiv:2510. 09041v3 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies.

By Junchao Fan, Qi Wei, Ruichen Zhang, Yang Lu, Jianhua Wang, Xiaolin Chang, Bo Ai

agents reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Oct 15

Reinforcement Learning via Advantage Guidance

arXiv:2610.19783v1 Announce Type: new Abstract: Reinforcement learning (RL) holds the promise of learning a policy that can outperform what is shown in the data. However, in practice, such systems su...

By Zhiyuan Zhou, Sergey Levine

diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Oct 15

Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving

arXiv:2610.19429v1 Announce Type: new Abstract: This paper presents a comprehensive comparison of four representative reinforcement learning (RL) methods in autonomous vehicle (AV) decision-making, i...

By Dechen Xie, Mushuang Liu

agents reinforcement-learning robotics safety

arXiv AI

Jul 3

Playing 20 Question Game with Policy-Based Reinforcement Learning

arXiv:1808. 07645v5 Announce Type: replace-cross Abstract: The 20 Questions (Q20) game is a well known game which encourages deductive reasoning and creativity.

By Huang Hu, Xianchao Wu, Bingfeng Luo, Chongyang Tao, Can Xu, Wei Wu, Zhan Chen

rag agents reinforcement-learning

arXiv Machine Learning

Jul 28

Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model

arXiv:2107. 08183v2 Announce Type: replace Abstract: High-dimensional state and action spaces combined with sparse reward structures in reinforcement learning (RL) environments typically require advanced control architectures.

By JaeYoon Kim, Junyu Xuan, Christy Liang, Farookh Hussain

reinforcement-learning benchmarks

arXiv Machine Learning

Sep 30

ABC: Advantage-Based Control Variates for Reinforcement Learning with Verifiable Rewards

arXiv:2609.36058v1 Announce Type: new Abstract: Recent progress in reinforcement learning with verifiable rewards (RLVR) has highlighted the effectiveness of simple critic-free policy-gradient method...

By Hsiao-Ru Pan, Florent Draye, Bernhard Sch\"olkopf

reinforcement-learning safety

arXiv AI

Oct 15

A Geometric Perspective on Reward Function Updates in Inverse Reinforcement Learning

arXiv:2610.14041v1 Announce Type: cross Abstract: Inverse Reinforcement Learning (IRL) is classically solved using Lagrangian optimisation, by jointly optimising a primal-dual problem with gradient d...

By Anish Diwan, Jan Peters, Oleg Arenz

reinforcement-learning

arXiv Machine Learning

Jul 21

Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials

arXiv:2105. 00990v3 Announce Type: replace Abstract: Autonomous control in high-dimensional, continuous state spaces is a persistent and important challenge in the fields of robotics and artificial intelligence.

By Adrian P. Pope, Jaime S. Ide, Daria Micovic, Henry Diaz, David Rosenbluth, Lee Ritholtz, Jason C. Twedt, Thayne T. Walker, Kevin Alcedo, Daniel Javorsek

agents reinforcement-learning robotics

arXiv Machine Learning

Oct 9

Adaptive Multi-Discriminator WGAN Framework for Resource-Constrained Internet of Vehicles Using Reinforcement Learning and Game Theory

The paper proposes an adaptive multi‑discriminator Wasserstein GAN (MD‑WGAN) framework for Internet of Vehicles (IoV) that combines reinforcement learning and game‑theoretic coordination to manage resource allocation for distributed training. Roadside units host generators with Deep Q‑Network agents that select discriminator subsets and coordinate training epochs, while a unified objective links adversarial quality to resource efficiency, communication overhead, and latency. Experiments on real‑world NGSIM trajectory data show comparable prediction accuracy to state‑of‑the‑art GANs, with a 28% reduction in CPU usage and 6% reduction in memory usage, while maintaining competitive communication overhead and latency.

By Farhoud Jafari Kaleibar, Amr M. Zaki, Marin Litoiu

agents diffusion reinforcement-learning benchmarks safety

arXiv Machine Learning

Aug 12

Efficient Hypergradient Descent for Inverse Reinforcement Learning

arXiv:2608. 11052v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) aims to recover a reward function under which the resulting policy reproduces the behavior observed in expert demonstrations.

By Nikita Sevriukov, Anna Barabanova, Uliana Gagarina, Karina Ivanova, Sofiia Kasaeva, Ilya Levin, Marina Sheshukova

reinforcement-learning

arXiv AI

Aug 24

AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning

arXiv:2508.14313v4 Announce Type: replace-cross Abstract: Test-time scaling strategies for Large Language Models predominantly rely on either reinforcement learning with sparse outcome rewards or sea...

By Can Jin, Yang Zhou, Qixin Zhang, Hongwu Peng, Di Zhang, Zihan Dong, Marco Pavone, Ligong Han, Zhang-Wei Hong, Tong Che, Dimitris N. Metaxas

llms reinforcement-learning benchmarks safety

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