arXiv Machine Learning

Maximising the Set-Piece Return: Optimising Football Corner Tactics with Graph Reinforcement Learning

arXiv:2606. 06353v1 Announce Type: new Abstract: Machine learning is increasingly employed for the evaluation of football tactics.

arXiv Machine Learning

Jul 31

Policy Gradient Steering: Interventions from Behavioral Objectives

arXiv:2607. 27574v1 Announce Type: new Abstract: Activation steering has emerged in large language models as a lightweight alternative for dynamically changing a model's behavior at inference time.

By Yoann Poupart, Aur\'elie Beynier, Nicolas Maudet

llms reinforcement-learning

arXiv AI

Jun 10

Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football

arXiv:2606. 11120v1 Announce Type: new Abstract: We recast pass evaluation in football (soccer) as a Monte Carlo Tree Search (MCTS)-like evaluation problem whose components mostly exist in the literature under different names: a value model (possession value), a world model (multi-agent trajectories with ball interactions), and a policy over counterfactual actions (sampling pass variants with noise).

By Andrew Kang, Priya Narasimhan

agents robotics

arXiv AI

Jul 2

Play Like Champions: Counterfactual Feedback Generation in Latent Space

arXiv:2607. 00190v1 Announce Type: cross Abstract: Recent advances in reinforcement learning have produced superhuman agents across a wide range of competitive games.

By Andrzej Bia{\l}ecki, Adam Mastalerz, Han Zhou

agents diffusion reinforcement-learning

arXiv AI

Jun 3

Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach

arXiv:2510. 23216v4 Announce Type: replace Abstract: While several high profile video games have served as testbeds for Deep Reinforcement Learning (DRL), this technique has rarely been employed by the game industry for crafting authentic AI behaviors.

By Alessandro Sestini, Joakim Bergdahl, Jean-Philippe Barrette-LaPierre, Florian Fuchs, Brady Chen, Fabio Zinno, Michael Jones, Linus Gissl\'en

agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 2

Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation

arXiv:2509. 03456v2 Announce Type: replace-cross Abstract: Off-policy evaluation (OPE) and off-policy learning (OPL) are foundational for decision-making in offline contextual bandits.

By Imad Aouali, Otmane Sakhi

arXiv AI

Jul 10

Feedback Manipulation Regularization: Enabling Offline Agent Alignment for Imitation Learning

arXiv:2607. 07859v1 Announce Type: new Abstract: Reinforcement learning (RL) research has increasingly shifted focus towards alignment, ensuring agents learn behaviors adhering to human values.

By Benjamin Poole, Minwoo Lee

agents reinforcement-learning robotics safety

arXiv AI

Jul 14

Training on Irrelevant States Implies Data Augmentation: Generalization in Contextual MDPs

arXiv:2410. 03565v4 Announce Type: replace-cross Abstract: In the zero-shot policy transfer (ZSPT) setting for contextual Markov decision processes (CMDP), agents train on a fixed, finite set of contexts and must generalize to new ones.

By Max Weltevrede, Caroline Horsch, Matthijs T. J. Spaan, Wendelin B\"ohmer

agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 25

Consistent Zero-Shot Imitation with Contrastive Goal Inference

arXiv:2510. 17059v2 Announce Type: replace Abstract: Zero-shot imitation learning requires an agent to reproduce expert behavior from a single demonstration without additional environment interaction or gradient updates at test time.

By Kathryn Wantlin, Chongyi Zheng, Benjamin Eysenbach

agents reinforcement-learning benchmarks

arXiv AI

Jun 11

The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning

arXiv:2505. 03296v2 Announce Type: replace-cross Abstract: We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation.

By Jan Ole von Hartz, Adrian R\"ofer, Joschka Boedecker, Abhinav Valada

robotics multimodal benchmarks

arXiv Machine Learning

Jul 8

Supervised Reward Inference

arXiv:2502. 18447v2 Announce Type: replace Abstract: Existing approaches to reward inference typically assume that humans provide demonstrations according to specific behavior models.

By Will Schwarzer, Jordan Schneider, Philip S. Thomas, Scott Niekum

robotics benchmarks

arXiv Machine Learning

Jun 2

PathCRF: Ball-Free Soccer Event Detection via Possession Path Inference from Player Trajectories

arXiv:2602. 12080v2 Announce Type: replace Abstract: Despite recent advances in AI, event data collection in soccer still relies heavily on labor-intensive manual annotation.

By Hyunsung Kim, Kunhee Lee, Sangwoo Seo, Sang-Ki Ko, Jinsung Yoon, Chanyoung Park

rag

arXiv Machine Learning

Jun 9

Intention Driven Identification of In-Possession Match Phases in Association Football through Temporal Graph Learning

arXiv:2606. 09289v1 Announce Type: new Abstract: Understanding tactical organisation of association football, hereafter referred to as football, requires identifying distinct match phases.

By Yuesen Li, Daniel Link

llms diffusion computer-vision

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