arXiv:2606. 19328v1 Announce Type: cross Abstract: Preference-based RL provides an approach to learning reward models from pairwise comparisons of behaviors, bypassing the need for explicit reward design.
By Mohamed Nabail, Leo Cheng, Jingmin Wang, Nicholas Rhinehart
The paper introduces Reward Ensemble under Confidence (REC), a probabilistic reward learning framework for preference-based reinforcement learning that models per‑timestep reward uncertainty using an ensemble of distributional reward models. REC incorporates uncertainty into the preference loss and uses model disagreement to drive exploration, achieving 88.4% of shaped‑reward performance on acrobatic quadrotor control versus 55.2% with standard Preference PPO. The authors train policies in simulation and transfer them zero‑shot to real quadrotors, demonstrating complex acrobatic maneuvers learned solely from human preference feedback, and validate REC on a continuous‑control benchmark.
By Colin Merk, Ismail Geles, Jiaxu Xing, Angel Romero, Giorgia Ramponi, Davide Scaramuzza
arXiv:2506. 13741v2 Announce Type: replace-cross Abstract: Preference-based reinforcement learning (PbRL) has emerged as a promising approach for learning behaviors from human feedback without predefined reward functions.
By Brahim Driss, Alex Davey, Riad Akrour
arXiv:2608. 08604v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) is a powerful framework for solving complex collaborative tasks, but it relies heavily on well-defined global reward functions.
By Ni Mu, Yao Luan, Yiqin Yang, Qing-Shan Jia
The paper introduces a diagnostic workflow for multi‑objective reinforcement learning (MORL) that reveals behavioral differences among policies on the Pareto front, which are not apparent from value vectors alone. It offers quantitative and visual tools to inspect these variations and demonstrates their effectiveness on both simple grid tasks and more complex continuous‑control benchmarks.
By Antonio Mone, Zuzanna Osika, Florian Felten, Pradeep K. Murukannaiah, Mark Fuge, Frans A. Oliehoek, Luciano Cavalcante Siebert
arXiv:2606. 03962v1 Announce Type: cross Abstract: Classical reinforcement learning (RL) typically seeks a deterministic policy that maximizes the expected sum of a scalar reward.
By Anthony GX-Chen, Ankit Anand, Gheorghe Comanici, Zaheer Abbas, Eser Ayg\"un, David Smalling, Shibl Mourad, Doina Precup, Andr\'e Barreto, Mark Rowland