We’re launching a transfer learning contest that measures a reinforcement learning algorithm’s ability to generalize from previous experience.
arXiv:2609.07575v1 Announce Type: cross
Abstract: This work introduces an alternative view of efficient exploration and studies its theoretical and empirical implications in the absence of extrinsic...
By Mikel Malag\'on, Jon Vadillo, Josu Ceberio, Michael Bowling, Jose A. Lozano
arXiv:2606. 00840v1 Announce Type: new Abstract: This work presents a logic-driven framework to evaluate the performance of reinforcement learning (RL) algorithms in their ability to generalize to unseen tasks.
By Vignesh Subramanian, {\DJ}or{\dj}e \v{Z}ikeli\'c, Suguman Bansal
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
The paper investigates when intrinsic rewards effectively drive exploration in reinforcement learning. It introduces a formal criterion that evaluates policies based on the counterfactual information they acquire, comparing how well their histories can replace experience from alternative policies. Using a simple environment, the authors show that common intrinsic reward objectives—count-based, prediction-error, empowerment, and information-gain—can lead to Pareto-suboptimal exploration under this criterion, and they propose conditions and a new objective that better align with optimal exploration.
By Scott W. Viteri (Stanford University), Laura Gomezjurado Gonzalez (Stanford University), Clark Barrett (Stanford University)
arXiv:2605.14211v4 Announce Type: replace
Abstract: Long-horizon visuomotor tasks remain a fundamental challenge in AI, as current methods rely on hand-engineered rewards or action-labeled demonstrat...
By Benjamin Schneider, Xavier Schneider, Victor Zhong, Sun Sun