Reinforcement Learning
Simplifying Constraint Inference with Inverse Reinforcement Learning
The field of inverse constrained RL, which seeks to infer constraints from expert data, is a promising step in this direction.
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
These challenges are partially due to a lack of structure or inductive bias in the neural networks typically used in learning the policy. One such form of structure that is commonly observed in multi-agent scenarios is symmetry.
Progressive Exploration-Conformal Learning for Sparsely Annotated Object Detection in Aerial Images
The ability to detect aerial objects with limited annotation is pivotal to the development of real-world aerial intelligence systems. In this work, we focus on a demanding but practical sparsely annotated object detection (SAOD) in aerial images, which encompasses a wider variety of aerial scenes with the same number of annotated objects.
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions Rui Y ang
These Q values are often objectives that offline algorithms aim to approximate.
