3 papers
cs.LG2020
Making Sense of Reinforcement Learning and Probabilistic Inference
Brendan O'Donoghue, Ian Osband, Catalin Ionescu
Reinforcement learning (RL) combines a control problem with statistical estimation: The system dynamics are not known to the agent, but can be learned through experience. A recent…
cs.CV2019
Unsupervised Learning of Object Keypoints for Perception and Control
Tejas Kulkarni, Ankush Gupta, Catalin Ionescu +4
The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic s…
cs.LG2018
Unsupervised Control Through Non-Parametric Discriminative Rewards
David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni +3
Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsuperv…