747 citations · 777 across the 7 of their papers we have counts for
6 papers · 1 filter
Representation Matters: Improving Perception and Exploration for Robotics
Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck +8
Projecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domain…
Differentially Private Bayesian Inference for Generalized Linear Models
Tejas Kulkarni, Joonas Jälkö, Antti Koskela +2
Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst's repertoire and often used on sensitive datasets. A large body of…
Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning
Aishwarya Agrawal, Mateusz Malinowski, Felix Hill +3
Advances in Deep Reinforcement Learning have led to agents that perform well across a variety of sensory-motor domains. In this work, we study the setting in which an agent must le…
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…
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Tejas D. Kulkarni, Karthik R. Narasimhan, Ardavan Saeedi +1
Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient e…
Understanding Visual Concepts with Continuation Learning
William F. Whitney, Michael Chang, Tejas Kulkarni +1
We introduce a neural network architecture and a learning algorithm to produce factorized symbolic representations. We propose to learn these concepts by observing consecutive fram…