10.1k citations · 10.9k across the 10 of their papers we have counts for
11 papers
Hyperbolic Deep Reinforcement Learning
Edoardo Cetin, Benjamin Chamberlain, Michael Bronstein +1
We propose a new class of deep reinforcement learning (RL) algorithms that model latent representations in hyperbolic space. Sequential decision-making requires reasoning about the…
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning
Zhendong Wang, Jonathan J Hunt, Mingyuan Zhou
Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously collected static dataset, is an important paradigm of RL. Standard RL methods often pe…
An Analysis Of Entire Space Multi-Task Models For Post-Click Conversion Prediction
Conor O'Brien, Kin Sum Liu, James Neufeld +2
Industrial recommender systems are frequently tasked with approximating probabilities for multiple, often closely related, user actions. For example, predicting if a user will clic…
The Option Keyboard: Combining Skills in Reinforcement Learning
André Barreto, Diana Borsa, Shaobo Hou +8
The ability to combine known skills to create new ones may be crucial in the solution of complex reinforcement learning problems that unfold over extended periods. We argue that a…
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
Mehdi Mirza, Andrew Jaegle, Jonathan J. Hunt +9
Recent work in deep reinforcement learning (RL) has produced algorithms capable of mastering challenging games such as Go, chess, or shogi. In these works the RL agent directly obs…
Composing Entropic Policies using Divergence Correction
Jonathan J Hunt, Andre Barreto, Timothy P Lillicrap +1
Composing previously mastered skills to solve novel tasks promises dramatic improvements in the data efficiency of reinforcement learning. Here, we analyze two recent works composi…