398 citations · 1.7k across the 20 of their papers we have counts for
21 papers
Latent-Constrained Conditional VAEs for Augmenting Large-Scale Climate Ensembles
Jacquelyn Shelton, Przemyslaw Polewski, Alexander Robel +2
Large climate-model ensembles are computationally expensive; yet many downstream analyses would benefit from additional, statistically consistent realizations of spatiotemporal cli…
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning
Patrick Emedom-Nnamdi, Abram L. Friesen, Bobak Shahriari +2
Standard approaches to sequential decision-making exploit an agent's ability to continually interact with its environment and improve its control policy. However, due to safety, et…
An Empirical Study of Implicit Regularization in Deep Offline RL
Caglar Gulcehre, Srivatsan Srinivasan, Jakub Sygnowski +5
Deep neural networks are the most commonly used function approximators in offline reinforcement learning. Prior works have shown that neural nets trained with TD-learning and gradi…
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach
Bobak Shahriari, Abbas Abdolmaleki, Arunkumar Byravan +6
Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control…
Regularized Behavior Value Estimation
Caglar Gulcehre, Sergio Gómez Colmenarejo, Ziyu Wang +7
Offline reinforcement learning restricts the learning process to rely only on logged-data without access to an environment. While this enables real-world applications, it also pose…
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov +15
Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to lea…