12 citations · 29 across the 5 of their papers we have counts for
5 papers
Efficient Imitation Without Demonstrations via Value-Penalized Auxiliary Control from Examples
Trevor Ablett, Bryan Chan, Jayce Haoran Wang +1
Common approaches to providing feedback in reinforcement learning are the use of hand-crafted rewards or full-trajectory expert demonstrations. Alternatively, one can use examples…
Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks
Trevor Ablett, Bryan Chan, Jonathan Kelly
Adversarial imitation learning (AIL) has become a popular alternative to supervised imitation learning that reduces the distribution shift suffered by the latter. However, AIL requ…
MLGOPerf: An ML Guided Inliner to Optimize Performance
Amir H. Ashouri, Mostafa Elhoushi, Yuzhe Hua +4
For the past 25 years, we have witnessed an extensive application of Machine Learning to the Compiler space; the selection and the phase-ordering problem. However, limited works ha…
Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning
Trevor Ablett, Bryan Chan, Jonathan Kelly
Effective exploration continues to be a significant challenge that prevents the deployment of reinforcement learning for many physical systems. This is particularly true for system…
Heteroscedastic Uncertainty for Robust Generative Latent Dynamics
Oliver Limoyo, Bryan Chan, Filip Marić +3
Learning or identifying dynamics from a sequence of high-dimensional observations is a difficult challenge in many domains, including reinforcement learning and control. The proble…