4 citations · 6 across the 4 of their papers we have counts for
4 papers
Value-aware Importance Weighting for Off-policy Reinforcement Learning
Kristopher De Asis, Eric Graves, Richard S. Sutton
Importance sampling is a central idea underlying off-policy prediction in reinforcement learning. It provides a strategy for re-weighting samples from a distribution to obtain unbi…
Importance Sampling Placement in Off-Policy Temporal-Difference Methods
Eric Graves, Sina Ghiassian
A central challenge to applying many off-policy reinforcement learning algorithms to real world problems is the variance introduced by importance sampling. In off-policy learning,…
Off-Policy Actor-Critic with Emphatic Weightings
Eric Graves, Ehsan Imani, Raksha Kumaraswamy +1
A variety of theoretically-sound policy gradient algorithms exist for the on-policy setting due to the policy gradient theorem, which provides a simplified form for the gradient. T…
An Off-policy Policy Gradient Theorem Using Emphatic Weightings
Ehsan Imani, Eric Graves, Martha White
Policy gradient methods are widely used for control in reinforcement learning, particularly for the continuous action setting. There have been a host of theoretically sound algorit…