Research Post
Emphatic algorithms are temporal-difference learning algorithms that change their effective state distribution by selectively emphasizing and de-emphasizing their updates on different time steps. Recent works by Sutton, Mahmood and White (2015), and Yu (2015) show that by varying the emphasis in a particular way, these algorithms become stable and convergent under off-policy training with linear function approximation. This paper serves as a unified summary of the available results from both works. In addition, we demonstrate the empirical benefits from the flexibility of emphatic algorithms, including state-dependent discounting, state-dependent bootstrapping, and the user-specified allocation of function approximation resources.
Feb 15th 2022
Research Post
Read this research paper, co-authored by Amii Fellow and Canada CIFAR AI Chair Adam White: Learning Expected Emphatic Traces for Deep RL
Feb 15th 2022
Research Post
Read this research paper, co-authored by Canada CIFAR AI Chair Kevin Leyton-Brown: The Perils of Learning Before Optimizing
Feb 14th 2022
Research Post
Read this research paper, co-authored by Amii Fellows and Canada CIFAR AI Chairs Osmar Zaïane,and Lili Mou, Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision
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