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.
Jan 31st 2023
Research Post
Jan 20th 2023
Research Post
Aug 8th 2022
Research Post
Read this research paper co-authored by Canada CIFAR AI Chair Angel Chang: Learning Expected Emphatic Traces for Deep RL
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