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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 6: AAAI-22 Technical Tracks 6

Learning Expected Emphatic Traces for Deep RL

February 1, 2023

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Authors

Ray Jiang

Deepmind


Shangtong Zhang

University of Oxford


Veronica Chelu

Mila/McGill University


Adam White

DeepMind


Hado van Hasselt

DeepMind


DOI:

10.1609/aaai.v36i6.20660


Abstract:

Off-policy sampling and experience replay are key for improving sample efficiency and scaling model-free temporal difference learning methods. When combined with function approximation, such as neural networks, this combination is known as the deadly triad and is potentially unstable. Recently, it has been shown that stability and good performance at scale can be achieved by combining emphatic weightings and multi-step updates. This approach, however, is generally limited to sampling complete trajectories in order, to compute the required emphatic weighting. In this paper we investigate how to combine emphatic weightings with non-sequential, off-line data sampled from a replay buffer. We develop a multi-step emphatic weighting that can be combined with replay, and a time-reversed n-step TD learning algorithm to learn the required emphatic weighting. We show that these state weightings reduce variance compared with prior approaches, while providing convergence guarantees. We tested the approach at scale on Atari 2600 video games, and observed that the new X-ETD(n) agent improved over baseline agents, highlighting both the scalability and broad applicability of our approach.

Topics: AAAI

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HOW TO CITE:

Ray Jiang||Shangtong Zhang||Veronica Chelu||Adam White||Hado van Hasselt Learning Expected Emphatic Traces for Deep RL Proceedings of the AAAI Conference on Artificial Intelligence (2022) 7015-7023.

Ray Jiang||Shangtong Zhang||Veronica Chelu||Adam White||Hado van Hasselt Learning Expected Emphatic Traces for Deep RL AAAI 2022, 7015-7023.

Ray Jiang||Shangtong Zhang||Veronica Chelu||Adam White||Hado van Hasselt (2022). Learning Expected Emphatic Traces for Deep RL. Proceedings of the AAAI Conference on Artificial Intelligence, 7015-7023.

Ray Jiang||Shangtong Zhang||Veronica Chelu||Adam White||Hado van Hasselt. Learning Expected Emphatic Traces for Deep RL. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.7015-7023.

Ray Jiang||Shangtong Zhang||Veronica Chelu||Adam White||Hado van Hasselt. 2022. Learning Expected Emphatic Traces for Deep RL. "Proceedings of the AAAI Conference on Artificial Intelligence". 7015-7023.

Ray Jiang||Shangtong Zhang||Veronica Chelu||Adam White||Hado van Hasselt. (2022) "Learning Expected Emphatic Traces for Deep RL", Proceedings of the AAAI Conference on Artificial Intelligence, p.7015-7023

Ray Jiang||Shangtong Zhang||Veronica Chelu||Adam White||Hado van Hasselt, "Learning Expected Emphatic Traces for Deep RL", AAAI, p.7015-7023, 2022.

Ray Jiang||Shangtong Zhang||Veronica Chelu||Adam White||Hado van Hasselt. "Learning Expected Emphatic Traces for Deep RL". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.7015-7023.

Ray Jiang||Shangtong Zhang||Veronica Chelu||Adam White||Hado van Hasselt. "Learning Expected Emphatic Traces for Deep RL". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 7015-7023.

Ray Jiang||Shangtong Zhang||Veronica Chelu||Adam White||Hado van Hasselt. Learning Expected Emphatic Traces for Deep RL. AAAI[Internet]. 2022[cited 2023]; 7015-7023.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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