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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 11: AAAI-21 Technical Tracks 11

Expected Eligibility Traces

February 1, 2023

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Authors

Hado van Hasselt

DeepMind


Sephora Madjiheurem

University College London


Matteo Hessel

DeepMind


David Silver

DeepMind


André Barreto

DeepMind


Diana Borsa

DeepMind


DOI:

10.1609/aaai.v35i11.17200


Abstract:

The question of how to determine which states and actions are responsible for a certain outcome is known as the credit assignment problem and remains a central research question in reinforcement learning and artificial intelligence. Eligibility traces enable efficient credit assignment to the recent sequence of states and actions experienced by the agent, but not to counterfactual sequences that could also have led to the current state. In this work, we introduce expected eligibility traces. Expected traces allow, with a single update, to update states and actions that could have preceded the current state, even if they did not do so on this occasion. We discuss when expected traces provide benefits over classic (instantaneous) traces in temporal-difference learning, and show that some- times substantial improvements can be attained. We provide a way to smoothly interpolate between instantaneous and expected traces by a mechanism similar to bootstrapping, which ensures that the resulting algorithm is a strict generalisation of TD(λ). Finally, we discuss possible extensions and connections to related ideas, such as successor features.

Topics: AAAI

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HOW TO CITE:

Hado van Hasselt||Sephora Madjiheurem||Matteo Hessel||David Silver||André Barreto||Diana Borsa Expected Eligibility Traces Proceedings of the AAAI Conference on Artificial Intelligence (2021) 9997-10005.

Hado van Hasselt||Sephora Madjiheurem||Matteo Hessel||David Silver||André Barreto||Diana Borsa Expected Eligibility Traces AAAI 2021, 9997-10005.

Hado van Hasselt||Sephora Madjiheurem||Matteo Hessel||David Silver||André Barreto||Diana Borsa (2021). Expected Eligibility Traces. Proceedings of the AAAI Conference on Artificial Intelligence, 9997-10005.

Hado van Hasselt||Sephora Madjiheurem||Matteo Hessel||David Silver||André Barreto||Diana Borsa. Expected Eligibility Traces. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.9997-10005.

Hado van Hasselt||Sephora Madjiheurem||Matteo Hessel||David Silver||André Barreto||Diana Borsa. 2021. Expected Eligibility Traces. "Proceedings of the AAAI Conference on Artificial Intelligence". 9997-10005.

Hado van Hasselt||Sephora Madjiheurem||Matteo Hessel||David Silver||André Barreto||Diana Borsa. (2021) "Expected Eligibility Traces", Proceedings of the AAAI Conference on Artificial Intelligence, p.9997-10005

Hado van Hasselt||Sephora Madjiheurem||Matteo Hessel||David Silver||André Barreto||Diana Borsa, "Expected Eligibility Traces", AAAI, p.9997-10005, 2021.

Hado van Hasselt||Sephora Madjiheurem||Matteo Hessel||David Silver||André Barreto||Diana Borsa. "Expected Eligibility Traces". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.9997-10005.

Hado van Hasselt||Sephora Madjiheurem||Matteo Hessel||David Silver||André Barreto||Diana Borsa. "Expected Eligibility Traces". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 9997-10005.

Hado van Hasselt||Sephora Madjiheurem||Matteo Hessel||David Silver||André Barreto||Diana Borsa. Expected Eligibility Traces. AAAI[Internet]. 2021[cited 2023]; 9997-10005.


ISSN: 2374-3468


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