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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 7: AAAI-22 Technical Tracks 7

Same State, Different Task: Continual Reinforcement Learning without Interference

February 1, 2023

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Authors

Samuel Kessler

University of Oxford


Jack Parker-Holder

University of Oxford


Philip Ball

University of Oxford


Stefan Zohren

University of Oxford


Stephen J. Roberts

University of Oxford


DOI:

10.1609/aaai.v36i7.20674


Abstract:

Continual Learning (CL) considers the problem of training an agent sequentially on a set of tasks while seeking to retain performance on all previous tasks. A key challenge in CL is catastrophic forgetting, which arises when performance on a previously mastered task is reduced when learning a new task. While a variety of methods exist to combat forgetting, in some cases tasks are fundamentally incompatible with each other and thus cannot be learnt by a single policy. This can occur, in reinforcement learning (RL) when an agent may be rewarded for achieving different goals from the same observation. In this paper we formalize this "interference" as distinct from the problem of forgetting. We show that existing CL methods based on single neural network predictors with shared replay buffers fail in the presence of interference. Instead, we propose a simple method, OWL, to address this challenge. OWL learns a factorized policy, using shared feature extraction layers, but separate heads, each specializing on a new task. The separate heads in OWL are used to prevent interference. At test time, we formulate policy selection as a multi-armed bandit problem, and show it is possible to select the best policy for an unknown task using feedback from the environment. The use of bandit algorithms allows the OWL agent to constructively re-use different continually learnt policies at different times during an episode. We show in multiple RL environments that existing replay based CL methods fail, while OWL is able to achieve close to optimal performance when training sequentially.

Topics: AAAI

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HOW TO CITE:

Samuel Kessler||Jack Parker-Holder||Philip Ball||Stefan Zohren||Stephen J. Roberts Same State, Different Task: Continual Reinforcement Learning without Interference Proceedings of the AAAI Conference on Artificial Intelligence (2022) 7143-7151.

Samuel Kessler||Jack Parker-Holder||Philip Ball||Stefan Zohren||Stephen J. Roberts Same State, Different Task: Continual Reinforcement Learning without Interference AAAI 2022, 7143-7151.

Samuel Kessler||Jack Parker-Holder||Philip Ball||Stefan Zohren||Stephen J. Roberts (2022). Same State, Different Task: Continual Reinforcement Learning without Interference. Proceedings of the AAAI Conference on Artificial Intelligence, 7143-7151.

Samuel Kessler||Jack Parker-Holder||Philip Ball||Stefan Zohren||Stephen J. Roberts. Same State, Different Task: Continual Reinforcement Learning without Interference. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.7143-7151.

Samuel Kessler||Jack Parker-Holder||Philip Ball||Stefan Zohren||Stephen J. Roberts. 2022. Same State, Different Task: Continual Reinforcement Learning without Interference. "Proceedings of the AAAI Conference on Artificial Intelligence". 7143-7151.

Samuel Kessler||Jack Parker-Holder||Philip Ball||Stefan Zohren||Stephen J. Roberts. (2022) "Same State, Different Task: Continual Reinforcement Learning without Interference", Proceedings of the AAAI Conference on Artificial Intelligence, p.7143-7151

Samuel Kessler||Jack Parker-Holder||Philip Ball||Stefan Zohren||Stephen J. Roberts, "Same State, Different Task: Continual Reinforcement Learning without Interference", AAAI, p.7143-7151, 2022.

Samuel Kessler||Jack Parker-Holder||Philip Ball||Stefan Zohren||Stephen J. Roberts. "Same State, Different Task: Continual Reinforcement Learning without Interference". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.7143-7151.

Samuel Kessler||Jack Parker-Holder||Philip Ball||Stefan Zohren||Stephen J. Roberts. "Same State, Different Task: Continual Reinforcement Learning without Interference". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 7143-7151.

Samuel Kessler||Jack Parker-Holder||Philip Ball||Stefan Zohren||Stephen J. Roberts. Same State, Different Task: Continual Reinforcement Learning without Interference. AAAI[Internet]. 2022[cited 2023]; 7143-7151.


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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