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

Offline Reinforcement Learning as Anti-exploration

February 1, 2023

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Authors

Shideh Rezaeifar

University of Geneva


Robert Dadashi

Google Research, Brain Team


Nino Vieillard

Google Research, Brain Team Universite de Lorraine, CNRS, Inria


Léonard Hussenot

Google Research, Brain Team Universite de Lille, CNRS, Inria


Olivier Bachem

Google Research, Brain Team


Olivier Pietquin

Google Research - Brain Team


Matthieu Geist

Google Research, Brain Team


DOI:

10.1609/aaai.v36i7.20783


Abstract:

Offline Reinforcement Learning (RL) aims at learning an optimal control from a fixed dataset, without interactions with the system. An agent in this setting should avoid selecting actions whose consequences cannot be predicted from the data. This is the converse of exploration in RL, which favors such actions. We thus take inspiration from the literature on bonus-based exploration to design a new offline RL agent. The core idea is to subtract a prediction-based exploration bonus from the reward, instead of adding it for exploration. This allows the policy to stay close to the support of the dataset and practically extends some previous pessimism-based offline RL methods to a deep learning setting with arbitrary bonuses. We also connect this approach to a more common regularization of the learned policy towards the data. Instantiated with a bonus based on the prediction error of a variational autoencoder, we show that our simple agent is competitive with the state of the art on a set of continuous control locomotion and manipulation tasks.

Topics: AAAI

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HOW TO CITE:

Shideh Rezaeifar||Robert Dadashi||Nino Vieillard||Léonard Hussenot||Olivier Bachem||Olivier Pietquin||Matthieu Geist Offline Reinforcement Learning as Anti-exploration Proceedings of the AAAI Conference on Artificial Intelligence (2022) 8106-8114.

Shideh Rezaeifar||Robert Dadashi||Nino Vieillard||Léonard Hussenot||Olivier Bachem||Olivier Pietquin||Matthieu Geist Offline Reinforcement Learning as Anti-exploration AAAI 2022, 8106-8114.

Shideh Rezaeifar||Robert Dadashi||Nino Vieillard||Léonard Hussenot||Olivier Bachem||Olivier Pietquin||Matthieu Geist (2022). Offline Reinforcement Learning as Anti-exploration. Proceedings of the AAAI Conference on Artificial Intelligence, 8106-8114.

Shideh Rezaeifar||Robert Dadashi||Nino Vieillard||Léonard Hussenot||Olivier Bachem||Olivier Pietquin||Matthieu Geist. Offline Reinforcement Learning as Anti-exploration. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.8106-8114.

Shideh Rezaeifar||Robert Dadashi||Nino Vieillard||Léonard Hussenot||Olivier Bachem||Olivier Pietquin||Matthieu Geist. 2022. Offline Reinforcement Learning as Anti-exploration. "Proceedings of the AAAI Conference on Artificial Intelligence". 8106-8114.

Shideh Rezaeifar||Robert Dadashi||Nino Vieillard||Léonard Hussenot||Olivier Bachem||Olivier Pietquin||Matthieu Geist. (2022) "Offline Reinforcement Learning as Anti-exploration", Proceedings of the AAAI Conference on Artificial Intelligence, p.8106-8114

Shideh Rezaeifar||Robert Dadashi||Nino Vieillard||Léonard Hussenot||Olivier Bachem||Olivier Pietquin||Matthieu Geist, "Offline Reinforcement Learning as Anti-exploration", AAAI, p.8106-8114, 2022.

Shideh Rezaeifar||Robert Dadashi||Nino Vieillard||Léonard Hussenot||Olivier Bachem||Olivier Pietquin||Matthieu Geist. "Offline Reinforcement Learning as Anti-exploration". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.8106-8114.

Shideh Rezaeifar||Robert Dadashi||Nino Vieillard||Léonard Hussenot||Olivier Bachem||Olivier Pietquin||Matthieu Geist. "Offline Reinforcement Learning as Anti-exploration". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 8106-8114.

Shideh Rezaeifar||Robert Dadashi||Nino Vieillard||Léonard Hussenot||Olivier Bachem||Olivier Pietquin||Matthieu Geist. Offline Reinforcement Learning as Anti-exploration. AAAI[Internet]. 2022[cited 2023]; 8106-8114.


ISSN: 2374-3468


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