<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>In many Reinforcement Learning (RL) tasks, the classical online interaction of the learning agent with the environment is impractical, either because such interaction is expensive or dangerous. In these cases, previous gathered data can be used, arising what is typically called Offline RL. However, this type of learning faces a large number of challenges, mostly derived from the fact that exploration/exploitation trade-off is overshadowed. In addition, the historical data is usually biased by the way it was obtained, typically, a sub-optimal controller, producing a distributional shift from historical data and the one required to learn the optimal policy. In this paper, we present a novel approach to deal with the uncertainty risen by the absence or sparse presence of some state-action pairs in the learning data. Our approach is based on shaping the reward perceived from the environment to ensure the task is solved. We present the approach and show that combining it with classic online RL methods make them perform as good as state of the art Offline RL algorithms such as CQL and BCQ. Finally, we show that using our method on top of established offline learning algorithms can improve them.</p>
</abstract>

In many Reinforcement Learning (RL) tasks, the classical online interaction of the learning agent with the environment is impractical, either because such interaction is expensive or dangerous. In these cases, previous gathered data can be used, arising what is typically called Offline RL. However, this type of learning faces a large number of challenges, mostly derived from the fact that exploration/exploitation trade-off is overshadowed. In addition, the historical data is usually biased by the way it was obtained, typically, a sub-optimal controller, producing a distributional shift from historical data and the one required to learn the optimal policy. In this paper, we present a novel approach to deal with the uncertainty risen by the absence or sparse presence of some state-action pairs in the learning data. Our approach is based on shaping the reward perceived from the environment to ensure the task is solved. We present the approach and show that combining it with classic online RL methods make them perform as good as state of the art Offline RL algorithms such as CQL and BCQ. Finally, we show that using our method on top of established offline learning algorithms can improve them.

Discrete Uncertainty Quantification For Offline Reinforcement Learning

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Discrete Uncertainty Quantification For Offline Reinforcement Learning"}

In many Reinforcement Learning (RL) tasks, the classical online interaction of the learning agent with the environment is impractical, either because such...

Discrete Uncertainty Quantification For Offline Reinforcement Learning

Publié en ligne: 30 oct. 2023

Pages: 273 - 287

Reçu: 02 juin 2023

Accepté: 07 oct. 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0019

Mots clés
Off-line Reinforcement Learning, uncertainty quantification, Machine Learning

© 2023 José Luis Pérez et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Discrete Uncertainty Quantification For Offline Reinforcement Learning

Publié en ligne: 30 oct. 2023

Pages: 273 - 287

Reçu: 02 juin 2023

Accepté: 07 oct. 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0019

Mots clésOff-line Reinforcement Learning, uncertainty quantification, Machine Learning

© 2023 José Luis Pérez et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Off-line Reinforcement Learning, uncertainty quantification, Machine Learning