Capsule networks; Deep Q-learning; Deep reinforcement learning; Capsule network; Competitive performance; Convolutional neural network; Learning settings; Model free; Network-based architectures; Objects recognition; Q-learning; Translation invariants; Computer Science (all); Mathematics (all)
Abstract :
[en] In recent years, Capsule Networks (CapsNets) have achieved promising results in tasks such as object recognition thanks to their invariance characteristics towards pose and lighting. They have been proposed as an alternative to relational insensitive and translation invariant Convolutional Neural Networks (CNN). It has been empirically proven that CapsNets are capable of achieving competitive performance while requiring significantly fewer parameters. This is a desirable characteristic for Deep reinforcement learning which is known to be sample-inefficient during training. In this paper, we propose DCapsQN, a task-independent CapsNets-based architecture in the deep reinforcement learning setting. We experiment in the model-free reinforcement learning setting, more specifically in Deep Q-Learning using the Atari suite as the testbed of our analysis. To the best of our knowledge, this work constitutes the first CapsNets-based deep reinforcement learning architecture to learn state-action value functions without the need for task-specific adaptation. Our results show that, in this setting, DCapsQN requires 92% fewer parameters than the baseline. Moreover, despite their smaller size, the DCapsQN provides significant boosts in performance (score), ranging between 10%–77% while further stabilising the Deep Q-Learning. This is supported by our empirical results which shows that DCapsQN agents outperform the benchmark Double-DQN agent, with Prioritized experience replay, in eight out of the nine selected environments.
Research Center/Unit :
IDLab, university of Antwerpen
Disciplines :
Computer science
Author, co-author :
Singh, Akash ; Université de Liège - ULiège > HEC Liège : UER > UER Opérations : Systèmes d'information de gestion ; imec IDLab, University of Antwerpen, Antwerpen, Belgium
De Schepper, Tom; imec IDLab, University of Antwerpen, Antwerpen, Belgium
Mets, Kevin; imec IDLab, University of Antwerpen, Antwerpen, Belgium
Hellinckx, Peter; imec IDLab, University of Antwerpen, Antwerpen, Belgium
Oramas, José; imec IDLab, University of Antwerpen, Antwerpen, Belgium
Latré, Steven; imec IDLab, University of Antwerpen, Antwerpen, Belgium
Language :
English
Title :
Task Independent Capsule-Based Agents for Deep Q-Learning
Publication date :
2022
Event name :
BENELEARN
Event date :
10 to 12 November 2021
By request :
Yes
Audience :
International
Main work title :
Artificial Intelligence and Machine Learning
Main work alternative title :
[en] Communications in Computer and Information Science
Acknowledgement. This research received funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” programme.
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