APSN: adaptive prediction sample network in Deep Q learning

Shijie Chu

doi:10.1117/12.3031933

8 June 2024 APSN: adaptive prediction sample network in Deep Q learning

Shijie Chu

Proceedings Volume 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024); 131711V (2024) https://doi.org/10.1117/12.3031933
Event: 3rd International Conference on Algorithms, Microchips and Network Applications (AMNA 2024), 2024, Jinan, China

Abstract

Deep Q learning is a crucial method of deep reinforcement learning and has achieved remarkable success in multiple applications. However, Deep Q-learning suffers from low sample efficiency. To overcome this limitation, we introduce a novel algorithm, adaptive prediction sample network (APSN), to improve the sample efficiency. APSN is designed to predict the importance of each sample to policy updates, enabling efficient sample selection. We introduce a new metric to evaluate the importance of samples and use it to train the APSN network. In the experimental parts, we evaluate our algorithm on four Atari games in OpenAI Gym and compare APSN with SDQN. Experimental results show that APSN performs better in terms of sample efficiency and provides an effective solution for improving the sample efficiency of deep reinforcement learning. This research result is expected to promote the performance of deep reinforcement learning algorithms in practical applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shijie Chu "APSN: adaptive prediction sample network in Deep Q learning", Proc. SPIE 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024), 131711V (8 June 2024); https://doi.org/10.1117/12.3031933

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available