Abstract
Recently Introduced Deep Reinforcement Learning (DRL) Techniques in Discrete-Time Have Resulted in Significant Advances in Online Games, Robotics, and So On. Inspired from Recent Developments, We Have Proposed an Approach Referred to as Quantile Critic with Spiking Actor and Normalized Ensemble (QC-SANE) for Continuous Control Problems, Which Uses Quantile Loss to Train Critic and a Spiking Neural Network (NN) to Train an Ensemble of Actors. the NN Does an Internal Normalization using a Scaled Exponential Linear Unit (SELU) Activation Function and Ensures Robustness. the Empirical Study on Multijoint Dynamics with Contact (MuJoCo)-Based Environments Shows Improved Training and Test Results Than the State-Of-The-Art Approach: Population Coded Spiking Actor Network (PopSAN).
Recommended Citation
S. Gupta et al., "QC-SANE: Robust Control in DRL using Quantile Critic with Spiking Actor and Normalized Ensemble," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 9, pp. 6656 - 6662, Institute of Electrical and Electronics Engineers, Sep 2023.
The definitive version is available at https://doi.org/10.1109/TNNLS.2021.3129525
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Actor critic; deep reinforcement learning (DRL); ensemble; reinforcement learning (RL); robust control; spiking neural network (SNN)
International Standard Serial Number (ISSN)
2162-2388; 2162-237X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Sep 2023
PubMed ID
34874871
Comments
Netaji Subhas University of Technology, Grant None