Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning-Reference-Cited by-同舟云学术

Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning

Published:2022-10 Issue:5 Volume:6 Page:1137-1156
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Sledge Isaac J.¹^ORCID,Bryner Darshan W.¹^ORCID,Principe Jose C.²

Affiliation:

1. Advanced Signal Processing and Automated Target Recognition Branch, Naval Surface Warfare Center, Panama City, FL, USA

2. Department of Electrical and Computer Engineering, and the Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA

Funder

Office of Naval Research

in-house the Laboratory Independent Research

ONR

Naval Innovation in Science and Engineering

Naval Sea Systems Command

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computational Mathematics,Control and Optimization,Computer Science Applications

Link

Reference77 articles.

3. Reinforcement learning methods for continuous-time Markov decision problems;bradtke;Proc Adv Neural Inf Process Syst,1994

5. The asymptotic convergence rate of $Q$-learning;szepesvári;Proc Adv Neural Inf Process Syst,1997

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