A Deep Reinforcement Learning Framework for Control of Robotic Manipulators in Simulated Environments-Reference-Cited by-同舟云学术

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A Deep Reinforcement Learning Framework for Control of Robotic Manipulators in Simulated Environments

Published:2024 Issue: Volume:12 Page:103133-103161
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Calderón-Cordova Carlos¹^ORCID,Sarango Roger¹,Castillo Darwin²^ORCID,Lakshminarayanan Vasudevan³^ORCID

Affiliation:

1. Department of Computer Science and Electronics, Universidad Técnica Particular de Loja, Loja, Ecuador

2. Department of Chemistry, Universidad Técnica Particular de Loja, Loja, Ecuador

3. Theoretical and Experimental Epistemology Laboratory, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON, Canada

Funder

Universidad Técnica Particular de Loja

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10606462.pdf?arnumber=10606462

Reference116 articles.

1. Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review

2. Neurodynamics Adaptive Reward and Action for Hand-to-Eye Calibration With Deep Reinforcement Learning

3. A Metaheuristic Optimization Approach for Trajectory Tracking of Robot Manipulators

4. Modern Control Laws for an Articulated Robotic Arm: Modeling and Simulation

5. Trajectory tracking control for robot manipulator using fractional order-fuzzy-PID controller;Mohammed;Int. J. Comput. Appl.,2016

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