Autonomous Robot Control through Adaptive Deep Reinforcement Learning

Authors

  • Sandeep Kumar Dasa  Independent Researcher, USA

Keywords:

Adaptive Deep Reinforcement Learning, Autonomous Robot Control, Real-Time Deep Learning Algorithms, Navigation and Manipulation, Dynamic Environment Interaction

Abstract

The combination of adaptive deep reinforcement learning with autonomous robot control is considered in this work to be a significant contribution to robotics. This paper aims to discuss how these DRL techniques can help robots make autonomous decisions based on the output of the environment feedback to accomplish tasks such as navigation, manipulation, and interactions with dynamic environments. Real-time change and challenges are catered for effectively using real-time deep learning algorithms qualified by reinforcement learning paradigms. By experience, the robot achieves the best control policies, and the system remains flexible, robust, and efficient. Real-life examples and simulation scenarios are described in this paper to illustrate the prospects and difficulties of this area.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2022-03-17

Issue

Volume 9, Issue 2, March-April-2022

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sandeep Kumar Dasa "Autonomous Robot Control through Adaptive Deep Reinforcement Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 2, pp.503-509, March-April-2022.