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Paweł Wawrzyński1, Ajay Kumar Tanwani

  • 1Warsaw University of Technology, Institute of Control and Computation Engineering, Poland. pwawrzynski@gmail.com

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Summary
This summary is machine-generated.

This study introduces a real-time reinforcement learning algorithm for autonomous control tasks. The novel approach enhances efficiency and policy adjustment for complex robotics challenges.

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Area of Science:

  • Robotics and Control Systems
  • Machine Learning
  • Artificial Intelligence

Background:

  • Real-world control tasks demand efficient and autonomous reinforcement learning (RL) algorithms.
  • Current RL methods often struggle with the complexities of real-time adaptation and autonomous operation.
  • Bridging the gap between RL capabilities and practical control applications remains a significant challenge.

Purpose of the Study:

  • To present a novel real-time reinforcement learning algorithm designed for enhanced efficiency and autonomy.
  • To enable RL algorithms to autonomously adjust control policies and learning step-sizes in real-time.
  • To demonstrate the algorithm's effectiveness in solving complex, real-life control problems.

Main Methods:

  • Developed a real-time reinforcement learning algorithm based on the actor-critic with experience replay architecture.
  • Integrated an enhanced fixed-point algorithm for on-line neural network training to autonomously determine learning step-sizes.
  • Utilized previously collected samples for repeated adjustment of the control policy.

Main Results:

  • The proposed algorithm successfully demonstrated autonomous policy adjustment and step-size estimation.
  • Experimental studies with simulated octopus arm and half-cheetah environments validated the algorithm's feasibility.
  • The approach proved capable of solving difficult learning control problems autonomously and within reasonable timeframes.

Conclusions:

  • The presented real-time reinforcement learning algorithm offers a viable solution for autonomous control in complex scenarios.
  • The method enhances both the efficiency and autonomy of reinforcement learning for practical applications.
  • This work paves the way for more robust and adaptable RL-based control systems in robotics and beyond.