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The primary objective of this research work is to investigate model-free path planning for reconfigurable robots using value and policy iterations. The focus is on developing and evaluating an autonomous algorithm for robot path planning. Initially, the A-Star algorithm was modified to incorporate model-based learning. Subsequently, model-free reinforced learning was incorporated through value iteration and policy iteration. The experimental validations of modified A-Star algorithm is conducted in python virtual environment.

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Reinforcement Learning in Simultaneous Localization and Mapping

  • 会议论文

  • 发表日期：

    2025年

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  • Abhik Kumar,
  • Sudarshan K. Valluru,
  • M. M. Rayguru

摘要

The primary objective of this research work is to investigate model-free path planning for reconfigurable robots using value and policy iterations. The focus is on developing and evaluating an autonomous algorithm for robot path planning. Initially, the A-Star algorithm was modified to incorporate model-based learning. Subsequently, model-free reinforced learning was incorporated through value iteration and policy iteration. The experimental validations of modified A-Star algorithm is conducted in python virtual environment.