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Overcoming Challenges of Applying Reinforcement Learning for Intelligent Vehicle Control.

Rafael Pina1, Haileleol Tibebu1, Joosep Hook1

  • 1Institute of Digital Technologies, Loughborough University London, 3 Lesney Avenue, London E20 3BS, UK.

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|December 10, 2021
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Summary
This summary is machine-generated.

Reinforcement learning (RL) in intelligent vehicle control can be improved using Curriculum Learning (CL) and sim-to-real transfer. These methods address safety and practical challenges in real-world applications.

Keywords:
curriculum learningintelligent mobilityreinforcement learningsim-to-real worldvehicle control

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Intelligent Vehicle Control

Background:

  • Reinforcement learning (RL) offers potential for intelligent vehicle control but faces practical challenges.
  • Ensuring safety and comprehensive training in real-world scenarios remains difficult for RL agents.

Purpose of the Study:

  • Investigate the impact of RL in intelligent vehicle control, specifically path planning.
  • Propose and evaluate methods to bridge the gap between theoretical RL and practical implementation.

Main Methods:

  • Curriculum Learning (CL) to structure the agent's learning process gradually.
  • Sim-to-real policy transfer using Arduino Yun-controlled robots for real-world testing.

Main Results:

  • Curriculum Learning significantly aids in training RL agents for intelligent vehicle control.
  • RL policies successfully transferred from simulation to reality, even on resource-limited platforms.

Conclusions:

  • CL and sim-to-real transfer are effective strategies for deploying RL in intelligent vehicle control.
  • These approaches enhance the practicality and safety of RL applications in autonomous systems.