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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Self-balancing robots require sophisticated control systems to maintain stability.
  • Reinforcement learning offers a promising approach for developing adaptive control strategies.

Purpose of the Study:

  • To implement and compare Q learning and deep Q network (DQN) algorithms for a self-balancing robot model.
  • To enable the robot to learn optimal actions for environmental balance through reinforcement.

Main Methods:

  • Utilized the Gazebo simulation environment to model a self-balancing robot.
  • Implemented two reinforcement learning algorithms: Q learning and deep Q network (DQN).
  • Conducted experiments with varying hyperparameters and analyzed performance curves.

Main Results:

  • Demonstrated the feasibility of using Q learning and DQN for robot self-balancing.
  • Performance curves illustrate the learning progress and effectiveness of the implemented algorithms.
  • Identified optimal hyperparameters for improved balancing capabilities.

Conclusions:

  • Both Q learning and DQN can effectively train a self-balancing robot.
  • The choice of hyperparameters significantly impacts learning efficiency and balancing performance.
  • This research provides a foundation for developing more robust autonomous balancing systems.