Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes.

Area of Science:

• Control Theory
• Machine Learning
• Artificial Intelligence

Background:

• Machine learning, particularly reinforcement learning (RL), is a growing paradigm in control theory.
• RL is often perceived as less intuitive than model-based methods, hindering experimental design and implementation.
• There is a need to bridge the gap between theoretical RL concepts and practical, intuitive understanding through experimentation.

Purpose of the Study:

• To propose a general framework for reproducing successful reinforcement learning experiments and simulations.
• To provide a comprehensive understanding of RL algorithms by applying them to the classic inverted pendulum benchmark.
• To discuss the implementation and robustness of RL on real-world systems.

Main Methods:

• Utilized the inverted pendulum as a benchmark system for control strategy evaluation.
• Implemented and compared two RL algorithms: basic Q-Learning and Deep Q-Networks (DQN).
• Conducted both physical experiments and simulations using a virtual environment.

Main Results:

• High-accuracy control of the inverted pendulum was achieved within hours of learning.
• Simulations offered insights into the impact of physical parameters on control performance.
• The feasibility and robustness of the RL approach were successfully tested.

Conclusions:

• Reinforcement learning can be effectively applied to control problems like the inverted pendulum, achieving high accuracy.
• The proposed framework facilitates a more intuitive understanding and practical implementation of RL algorithms.
• The study highlights the potential of RL for real-world control applications.