Learning a Low-Dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems.

Area of Science:

• Robotics
• Control Systems
• Machine Learning

Background:

• Reinforcement learning (RL) algorithms are powerful for controlling complex systems but require safety guarantees.
• Existing safe reinforcement learning (SRL) frameworks often rely on simplified system models, which are difficult to obtain for high-dimensional nonlinear dynamical systems.
• This limitation hinders the practical application of SRL in real-world scenarios.

Purpose of the Study:

• To develop a general, data-driven approach for efficiently learning a low-dimensional representation of the safe region in dynamical systems.
• To overcome the challenge of manually defining simplified system models for SRL.
• To enhance the accuracy and reliability of safety estimates for reinforcement learning algorithms.

Main Methods:

• A data-driven methodology is proposed to learn a low-dimensional representation of the safe region directly from system data.
• An online adaptation mechanism is utilized to continuously update this representation using feedback, thereby improving safety estimations.
• The approach is validated using a quadcopter system as a case study.

Main Results:

• The proposed method successfully identifies a more reliable and representative low-dimensional safe region compared to existing techniques.
• The learned representation provides more accurate safety estimates for reinforcement learning algorithms.
• The data-driven approach significantly reduces the effort required to define system models for SRL.

Conclusions:

• The developed data-driven approach effectively learns low-dimensional safe region representations for complex dynamical systems.
• This method enhances the applicability and reliability of safe reinforcement learning frameworks.
• The findings pave the way for safer deployment of RL in high-dimensional, nonlinear control applications.