[3Q6-GS-8] Robot and real worlds:

[3Q6-GS-8-05] Deployment of Control Barrier Functions for Hi-performance System Design with Safety in Reinforcement Learning Approach

〇Kento Nagata¹, Sachiyo Arai¹ (1. Chiba University)

Keywords:Reinforcement Learning, Control Theory, Control Barrier Functions

Deep reinforcement learning is expected to be used not only for Go and Shogi, but also for controlling robots that operate in real space and in coexistence with humans, such as self-driving cars. In recent years, research has been conducted on safety-oriented algorithms such as collision avoidance with obstacles and humans for real-world applications of reinforcement learning involving trial-and-error. However, the emphasis on safety has led to methods that suppress optimality and other aspects of task processing.
In contrast, this study aims to obtain safe and high-performance control laws by combining deep reinforcement learning (DRL) and control barrier functions (CBFs) using environmental models. In the proposed method, the CBF uses a nominal model based on physical laws to identify regions that guarantee the safety of the DRL controller's operation. It also uses a Gaussian process model derived from measurement data to allow handling of uncertainties in the unknown environment that cannot be represented by the nominal model.