Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions
This addresses safe exploration and deployment for physical systems, but it appears incremental as it builds on existing methods like control barrier functions.
The paper tackles the challenge of learning safe control policies for complex, long-horizon tasks in reinforcement learning by combining temporal logic with control Lyapunov and barrier functions, resulting in a flexible system that integrates model-free learning with model-based planning.
Using reinforcement learning to learn control policies is a challenge when the task is complex with potentially long horizons. Ensuring adequate but safe exploration is also crucial for controlling physical systems. In this paper, we use temporal logic to facilitate specification and learning of complex tasks. We combine temporal logic with control Lyapunov functions to improve exploration. We incorporate control barrier functions to safeguard the exploration and deployment process. We develop a flexible and learnable system that allows users to specify task objectives and constraints in different forms and at various levels. The framework is also able to take advantage of known system dynamics and handle unknown environmental dynamics by integrating model-free learning with model-based planning.