A model-free RL algorithm learns policies by sequentially maximizing safety probability, then LTL satisfaction probability, then discounted QoC rewards in unknown MDPs.
Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions
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abstract
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.
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A literature review of safe RL using Lyapunov and barrier functions that identifies a shift to model-free methods since 2017, well-defined open problems per approach class, and high-dimensional scalability as the main barrier.
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Model-Free Learning of Safe yet Effective Controllers
A model-free RL algorithm learns policies by sequentially maximizing safety probability, then LTL satisfaction probability, then discounted QoC rewards in unknown MDPs.
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A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
A literature review of safe RL using Lyapunov and barrier functions that identifies a shift to model-free methods since 2017, well-defined open problems per approach class, and high-dimensional scalability as the main barrier.