Safe Reinforcement Learning for Probabilistic Safety Verification: A Lyapunov-Based Approach
허수빈 · Seoul National University Open Repository (Seoul National University) · 2020
Emerging applications in robotic and autonomous systems, such as autonomous driving and robotic surgery, often involve critical safety constraints that must be satisfied even when information about system models is limited.In this regard, we propose a model-free safety specification method that learns the maximal probability of safe operation by carefully combining probabilistic reachability analysis and safe reinforcement learning (RL).Our approach constructs a Lyapunov function with respect to a safe policy to restrain each policy improvement stage.As a result, it yields a sequence of safe policies that determine the range of safe operation, called the safe set, which monotonically expands and gradually converges.We also develop an efficient safe exploration scheme that accelerates the process of identifying the safety of unexamined states.Exploiting the Lyapunov shieding, our method regulates the exploratory policy to avoid dangerous states with high confidence.To handle high-dimensional systems, we further extend our approach to deep RL by introducing a Lagrangian relaxation technique to establish a tractable actor-critic algorithm.The empirical performance of our method is demonstrated through continuous control benchmark problems, such as a reaching task on a planar robot arm.