Verified Probabilistic Policies for Deep Reinforcement Learning

Understand

Deep reinforcement learning is an increasingly popular technique for synthesising policies to control an agent's interaction with its environment.

• There is also growing interest in formally verifying that such policies are correct and execute safely.
• Progress has been made in this area by building on existing work for verification of deep neural networks and of continuous-state dynamical systems.
• In this paper, we tackle the problem of verifying probabilistic policies for deep reinforcement learning, which are used to, for example, tackle adversarial environments, break symmetries and manage trade-offs.