Formal Methods Assisted Training of Safe Reinforcement Learning Agents

Reinforcement learning (RL) is emerging as a powerful machine learning paradigm to develop autonomous safety critical systems; RL enables the systems to learn optimal control strategies by interacting with the environment. However, there is also widespread apprehension to deploying such systems in the real world since rigorously ensuring if they had learned safe strategies by interacting with an environment that is representative of the real world remains a challenge. Hence, there is a surge of interest to establish safety-focused RL techniques.