Trustworthy Reinforcement Learning: Opportunities and Challenges

Reinforcement Learning (RL) has long outgrown the traditional representations that guaranteed policy convergence but severely limited its application to complex domains. Modern Deep RL enables far richer and complex behaviour, yet at the cost of transparency and explainability. While these latter issues have recently received much attention in Machine Learning, they are underexplored in RL. In this talk, I will discuss them from multiple angles, survey state-of-the-art approaches, including recent developments in policy distillation and formal guarantees, and touch upon the related question of fairness.