Inverse Reinforcement Learning in Contextual MDPs

Real-world sequential decision problems often share two important properties – the reward function is often unknown, yet expert demonstrations can be acquired, and there often exists a static parameter, also known as the context, which determines certain aspects of the problem. In this work we formalize the Contextual Inverse Reinforcement Learning framework, propose several algorithms and analyze them both theoretically and empirically.