What are the practical applications of inverse reinforcement learning?
I think that the field has moved on from that paper. There is a trend to use the data from the expert to either "precondition" the agent or extracting a policy directly from the data. You can search for imitation learning or behavioral cloning. Some of these algorithms: Generative Adversarial Learning, DAGGER and Deep Q-learning from Demonstrations . AlphaGo used Supervised Learning as well to get to a good policy before getting trained in a RL setting. So instead of trying to recover a complicated reward function from the data you can use the above methods to get a good policy or initialize the parameters of your agent to more promising directions. Hope this helps!