Inverse reinforcement learning is about using expert trajectories to learn a reward function. Now the most successful method is Maximum Entropy Inverse Reinforcement Learning. But in that, you need a model-based reinforcement learning. But most of the practical world problems are model-free which is hard to calculate the state transition probabilities. So how can we use this inverse reinforcement learning in real-world problems?
closed as too broad by D.W., Stephen Rauch♦, Siong Thye Goh, Aditya, Toros91 Apr 6 '18 at 1:08
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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!