Generalized Maximum Causal Entropy for Inverse Reinforcement Learning

16 Nov 2019Tien MaiKennard ChanPatrick Jaillet

We consider the problem of learning from demonstrated trajectories with inverse reinforcement learning (IRL). Motivated by a limitation of the classical maximum entropy model in capturing the structure of the network of states, we propose an IRL model based on a generalized version of the causal entropy maximization problem, which allows us to generate a class of maximum entropy IRL models... (read more)

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