Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees

• URL: http://arxiv.org/abs/2102.07937v1
• Date: Tue, 16 Feb 2021 03:17:23 GMT
• Title: Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees
• Authors: Gregory Dexter, Kevin Bello, and Jean Honorio
• Abstract summary: Inverse Reinforcement Learning (IRL) is the problem of finding a reward function which describes observed/known expert behavior. We provide a new IRL algorithm for the continuous state space setting with unknown transition dynamics.
• Score: 31.122125783516726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inverse Reinforcement Learning (IRL) is the problem of finding a reward function which describes observed/known expert behavior. IRL is useful for automated control in situations where the reward function is difficult to specify manually, which impedes reinforcement learning. We provide a new IRL algorithm for the continuous state space setting with unknown transition dynamics by modeling the system using a basis of orthonormal functions. We provide a proof of correctness and formal guarantees on the sample and time complexity of our algorithm.

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