Papers › On the Correctness and Sample Complexity of Inverse Reinforcement Learning
On the Correctness and Sample Complexity of Inverse Reinforcement Learning
Abi Komanduru, Jean Honorio
Inverse reinforcement learning (IRL) is the problem of finding a reward function that generates a given optimal policy for a given Markov Decision Process. This paper looks at an algorithmic-independent geometric analysis of the IRL problem with finite states and actions. A L1-regularized Support Vector Machine formulation of the IRL problem motivated by the geometric analysis is then proposed with the basic objective of the inverse reinforcement problem in mind: to find a reward function that generates a specified optimal policy. The paper further analyzes the proposed formulation of inverse reinforcement learning with n states and k actions, and shows a sample complexity of O(n² log(nk)) for recovering a reward function that generates a policy that satisfies Bellman's optimality condition with respect to the true transition probabilities.
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