2021
A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning
ICML 2021spotlight
Inverse reinforcement learning (IRL) is the task of finding a reward function that generates a desired optimal policy for a given Markov Decision Process (MDP). This paper develops an information-theoretic lower bound for the sample complexity of the finite state, finite action IRL problem. A geomet…