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Found 2 papers, 1 papers with code
A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning
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).
On the Correctness and Sample Complexity of Inverse Reinforcement Learning
The paper further analyzes the proposed formulation of inverse reinforcement learning with $n$ states and $k$ actions, and shows a sample complexity of $O(n^2 \log (nk))$ 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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