We derive and analyze learning algorithms for apprenticeship learning, policy evaluation, and policy gradient for average reward criteria. Existing algorithms explicitly require an upper bound on the mixing time. In contrast, we build on ideas from Markov chain theory and derive sampling algorithms that do not require such an upper bound. For these algorithms, we provide theoretical bounds on their sample-complexity and running time.
|Number of pages||10|
|Journal||Proceedings of Machine Learning Research|
|State||Published - 2020|
|Event||36th Conference on Uncertainty in Artificial Intelligence, UAI 2020 - Virtual, Online|
Duration: 3 Aug 2020 → 6 Aug 2020