TY - CONF
T1 - Unknown mixing times in apprenticeship and reinforcement learning
AU - Zahavy, Tom
AU - Cohen, Alon
AU - Kaplan, Haim
AU - Mansour, Yishay
N1 - Publisher Copyright:
© Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85101630463&partnerID=8YFLogxK
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AN - SCOPUS:85101630463
SP - 430
EP - 439
T2 - 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020
Y2 - 3 August 2020 through 6 August 2020
ER -
