Abstract
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.
Original language | English |
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Pages | 430-439 |
Number of pages | 10 |
State | Published - 2020 |
Event | 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020 - Virtual, Online Duration: 3 Aug 2020 → 6 Aug 2020 |
Conference
Conference | 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020 |
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City | Virtual, Online |
Period | 3/08/20 → 6/08/20 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence