all AI news
Stochastic first-order methods for average-reward Markov decision processes. (arXiv:2205.05800v4 [cs.LG] UPDATED)
June 29, 2022, 1:11 a.m. | Tianjiao Li, Feiyang Wu, Guanghui Lan
stat.ML updates on arXiv.org arxiv.org
We study the problem of average-reward Markov decision processes (AMDPs) and
develop novel first-order methods with strong theoretical guarantees for both
policy evaluation and optimization. Existing on-policy evaluation methods
suffer from sub-optimal convergence rates as well as failure in handling
insufficiently random policies, e.g., deterministic policies, for lack of
exploration. To remedy these issues, we develop a novel variance-reduced
temporal difference (VRTD) method with linear function approximation for
randomized policies along with optimal convergence guarantees, and an
exploratory variance-reduced temporal …
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Data Management Associate
@ EcoVadis | Ebène, Mauritius
Senior Data Engineer
@ Telstra | Telstra ICC Bengaluru