12 citations · 19 across the 7 of their papers we have counts for
3 papers · 1 filter
Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approximation
Shuhang Chen, Adithya Devraj, Andrey Bernstein +1
The ODE method has been a workhorse for algorithm design and analysis since the introduction of the stochastic approximation. It is now understood that convergence theory amounts t…
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation
Shuhang Chen, Adithya M. Devraj, Ana Bušić +1
This paper concerns error bounds for recursive equations subject to Markovian disturbances. Motivating examples abound within the fields of Markov chain Monte Carlo (MCMC) and Rein…
Q-learning with Uniformly Bounded Variance: Large Discounting is Not a Barrier to Fast Learning
Adithya M. Devraj, Sean P. Meyn
Sample complexity bounds are a common performance metric in the Reinforcement Learning literature. In the discounted cost, infinite horizon setting, all of the known bounds have a…