Search Results for author: Adithya Devraj

Found 2 papers, 0 papers with code

The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning

no code implementations27 Oct 2021 Vivek Borkar, Shuhang Chen, Adithya Devraj, Ioannis Kontoyiannis, Sean Meyn

In addition to standard Lipschitz assumptions and conditions on the vanishing step-size sequence, it is assumed that the associated \textit{mean flow} $ \tfrac{d}{dt} \vartheta_t = \bar{f}(\vartheta_t)$, is globally asymptotically stable with stationary point denoted $\theta^*$, where $\bar{f}(\theta)=\text{ E}[f(\theta,\Phi)]$ with $\Phi$ having the stationary distribution of the chain.

reinforcement-learning Reinforcement Learning (RL)

Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approximation

no code implementations30 Sep 2020 Shuhang Chen, Adithya Devraj, Andrey Bernstein, Sean Meyn

(ii) With gain $a_t = g/(1+t)$ the results are not as sharp: the rate of convergence $1/t$ holds only if $I + g A^*$ is Hurwitz.

reinforcement-learning Reinforcement Learning (RL)

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