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Found 15 papers, 1 papers with code
A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks
We show, in theory and through experiments, that our algorithm updates have low variance, and the training loss reduces in a smooth manner.
Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays
We generalize the Borkar-Meyn stability Theorem (BMT) to distributed stochastic approximations (SAs) with information delays that possess an arbitrary moment bound.
Distributed gradient-based optimization in the presence of dependent aperiodic communication
We show: If for any $p \ge0$ the processes that describe the success of communication between agents in a SSC network are $\alpha$-mixing with $n^{p-1}\alpha(n)$ summable, then the associated AoI processes are stochastically dominated by a random variable with finite $p$-th moment.
3DPG: Distributed Deep Deterministic Policy Gradient Algorithms for Networked Multi-Agent Systems
We prove the asymptotic convergence of 3DPG even in the presence of potentially unbounded Age of Information (AoI).
Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis
Deep Q-Learning is an important reinforcement learning algorithm, which involves training a deep neural network, called Deep Q-Network (DQN), to approximate the well-known Q-function.
Deep reinforcement learning for scheduling in large-scale networked control systems
This work considers the problem of control and resource scheduling in networked systems.
DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme
This algorithm is a two fold improvement over the classic Simultaneous Perturbation Stochastic Approximations (SPSA) algorithm.
Multi-Stage Reinforcement Learning For Object Detection
We compare an approach that is based purely on zoom actions with one that is extended by a second refinement stage to fine-tune the bounding box after each zoom step.
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling
The main contribution of this paper is to develop a deep reinforcement learning-based \emph{control-aware} scheduling (\textsc{DeepCAS}) algorithm to tackle these issues.
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning
In this paper, we present verifiable sufficient conditions for stability and convergence of asynchronous SAs with biased approximation errors.
Analyzing Approximate Value Iteration Algorithms
In this paper, we consider the stochastic iterative counterpart of the value iteration scheme wherein only noisy and possibly biased approximations of the Bellman operator are available.
Analysis of gradient descent methods with non-diminishing, bounded errors
The main aim of this paper is to provide an analysis of gradient descent (GD) algorithms with gradient errors that do not necessarily vanish, asymptotically.
Stability of Stochastic Approximations with `Controlled Markov' Noise and Temporal Difference Learning
Analyzing this class of algorithms is important, since many reinforcement learning (RL) algorithms can be cast as SAs driven by a `controlled Markov' process.
A Generalization of the Borkar-Meyn Theorem for Stochastic Recursive Inclusions
In this paper the stability theorem of Borkar and Meyn is extended to include the case when the mean field is a differential inclusion.
Stochastic recursive inclusion in two timescales with an application to the Lagrangian dual problem
In this paper we present a framework to analyze the asymptotic behavior of two timescale stochastic approximation algorithms including those with set-valued mean fields.
