3 citations · 6 across the 8 of their papers we have counts for
8 papers
Distributed Stochastic Approximation Algorithms and Heavy-Tailed Age of Information
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
Algorithms in multi-agent systems such as federated learning, mobile robotic swarming, and consensus control can be designed and analyzed as distributed stochastic approximation al…
C-STEP: Continuous Space-Time Empowerment for Physics-informed Safe Reinforcement Learning of Mobile Agents
Guihlerme Daubt, Adrian Redder
Safe navigation in complex environments remains a central challenge for reinforcement learning (RL) in robotics. This paper introduces Continuous Space-Time Empowerment for Physics…
Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
We generalize the Borkar-Meyn stability Theorem (BMT) to distributed stochastic approximations (SAs) with information delays that possess an arbitrary moment bound. To model the de…
Distributed gradient-based optimization in the presence of dependent aperiodic communication
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
Iterative distributed optimization algorithms involve multiple agents that communicate with each other, over time, in order to minimize/maximize a global objective. In the presence…
3DPG: Distributed Deep Deterministic Policy Gradient Algorithms for Networked Multi-Agent Systems
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
We present Distributed Deep Deterministic Policy Gradient (3DPG), a multi-agent actor-critic (MAAC) algorithm for Markov games. Unlike previous MAAC algorithms, 3DPG is fully distr…
Practical sufficient conditions for convergence of distributed optimisation algorithms over communication networks with interference
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
Information exchange over networks can be affected by various forms of delay. This causes challenges for using the network by a multi-agent system to solve a distributed optimisati…