33 citations · 41 across the 7 of their papers we have counts for
7 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…
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…
Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application
Jing Tan, Ramin Khalili, Holger Karl +1
We formulate offloading of computational tasks from a dynamic group of mobile agents (e.g., cars) as decentralized decision making among autonomous agents. We design an interaction…
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games
Jing Tan, Ramin Khalili, Holger Karl
We propose a multi-agent distributed reinforcement learning algorithm that balances between potentially conflicting short-term reward and sparse, delayed long-term reward, and lear…
Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions
Jing Tan, Ramin Khalili, Holger Karl +1
We formulate computation offloading as a decentralized decision-making problem with autonomous agents. We design an interaction mechanism that incentivizes agents to align private…
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…