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Found 13 papers, 6 papers with code
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning
We test our algorithm in an ITS environment with edge cloud computing.
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.
Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application
We formulate offloading of computational tasks from a dynamic group of mobile agents (e. g., cars) as decentralized decision making among autonomous agents.
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games
We propose a multi-agent distributed reinforcement learning algorithm that balances between potentially conflicting short-term reward and sparse, delayed long-term reward, and learns with partial information in a dynamic environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions
We formulate computation offloading as a decentralized decision-making problem with autonomous agents.
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).
Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding
Using Service Function Chaining (SFC) in wireless networks became popular in many domains like networking and multimedia.
Distributed Online Service Coordination Using Deep Reinforcement Learning
To address these issues, we propose a distributed self-learning service coordination approach using DRL.
Self-Driving Network and Service Coordination Using Deep Reinforcement Learning
While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e. g., a priori knowledge).
Machine Learning for Dynamic Resource Allocation in Network Function Virtualization
To solve this problem, we train machine learning models on real VNF data, containing measurements of performance and resource requirements.
The Softwarised Network Data Zoo
To this end, we introduce the "softwarised network data zoo" (SNDZoo), an open collection of software networking data sets aiming to streamline and ease machine learning research in the software networking domain.
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.
