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Found 5 papers, 1 papers with code
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems
Distributed machine learning (ML) is a modern computation paradigm that divides its workload into independent tasks that can be simultaneously achieved by multiple machines (i. e., agents) for better scalability.
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
The practicality of existing works addressing this challenge is limited to only small-scale synchronous decision-making scenarios or a single agent planning its best response against a single adversary with fixed, procedurally characterized strategies.
Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models
Bayesian nonparametrics are a class of probabilistic models in which the model size is inferred from data.
SLAM with Objects using a Nonparametric Pose Graph
The \textit{data association} and \textit{simultaneous localization and mapping} (SLAM) problems are, individually, well-studied in the literature.
Batch-iFDD for Representation Expansion in Large MDPs
Matching pursuit (MP) methods are a promising class of feature construction algorithms for value function approximation.
