Search Results for author:
Found 19 papers, 6 papers with code
FPGA-Based Neural Thrust Controller for UAVs
The advent of unmanned aerial vehicles (UAVs) has improved a variety of fields by providing a versatile, cost-effective and accessible platform for implementing state-of-the-art algorithms.
Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach
This hybrid graphex learning approach leverages that the system mainly consists of a highly connected core and a sparse periphery.
Collaborative Optimization of the Age of Information under Partial Observability
In this work, we devise a decentralized AoI-minimizing transmission policy for a number of sensor agents sharing capacity-limited, non-FIFO duplex channels that introduce random delays in communication with a common receiver.
Sparse Mean Field Load Balancing in Large Localized Queueing Systems
Empirically, the proposed methodology performs well on several realistic and scalable wireless network topologies as compared to a number of well-known load balancing heuristics and existing scalable multi-agent reinforcement learning methods.
Learning Discrete-Time Major-Minor Mean Field Games
We propose a novel discrete time version of major-minor MFGs (M3FGs), along with a learning algorithm based on fictitious play and partitioning the probability simplex.
Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior
However, multi-agent RL (MARL) remains a challenge in terms of decentralization, partial observability and scalability to many agents.
Multi-Agent Reinforcement Learning via Mean Field Control: Common Noise, Major Agents and Approximation Properties
In this work, we propose a novel discrete-time generalization of Markov decision processes and MFC to both many minor agents and potentially complex major agents -- major-minor mean field control (M3FC).
Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control
Thus, we combine collision avoidance and learning of mean-field control into a unified framework for tractably designing intelligent robotic swarm behavior.
Learning Sparse Graphon Mean Field Games
Although the field of multi-agent reinforcement learning (MARL) has made considerable progress in the last years, solving systems with a large number of agents remains a hard challenge.
Mean Field Games on Weighted and Directed Graphs via Colored Digraphons
Graphon mean field games (GMFGs) on the other hand provide a scalable and mathematically well-founded approach to learning problems that involve a large number of connected agents.
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning
The analysis and control of large-population systems is of great interest to diverse areas of research and engineering, ranging from epidemiology over robotic swarms to economics and finance.
Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systems
In this work, we consider a multi-agent load balancing system, with delayed information, consisting of many clients (load balancers) and many parallel queues.
RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search
More specifically, we consider the social bot detection problem as a user-centric subgraph embedding and classification task.
Hypergraphon Mean Field Games
We propose an approach to modelling large-scale multi-agent dynamical systems allowing interactions among more than just pairs of agents using the theory of mean field games and the notion of hypergraphons, which are obtained as limits of large hypergraphs.
Learning Graphon Mean Field Games and Approximate Nash Equilibria
Recent advances at the intersection of dense large graph limits and mean field games have begun to enable the scalable analysis of a broad class of dynamical sequential games with large numbers of agents.
Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning
Collision avoidance algorithms are of central interest to many drone applications.
Discrete-Time Mean Field Control with Environment States
Multi-agent reinforcement learning methods have shown remarkable potential in solving complex multi-agent problems but mostly lack theoretical guarantees.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning
We show that all discrete-time finite MFGs with non-constant fixed point operators fail to be contractive as typically assumed in existing MFG literature, barring convergence via fixed point iteration.
Color Image Demosaicking Using a 3-Stage Convolutional Neural Network Structure
Color demosaicking (CDM) is a critical first step for the acquisition of high-quality RGB images with single chip cameras.
