An overview on optimal flocking

LE Beaver, AA Malikopoulos - Annual Reviews in Control, 2021 - Elsevier
The decentralized aggregate motion of many individual robots is known as robotic flocking.
The study of robotic flocking has received considerable attention in the past twenty years. As …

Oracle-guided deep reinforcement learning for large-scale multi-UAVs flocking and navigation

W Wang, L Wang, J Wu, X Tao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The flocking and navigation control of large-scale Unmanned Aerial Vehicle (UAV) swarms
have received a lot of research interest due to the wide applications of UAVs in many fields …

Reinforcement learning agents acquire flocking and symbiotic behaviour in simulated ecosystems

P Sunehag, G Lever, S Liu, J Merel, N Heess… - Artificial life …, 2019 - direct.mit.edu
In nature, group behaviours such as flocking as well as cross-species symbiotic partnerships
are observed in vastly different forms and circumstances. We hypothesize that such …

A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learning

Q Fu, T Qiu, J Yi, Z Pu, X Ai… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
State-of-the-art (SOTA) multiagent reinforcement algorithms distinguish themselves in many
ways from their single-agent equivalences. However, most of them still totally inherit the …

Reinforcement learning: Exploration–exploitation dilemma in multi-agent foraging task

M Yogeswaran, SG Ponnambalam - Opsearch, 2012 - Springer
The exploration–exploitation dilemma has been an unresolved issue within the framework of
multi-agent reinforcement learning. The agents have to explore in order to improve the state …

Emergent computation of complex systems: a comprehensive review

R **ao, Y Zhang, Z Huang - International Journal of Bio …, 2015 - inderscienceonline.com
Emergent computation is proposed in the study of self-organisation, collective and
cooperative behaviour and it has become an important approach to complex system …

An Algorithm of Reinforcement Learning for Maneuvering Parameter Self‐Tuning Applying in Satellite Cluster

X Wang, P Shi, C Wen, Y Zhao - Mathematical Problems in …, 2020 - Wiley Online Library
Satellite cluster is a type of artificial cluster, which is attracting wide attention at present.
Although the traditional empirical parameter method (TEPM) has the potential to deal with …

Distributed learning of cooperative robotic behaviors using particle swarm optimization

E Di Mario, I Navarro, A Martinoli - Experimental Robotics: The 14th …, 2016 - Springer
In this paper we study the automatic synthesis of robotic controllers for the coordinated
movement of multiple mobile robots. The algorithm used to learn the controllers is a noise …

Solving the diffusion of responsibility problem in multiagent reinforcement learning with a policy resonance approach

Q Fu, T Qiu, J Yi, Z Pu, X Ai, W Yuan - arxiv preprint arxiv:2208.07753, 2022 - arxiv.org
SOTA multiagent reinforcement algorithms distinguish themselves in many ways from their
single-agent equivalences, except that they still totally inherit the single-agent exploration …

Review of flocking organization strategies for robot swarms

FH Martínez - Tekhnê, 2021 - revistas.udistrital.edu.co
Robotics promises great benefits for human beings, both at the industrial level and
concerning personal services. This has led to the continuous development and research in …