An overview on optimal flocking
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 …
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
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 …
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
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 …
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 …
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 …
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 …
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
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 …
Although the traditional empirical parameter method (TEPM) has the potential to deal with …
Distributed learning of cooperative robotic behaviors using particle swarm optimization
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 …
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 …
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 …
concerning personal services. This has led to the continuous development and research in …