Generating collective foraging behavior for robotic swarm using deep reinforcement learning

2020 ◽  
Vol 25 (4) ◽  
pp. 588-595
Author(s):  
Boyin Jin ◽  
Yupeng Liang ◽  
Ziyao Han ◽  
Kazuhiro Ohkura
Keyword(s):  
Reinforcement Learning ◽  
Foraging Behavior ◽  
Robotic Swarm ◽  
Collective Foraging

A hierarchical training method of generating collective foraging behavior for a robotic swarm

2021 ◽  
Author(s):  
Boyin Jin ◽  
Yupeng Liang ◽  
Ziyao Han ◽  
Motoaki Hiraga ◽  
Kazuhiro Ohkura
Keyword(s):  
Foraging Behavior ◽  
Training Method ◽  
Robotic Swarm ◽  
Collective Foraging

scholarly journals Learning a Swarm Foraging Behavior with Microscopic Fuzzy Controllers Using Deep Reinforcement Learning

2021 ◽  
Vol 11 (6) ◽  
pp. 2856
Author(s):  
Fidel Aznar ◽  
Mar Pujol ◽  
Ramón Rizo
Keyword(s):  
Reinforcement Learning ◽  
Obstacle Avoidance ◽  
Foraging Behavior ◽  
Target Position ◽  
Light Detection And Ranging ◽  
Complex Task ◽  
Training Phase ◽  
Learning Techniques ◽  
Robotic Swarm ◽  
Foraging Task

This article presents a macroscopic swarm foraging behavior obtained using deep reinforcement learning. The selected behavior is a complex task in which a group of simple agents must be directed towards an object to move it to a target position without the use of special gripping mechanisms, using only their own bodies. Our system has been designed to use and combine basic fuzzy behaviors to control obstacle avoidance and the low-level rendezvous processes needed for the foraging task. We use a realistically modeled swarm based on differential robots equipped with light detection and ranging (LiDAR) sensors. It is important to highlight that the obtained macroscopic behavior, in contrast to that of end-to-end systems, combines existing microscopic tasks, which allows us to apply these learning techniques even with the dimensionality and complexity of the problem in a realistic robotic swarm system. The presented behavior is capable of correctly developing the macroscopic foraging task in a robust and scalable way, even in situations that have not been seen in the training phase. An exhaustive analysis of the obtained behavior is carried out, where both the movement of the swarm while performing the task and the swarm scalability are analyzed.

scholarly journals A Multiscale Review of Behavioral Variation in Collective Foraging Behavior in Honey Bees

Insects ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 370 ◽  
Author(s):  
Natalie J. Lemanski ◽  
Chelsea N. Cook ◽  
Brian H. Smith ◽  
Noa Pinter-Wollman
Keyword(s):  
Individual Differences ◽  
Foraging Behavior ◽  
Collective Behavior ◽  
Individual Variation ◽  
Honey Bees ◽  
Multiple Scales ◽  
Model System ◽  
Bee Foraging ◽  
Collective Foraging ◽  
The Impact

The emergence of collective behavior from local interactions is a widespread phenomenon in social groups. Previous models of collective behavior have largely overlooked the impact of variation among individuals within the group on collective dynamics. Honey bees (Apis mellifera) provide an excellent model system for exploring the role of individual differences in collective behavior due to their high levels of individual variation and experimental tractability. In this review, we explore the causes and consequences of individual variation in behavior for honey bee foraging across multiple scales of organization. We summarize what is currently known about the genetic, developmental, and neurophysiological causes of individual differences in learning and memory among honey bees, as well as the consequences of this variation for collective foraging behavior and colony fitness. We conclude with suggesting promising future directions for exploration of the genetic and physiological underpinnings of individual differences in behavior in this model system.

Imitation as a Mechanism of Cultural Transmission

2010 ◽  
Vol 16 (1) ◽  
pp. 21-37 ◽  
Author(s):  
Chris Marriott ◽  
James Parker ◽  
Jörg Denzinger
Keyword(s):  
Control System ◽  
Reinforcement Learning ◽  
Foraging Behavior ◽  
Cultural Transmission ◽  
Learning Strategy ◽  
Over Time

We study the effects of an imitation mechanism on a population of animats capable of individual ontogenetic learning. An urge to imitate others augments a network-based reinforcement learning strategy used in the control system of the animats. We test populations of animats with imitation against populations without for their ability to find, and maintain over generations, successful foraging behavior in an environment containing three necessary resources: food, water, and shelter. We conclude that even simple imitation mechanisms are effective at increasing the frequency of success when measured over time and over populations of animats.

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