Dynamics and risk sharing in groups of selfish individuals

Monter, Samuel; Heuthe, Veit-Lorenz; Panizon, Emanuele; Bechinger, Clemens

doi:10.1016/j.jtbi.2023.111433

Cited by 2 publications

(10 citation statements)

References 29 publications

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“…Homogeneity has been widely adopted in the modeling of swarm systems. For example, works such as those in [3,5,[7][8][9][10][11][12] propose singleton control laws that dominate all the agents in a swarm, while those in [14][15][16][17] employ parameter-sharing techniques for the agents' neural networks. As a result, the homogeneity leads to two features of the proposed training regime: parameter-sharing of actor-critic networks and replay buffer sharing among conspecifics, which are described as follows.…”

Section: Homogeneitymentioning

confidence: 99%

“…That is, if an agent loses its neighbor, it receives a penalty of −1. The recent work in [17] assumes prey receive larger reward as the area of domain of danger decreases [23], thus explicitly encouraging prey agents to move closer to each other. The authors in [13] implicitly assume the attack efficiency is inversely proportional to the number of prey visible to the predator due to confusion.…”

Section: Introductionmentioning

confidence: 99%

“…Although these models can phenomenally give rise to swarming behaviors including complex ring-shaped and swirling patterns [6,11,12], the interaction rules are mostly heuristic and static, failing to capture the adaptation property of the biological world. To address this issue, recent studies have utilized evolution-based methods including neuroevolution [13] and reinforcement learning (RL) methods [14][15][16][17], because they offer the potential for adaptable strategies, analogous to the way biological organisms evolve [18][19][20]. In addition, multi-agent RL allows to model the interactions among individual agents in a swarm, and to optimize their collective behaviors [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…To address this issue, recent studies have utilized evolution-based methods including neuroevolution [13] and reinforcement learning (RL) methods [14][15][16][17], because they offer the potential for adaptable strategies, analogous to the way biological organisms evolve [18][19][20]. In addition, multi-agent RL allows to model the interactions among individual agents in a swarm, and to optimize their collective behaviors [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…However, the interaction mechanism or reward functions in evolution-based methods [13][14][15][16][17] are task-specific [21,22] meaning they are handcrafted by designers to intentionally fit for the characteristics implicitly or explicitly associated to swarming behaviors, which we refer to as swarm-dependent in this study. For example, the reward function in [16] penalizes losing neighbors.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Predator–prey survival pressure is sufficient to evolve swarming behaviors

Li,

Zhao

2023

New J. Phys.

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The comprehension of how local interactions arise in global collective behavior is of utmost importance in both biological and physical research. Traditional agent-based models often rely on static rules that fail to capture the dynamic strategies of the biological world. Reinforcement learning has been proposed as a solution, but most previous methods adopt handcrafted reward functions that implicitly or explicitly encourage the emergence of swarming behaviors. In this study, we propose a minimal predator-prey coevolution framework based on mixed cooperative-competitive multiagent reinforcement learning, and adopt a reward function that is solely based on the fundamental survival pressure, that is, prey receive a reward of −1 if caught by predators while predators receive a reward of +1. Surprisingly, our analysis of this approach reveals an unexpectedly rich diversity of emergent behaviors for both prey and predators, including flocking and swirling behaviors for prey, as well as dispersion tactics, confusion, and marginal predation phenomena for predators. Overall, our study provides novel insights into the collective behavior of organisms and highlights the potential applications in swarm robotics.

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Section: Homogeneitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predator–prey survival pressure is sufficient to evolve swarming behaviors

Li,

Zhao

2023

New J. Phys.

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Collective foraging of active particles trained by reinforcement learning

Löffler,

Panizon,

Bechinger

2023

Sci Rep

Self Cite

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Collective self-organization of animal groups is a recurring phenomenon in nature which has attracted a lot of attention in natural and social sciences. To understand how collective motion can be achieved without the presence of an external control, social interactions have been considered which regulate the motion and orientation of neighbors relative to each other. Here, we want to understand the motivation and possible reasons behind the emergence of such interaction rules using an experimental model system of light-responsive active colloidal particles (APs). Via reinforcement learning (RL), the motion of particles is optimized regarding their foraging behavior in presence of randomly appearing food sources. Although RL maximizes the rewards of single APs, we observe the emergence of collective behaviors within the particle group. The advantage of such collective strategy in context of foraging is to compensate lack of local information which strongly increases the robustness of the resulting policy. Our results demonstrate that collective behavior may not only result on the optimization of behaviors on the group level but may also arise from maximizing the benefit of individuals. Apart from a better understanding of collective behaviors in natural systems, these results may also be useful in context of the design of autonomous robotic systems.

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Dynamics and risk sharing in groups of selfish individuals

Cited by 2 publications

References 29 publications

Predator–prey survival pressure is sufficient to evolve swarming behaviors

Predator–prey survival pressure is sufficient to evolve swarming behaviors

Collective foraging of active particles trained by reinforcement learning

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