Consensus, cooperative learning, and flocking for multiagent predator avoidance

Young, Zachary; La, Hung Manh

doi:10.1177/1729881420960342

Cited by 17 publications

(13 citation statements)

References 56 publications

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“…Discovery learning is also beneficial for students so that discovery, learning, and engagement; add and maintain knowledge and technology; and involve students in their professional lives (Plagens, 2011). Discovery learning can also motivate students to improve their learning outcomes and learning achievement (Young & La, 2020). In addition, discovery learning is able to uncover and solve problems for students (Santrock, 2017).…”

Section: Introductionmentioning

confidence: 99%

The influence of types of collaborative learning models jigsaw vs discovery learning model and learning discipline on learning results

2022

pegegog

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This study aims to determine the effect of the jigsaw type collaborative learning model vs discovery learning model and learning discipline on learning outcomes, Message Design courses for students. This study used a quasi-experimental research design with a 2 x 2 factorial design. The data in testing this hypothesis used Descriptive Statistical Analysis and ANOVA (analysis of variance) based on a 2X2 or two-way factorial design. The null hypothesis was tested at a significance level of 5% (α = 0.05). Thus the jigsaw type collaborative learning model has advantages over the discovery learning model in achieving learning disciplines on learning outcomes message design course. The interaction between jigsaw type collaborative learning model vs discovery learning model and learning discipline on learning outcomes. Learning outcomes varied between groups of students who were taught using the collective learning model and groups of students who were taught using the exploratory model, different results were obtained. Learning outcomes varied between groups of students with high levels of learning discipline and groups of students with low levels of learning discipline. Therefore, educational institutions are expected to make policies to improve the quality of learning, especially in developing a more student-oriented learning model or better known as studentcentered learning. Especially in the message design course.

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Section: Introductionmentioning

confidence: 99%

The influence of types of collaborative learning models jigsaw vs discovery learning model and learning discipline on learning results

2022

pegegog

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“…consensus:refers to the problem setup where agents are allowed to communicate with their neighbors to reach an agreement. The information is shared locally-between neighboring agents-preserving scalability even as the number of agents increases [148,149].…”

Section: ) Multi-agent Reinforcement Learningmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹,

Saaybi²,

Rietbergen³

et al. 2021

Preprint

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<div>Deep Reinforcement Learning (DRL) has the potential to surpass human-level control in sequential decision-making problems. Evolution Strategies (ESs) have different characteristics than DRL, yet they are promoted as a scalable alternative. </div><div>To get insights into their strengths and weaknesses, in this paper, we put the two approaches side by side. After presenting the fundamental concepts and algorithms for each of the two approaches, they are compared from the perspectives of scalability, exploration, adaptation to dynamic environments, and multi-agent learning. Then, the paper discusses hybrid algorithms, combining aspects of both DRL and ESs, and how they attempt to capitalize on the benefits of both techniques. Lastly, both approaches are compared based on the set of applications they support, showing their potential for tackling real-world problems.</div><div>This paper aims to present an overview of how DRL and ESs can be used, either independently or in unison, to solve specific learning tasks. It is intended to guide researchers to select which method suits them best and provides a bird's eye view of the overall literature in the field. Further, we also provide application scenarios and open challenges. </div>

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“…The authors show that independent learning with and without function approximation proved to be unreliable in learning to flock together towards the same target. Similarly, the authors of [41], assume the focal agent is accurately aware of the location and velocities of the neighboring agents while solving a multi-objective task. Nonetheless, they were not able to obtain a collective behavior by learning a policy at the individual level as obtained by Durve et al Furthermore, Lopez-Incera et al [42] studied the collective behavior of artificial learning agents driven by reinforcement learning action-making process and with abstract sensing mechanism, that arises as they attempt to survive in foraging environments.…”

Section: Introductionmentioning

confidence: 99%

“…Several attempts to model CM using learning models are presented [48][49][50]. Durve et al [48] developed both Vicsek-inspired CM teacher-based reinforcement learning and concurrent learning.…”

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confidence: 99%

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Collective Evolution Learning Model for Vision-Based Collective Motion with Collision Avoidance

Krongauz

Lazebnik

2022

Preprint

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Collective motion takes many forms in nature; schools of fish, flocks of birds, and swarms of locusts to name a few. Commonly, during collective motion the individuals of the group avoid collisions. These collective motion and collision avoidance behaviors are based on input from the environment such as smell, air pressure, and vision, all of which are processed by the individual and defined action. In this work, a novel vision-based collective motion with collision avoidance model (i.e., VCMCA) that is simulating the collective evolution learning process is proposed. In this setting, a learning agent obtains a visual signal about its environment, and throughout trial-and-error over multiple attempts, the individual learns to perform a local collective motion with collision avoidance which emerges into a global collective motion with collision avoidance dynamics. The proposed algorithm was evaluated on the case of locusts’ swarms, showing the evolution of these behaviors in a swarm from the learning process of the individual in the swarm. Thus, this work proposes a biologically-inspired learning process to obtain multi-agents multi-objective dynamics.

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Consensus, cooperative learning, and flocking for multiagent predator avoidance

Cited by 17 publications

References 56 publications

The influence of types of collaborative learning models jigsaw vs discovery learning model and learning discipline on learning results

The influence of types of collaborative learning models jigsaw vs discovery learning model and learning discipline on learning results

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Collective Evolution Learning Model for Vision-Based Collective Motion with Collision Avoidance

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