Evolving Collective Cognition of Robotic Swarms in the Foraging Task with Poison

Hiraga, Motoaki; Wei, Yufei; Ohkura, Kazuhiro

doi:10.1109/cec.2019.8790103

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…A copy of the controller is implemented on N different robots, before being evaluated and assessed depending on the swarm's performance. Another similar approach was proposed in [164] for building a swarm capable of cooperatively transporting food to a nest and collectively distinguishing between foods and poisons. Hiraga et al [164] developed a controller for a robotic swarm using CMA-ES, aiming to automatically generate the behavior of the robots.…”

Section: Learning In Multiagent Settingsmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid

Saaybi

François-Lavet

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist. To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects, such as scalability, exploration, adaptation to dynamic environments, and multiagent learning. Current research challenges are also discussed, including sample efficiency, exploration versus exploitation, dealing with sparse rewards, and learning to plan. Then, the benefits of hybrid algorithms that combine DRL and ESs are highlighted.

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Section: Learning In Multiagent Settingsmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid

Saaybi

François-Lavet

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

show abstract

“…A copy of the controller is implemented on N different robots, before being evaluated and assessed depending on the swarm's performance. Another similar approach was proposed in [162] for building a swarm capable of cooperatively transporting food to a nest and collectively distinguishing between foods and poisons. Hiraga et al [162] developed a controller for a robotic swarm using CMA-ES, aiming to automatically generate the behavior of the robots.…”

Section: ) Multi-agent Evolution Strategiesmentioning

confidence: 99%

“…Another similar approach was proposed in [162] for building a swarm capable of cooperatively transporting food to a nest and collectively distinguishing between foods and poisons. Hiraga et al [162] developed a controller for a robotic swarm using CMA-ES, aiming to automatically generate the behavior of the robots. The performed experiment covered a swarm of robots with each having eight distance sensors, an omnidirectional camera, an artificial neural network controller (three-layered neural network), and two motors (left and right).…”

Section: ) Multi-agent Evolution Strategiesmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹

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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“…A copy of the controller is implemented on N different robots, before being evaluated and assessed depending on the swarm's performance. Another similar approach was proposed in [154] for building a swarm capable of cooperatively transporting food to a nest and collectively distinguishing between foods and poisons. Hiraga et al [154] developed a controller for a robotic swarm using CMA-ES, aiming to automatically generate the behavior of the robots.…”

Section: ) Multi-agent Reinforcement Learningmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹,

Saaybi²,

Rietbergen³

et al. 2021

Preprint

View full text Add to dashboard Cite

<div>Deep Reinforcement Learning (DRL) and Evolution Strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist.</div><div>To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. </div><div>After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects such as scalability, exploration, adaptation to dynamic environments, and multi-agent learning. </div><div>Then, the benefits of hybrid algorithms that combine concepts from DRL and ESs are highlighted. </div><div>Finally, to have an indication about how they compare in real-world applications, a survey of the literature for the set of applications they support is provided.</div>

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Evolving Collective Cognition of Robotic Swarms in the Foraging Task with Poison

Cited by 4 publications

References 15 publications

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

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