MARbLE:Multi-Agent Reinforcement Learning at the Edge for Digital Agriculture

Boubin, Jayson and Burley, Codi and Han, Peida and Li, Bowen and Porter, Barry and Stewart, Christopher (2022) MARbLE:Multi-Agent Reinforcement Learning at the Edge for Digital Agriculture. In: Symposium on Edge Computing. IEEE. (In Press)

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Abstract

Digital agriculture, hailed as the fourth great agricultural revolution, employs software-driven autonomous agents for in-field crop management. Edge computing resources deployed near crop fields support autonomous agents with substantial computational needs for tasks such as AI inference. In large fields, using multiple autonomous agents, called swarms, can speed up crop management tasks if sufficient edge resources are provisioned. However, to use swarms today, farmers and software developers craft their own standalone solutions that are either simple and ineffective or complicated and hard-to-reproduce. We present MARbLE, a platform for developing and managing swarms. MARbLE provides an easy-to-use programming paradigm that helps users build swarm workloads using multi-agent reinforcement learning. Developers supply just two functions Map() and Eval(). The platform automatically compiles and deploys swarms and continuously updates the reinforcement learning models that govern their actions. Developers can experiment with multiple swarm and edge resource configurations both in simulation and with actual in-field runs. We studied real UAV swarms conducting digital agriculture missions. We observe that swarms demanded edge computing resources in bursts; the ratio of average to peak demand was 2.9X. MARbLE uses energy-saving load balancing policies to duty cycle machines during workload demand troughs, leveraging workload patterns to save edge energy. Using MARbLE, we found that four-agent swarms with load balancing techniques sped up missions by 2.1X and reduced edge energy usage by up to 2X compared to state of the art autonomous swarms.

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Contribution in Book/Report/Proceedings
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ID Code:
177698
Deposited By:
Deposited On:
20 Oct 2022 15:20
Refereed?:
Yes
Published?:
In Press
Last Modified:
21 Nov 2022 17:50