Decentralized reinforcement learning collectives advancing autonomous automation strategies for dynamic, scalable and secure operations under adversarial environmental uncertainties

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Decentralized reinforcement learning collectives enhance autonomous automation in hostile conditions, improving system resilience.
These collectives utilize cryptographic coordination and distributed consensus mechanisms, ensuring secure interactions.
Agents share policy insights autonomously while maintaining independence, promoting scalability across large networks.
Case illustrations show applications in industrial control, smart grids, and logistics, emphasizing operational fairness.

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Decentralized RL enhances adaptability and resilience in adversarial environments, suggesting improved decision-making.

Soetan et al. (2021) studied this question.

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