engleski

Reinforcement Learning as Adaptive Network Routing of Mobile Agents

In large distributed systems, like ad-hoc networks, centralized learning of routing or movement policies may be impractical. We need to employ learning algorithms that can learn independently, without the need for extensive coordination. A search for alternative methods of routing packets has resulted in reinforcement learning (RL) as a good approach to adaptive routing. RL methods are able to learn and adapt to a unknown and changing environment.

network routing; Q-learning; self-adaptation

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