CS 229 Final Report: Location Based Adaptive Routing Protocol(LBAR) using Reinforcement Learning
نویسندگان
چکیده
In this paper we present an algorithm for a location based adaptive routing protocol that uses both geographic routing and reinforcement learning to maximize throughput in our mobile vehicle network. We use reinforcement learning to determine the correct direction to forward a packet and then use geographic routing to forward a packet toward the network sink. We use an extension of the q-routing algorithm, originally proposed by Boyan, et.al [1], except that we apply it on a location based routing protocol that benefits over a link based routing protocol in scenarios where the network is highly mobile.
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