Reinforcement Learning with Application to Adaptive Network Routing
نویسنده
چکیده
Reinforcement learning (RL) is learning from interaction with an environment, from the consequences of action, rather than from explicit teaching. It is the learning performed by an agent by trial and error interactions with a dynamic environment. This paper discusses Reinforcement learning along with application to static routing.
منابع مشابه
Reinforcement Learning for Adaptive Routing
Reinforcement learning means learning a policy—a mapping of observations into actions— based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. We present an application of gradient ascent algorithm for reinforcement learning to a complex domain of packet routing in network communica...
متن کاملConndence Based Dual Reinforcement Q-routing: an Adaptive Online Network Routing Algorithm
This paper describes and evaluates the Conndence-based Dual Reinforcement Q-Routing algorithm (CDRQ-Routing) for adap-tive packet routing in communication networks. CDRQ-Routing is based on an application of the Q-learning framework to network routing, as rst proposed by Littman and Boyan (1993). The main contribution of CDRQ-routing is an increased quantity and an improved quality of explorati...
متن کاملAn Adaptive LEACH-based Clustering Algorithm for Wireless Sensor Networks
LEACH is the most popular clastering algorithm in Wireless Sensor Networks (WSNs). However, it has two main drawbacks, including random selection of cluster heads, and direct communication of cluster heads with the sink. This paper aims to introduce a new centralized cluster-based routing protocol named LEACH-AEC (LEACH with Adaptive Energy Consumption), which guarantees to generate balanced cl...
متن کاملCS 229 Final Report: Location Based Adaptive Routing Protocol(LBAR) using Reinforcement Learning
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-routin...
متن کاملPredictive Q-Routing: A Memory-based Reinforcement Learning Approach to Adaptive Traffic Control
In this paper, we propose a memory-based Q-Iearning algorithm called predictive Q-routing (PQ-routing) for adaptive traffic control. We attempt to address two problems encountered in Q-routing (Boyan & Littman, 1994), namely, the inability to fine-tune routing policies under low network load and the inability to learn new optimal policies under decreasing load conditions. Unlike other memory-ba...
متن کامل