With the wide application of deep learning, amount data required to train learning models is becoming increasingly larger, resulting in an increased training time and higher requirements for computing resources. To improve throughput a distributed system, task scheduling resource are required. This paper proposes combine ARIMA GRU predict future volume. In terms scheduling, multi-priority queue...

In distributed computing such as grid computing, online users submit their tasks anytime and anywhere to dynamic resources. Task arrival and execution processes are stochastic. How to adapt to the consequent uncertainties, as well as scheduling overhead and response time, are the main concern in dynamic scheduling. Based on the decision theory, scheduling is formulated as a Markov decision proc...

We propose a multiagent distributed actor-critic algorithm for multitask reinforcement learning (MRL), named Diff-DAC. The agents are connected, forming a (possibly sparse) network. Each agent is assigned a task and has access to data from this local task only. During the learning process, the agents are able to communicate some parameters to their neighbors. Since the agents incorporate their ...

In Vehicular Ad hoc Networks (VANETs), general purpose ad hoc routing protocols such as AODV cannot work efficiently due to the frequent changes in network topology caused by vehicle movement. This paper proposes a VANET routing protocol QLAODV (QLearning AODV) which suits unicast applications in high mobility scenarios. QLAODV is a distributed reinforcement learning routing protocol, which use...

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This paper describes the application and performance evaluation of a new algorithm for multiple objective optimization problems (MOOP) based on reinforcement learning. The new algorithm, called MDQL, considers a family of agents for each objective function involved in a MOOP. Each agent proposes a solution for its corresponding objective function. Agents leave traces while they construct soluti...

Reinforcement learning is a very general unsupervised learning mechanism. Due to its generality reinforcement learning does not scale very well for tasks that involve inferring subtasks. In particular when the subtasks are dynamically changing and the environment is adversarial. One of the most challenging reinforcement learning tasks so far has been the 3 to 2 keepaway task in the RoboCup simu...

This paper describes a novel hybrid reinforcement learning algorithm, Sarsa Learning Vector Quantization (SLVQ), that leaves the reinforcement part intact but employs a more effective representation of the policy function using a piecewise constant function based upon “policy prototypes.” The prototypes correspond to the pattern classes induced by the Voronoi tessellation generated by self-orga...

In recent years, the demand for new applications using various Internet of Things (IoT) devices has led to an increase in number connected wireless networks. However, owing limitation available frequency resources IoT devices, degradation communication quality caused by channel congestion is a practical problem developing technology. Many have hardware and software limitations that prevent cent...