206,249 research outputs found
Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients
Artificial Neural Networks (ANNs) are currently being used as function
approximators in many state-of-the-art Reinforcement Learning (RL) algorithms.
Spiking Neural Networks (SNNs) have been shown to drastically reduce the energy
consumption of ANNs by encoding information in sparse temporal binary spike
streams, hence emulating the communication mechanism of biological neurons. Due
to their low energy consumption, SNNs are considered to be important candidates
as co-processors to be implemented in mobile devices. In this work, the use of
SNNs as stochastic policies is explored under an energy-efficient
first-to-spike action rule, whereby the action taken by the RL agent is
determined by the occurrence of the first spike among the output neurons. A
policy gradient-based algorithm is derived considering a Generalized Linear
Model (GLM) for spiking neurons. Experimental results demonstrate the
capability of online trained SNNs as stochastic policies to gracefully trade
energy consumption, as measured by the number of spikes, and control
performance. Significant gains are shown as compared to the standard approach
of converting an offline trained ANN into an SNN.Comment: Submitted for conference publicatio
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A survey of intrusion detection techniques in Cloud
Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks
Pseudorehearsal in actor-critic agents with neural network function approximation
Catastrophic forgetting has a significant negative impact in reinforcement
learning. The purpose of this study is to investigate how pseudorehearsal can
change performance of an actor-critic agent with neural-network function
approximation. We tested agent in a pole balancing task and compared different
pseudorehearsal approaches. We have found that pseudorehearsal can assist
learning and decrease forgetting
Pseudorehearsal in actor-critic agents with neural network function approximation
Catastrophic forgetting has a significant negative impact in reinforcement
learning. The purpose of this study is to investigate how pseudorehearsal can
change performance of an actor-critic agent with neural-network function
approximation. We tested agent in a pole balancing task and compared different
pseudorehearsal approaches. We have found that pseudorehearsal can assist
learning and decrease forgetting
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