A Parameter-Free Gradient Bayesian Two-Action Learning Automaton Scheme

Ge, Hao; Yan, Yan; Li, Jianhua; Guo, Ying; Li, Shenghong

doi:10.1007/978-3-662-49831-6_100

Cited by 8 publications

(11 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An important feature of learning systems is the ability to improve their efficiency over time. In mathematical terms, it can be stated that the purpose of a learning system is to optimize a task that is not well known [12,13]. Therefore, an approach to this problem is to reduce the goals of the learning system to an optimization problem, which is defined on a set of parameters and aims to find a set of optimal parameters.…”

Section: Definitionmentioning

confidence: 99%

Eer-Al: An Energy Efficient Routing Protocol Based on Automated Learning Method

Kiani¹

2018

IJCT

View full text Add to dashboard Cite

The issue of energy in a wireless sensor network is one of the most important challenges for these networks. This issue is also being considered today in the new IoT topic. This paper studies the ability of the learning automata model to solve the problem in the sensor networks. Because they have capabilities such as low computational load, ability to use in distributed environments, and inaccurate information, require the least feedback from the environment, etc. One of the solutions to energy optimization is to provide routing protocols. In the routing area, a routing protocol based on learning automata has been proposed in which the network lifetime criterion is considered. The simulation results and the comparison of the proposed protocol with other protocols indicate that this protocol has better performance in the energy conversation and network lifetime.

show abstract

Section: Definitionmentioning

confidence: 99%

Eer-Al: An Energy Efficient Routing Protocol Based on Automated Learning Method

Kiani¹

2018

IJCT

View full text Add to dashboard Cite

show abstract

“…So extra efforts are necessary to realize the trade-off between the accuracy and the convergence rate in a specific environment. Most traditional schemes are parameter-sensitive, and the cost of parameter tuning can be extremely expensive [28]. In practical applications, especially where interacting with the environment could be expensive, the enormous cost for parameter tuning is intimidating.…”

Section: Introductionmentioning

confidence: 99%

“…Several parameter-free schemes have been proposed in recent years to address the problem of parameter tuning. The parameter-free concept, which is first presented in [29], indicates that a set of parameters can be universally applied to all environments without further tuning. The most representative parameter-free schemes are the parameter-free LA (PFLA) [28] and loss function-based LA (LFPLA) [30].…”

Section: Introductionmentioning

confidence: 99%

“…The parameter-free concept, which is first presented in [29], indicates that a set of parameters can be universally applied to all environments without further tuning. The most representative parameter-free schemes are the parameter-free LA (PFLA) [28] and loss function-based LA (LFPLA) [30]. However, both schemes are supported by time-consuming and computing resources-consuming Monte-Carlo simulations [31], preventing the schemes from being applied to timesensitive and resources-restricted tasks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Framework for Learning Automata: A Statistical Hypothesis Testing Approach

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Learning automaton (LA), a powerful tool in reinforcement learning, is of crucial importance for its adaptivity in the stochastic environment and its applicability in various engineering fields. In particular, the LA adaptively explores the optimal action that maximizes the reward among all possible choices by interacting with the environment. However, the traditional frameworks for LA have several limitations in practical applications, e.g., the cost of parameter tuning and predicaments in massive-action environments, preventing them from being applied to time-sensitive and resources-restricted tasks. In this paper, we propose a novel LA framework based on the statistical hypothesis testing, where the actions are compared by statistical hypothesis iteratively and the suboptimal ones are dismissed, and the estimated optimal action is attained. Apart from the proposal, the theoretical analyses for the framework are given to reveal its-optimality. The proposed framework also features efficiency in massive-action environments and the parameter-free property. The comprehensive simulations are conducted in both benchmark and massiveaction environments to demonstrate the superiority of the proposed framework over the ordinary schemes. INDEX TERMS Learning automata, reinforcement learning, statistical inference, parameter-free.

show abstract

“…The most prominent features of learning-based systems are that they improve themselves over time. In mathematical terms, it can be stated that the purpose of a learning system is to optimize a task that is not well known [3,4]. Therefore, an approach to this problem is to reduce the goals of the learning system to an optimization problem, which is defined on a set of parameters and aims to find a set of optimal (appropriate) parameters.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Automated Learning Methods based on Linear Learning Algorithms

Kiani

2018

IJMLNCE

View full text Add to dashboard Cite

n recent years, the learning methods are converted to one of the new research area. These researches are divided into two general categories. The first category recognizes the principles of learning the living entities and its stages. The second is learning based methodology to any machines that the proposed method of this paper is based on it. Learning is defined as changes made in the performance of a system based on experiences. An important feature of learning systems is the ability to improve their efficiency over time. In mathematical terms, it can be stated that the purpose of a learning system is to optimize a task that is not well-known. Therefore, an approach to this problem is to reduce the goals of the learning system to an optimization problem. So, it is defined on a set of parameters and its purpose is to find the optimal set of parameters. In many of the issues raised, there is no knowledge of the correct answers to the problem in supervised learning based methods especially. For this reason, the use of a learning method called reinforcement learning has been considered. The main advantage of this technique over other learning methods is the need for no information from the environment (except amplification signal). The other learning methods as supervised or unsupervised are not appropriate to these problems. In this method, each agent decides the next its actions based on current k-actions instead of one action. In this paper is proposed a new approach based on the reinforcement learning technique that has three versions in order to implementation in different areas. It behaviors based on reward and penalty model. The effectiveness of these interactions with the environment is evaluated by the maximum and minimum of the number of rewards and penalties that are taken from the environment. The three versions are simple, sequential and unstructured linear learning methods so they evaluated in different possibilities to get the appropriate responses. Depending on the needs of any system, they can be used. The mode of convergence of actions in the proposed automaton (machine) in six different scenarios is examined.

show abstract

A Parameter-Free Gradient Bayesian Two-Action Learning Automaton Scheme

Cited by 8 publications

References 16 publications

Eer-Al: An Energy Efficient Routing Protocol Based on Automated Learning Method

Eer-Al: An Energy Efficient Routing Protocol Based on Automated Learning Method

A Novel Framework for Learning Automata: A Statistical Hypothesis Testing Approach

Improvement of Automated Learning Methods based on Linear Learning Algorithms

Contact Info

Product

Resources

About