A novel algorithm for handling reducer side data skew in MapReduce based on a learning automata game

Irandoost, Mohammad Amin; Rahmani, Amir Masoud; Setayeshi, Saeed

doi:10.1016/j.ins.2018.11.007

Cited by 14 publications

(1 citation statement)

References 23 publications

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“…Compared with other algorithms, CARLA can use the probability density function to select behavior in continuous space with a stochastic or unknown system model. The system learns interactively with the environment in a trial-and-error manner, and gets better behavior strategies by strengthening signals, increasing the probability of the action by strategy iteration, and finally obtains the optimal parameters online [27][28][29][30].…”

Section: Continuous Action Reinforcement Learning Automata (Carla)mentioning

confidence: 99%

Multivariable System Identification Method Based on Continuous Action Reinforcement Learning Automata

Meiying

Jin

2019

Processes

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In this work, a closed-loop identification method based on a reinforcement learning algorithm is proposed for multiple-input multiple-output (MIMO) systems. This method could be an attractive alternative solution to the problem that the current frequency-domain identification algorithms are usually dependent on the attenuation factor. With this method, after continuously interacting with the environment, the optimal attenuation factor can be identified by the continuous action reinforcement learning automata (CARLA), and then the corresponding parameters could be estimated in the end. Moreover, the proposed method could be applied to time-varying systems online due to its online learning ability. The simulation results suggest that the presented approach can meet the requirement of identification accuracy in both square and non-square systems.

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Section: Continuous Action Reinforcement Learning Automata (Carla)mentioning

confidence: 99%