The apprentice modeling through reinforcement with a temporal analysis using the Q-learning algorithm

Guelpeli, Marcus Vinicius Carvalho; Oliveira, Bruno Santos de; Pinto, Marcia Aurelia; Santos, Ruana Carpanzano dos

doi:10.1109/csae.2012.6272601

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“…Meanwhile, some literature has analysed the training process for RL based on the parameters α, c, and some other parameters which are also important for the convergence rate [8]. e influence to the convergence in RL from the major parameters, algorithmic complexity, the reward designing, and the training data is analysed in [9].…”

Section: Introductionmentioning

confidence: 99%

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Exploration Entropy for Reinforcement Learning

Xin

Qin

et al. 2020

Mathematical Problems in Engineering

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The training process analysis and termination condition of the training process of a Reinforcement Learning (RL) system have always been the key issues to train an RL agent. In this paper, a new approach based on State Entropy and Exploration Entropy is proposed to analyse the training process. The concept of State Entropy is used to denote the uncertainty for an RL agent to select the action at every state that the agent will traverse, while the Exploration Entropy denotes the action selection uncertainty of the whole system. Actually, the action selection uncertainty of a certain state or the whole system reflects the degree of exploration and the stage of the learning process for an agent. The Exploration Entropy is a new criterion to analyse and manage the training process of RL. The theoretical analysis and experiment results illustrate that the curve of Exploration Entropy contains more information than the existing analytical methods.

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Section: Introductionmentioning

confidence: 99%

“…It has been successfully applied in many fields such as information processing, artificial intelligence, and statistics [12]. In the field of MDPs [8], entropy has been used to optimize the decision results.…”

Section: Introductionmentioning

confidence: 99%

Exploration Entropy for Reinforcement Learning

Xin

Qin

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

The apprentice modeling through reinforcement with a temporal analysis using the Q-learning algorithm

Cited by 1 publication

References 1 publication

Exploration Entropy for Reinforcement Learning

Exploration Entropy for Reinforcement Learning

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