Parallel Reinforcement Learning: A Framework and Case Study

Teng Liu

doi:10.1109/JAS.2018.7511144

Parallel Reinforcement Learning: A Framework and Case Study

2018-07-01

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AI

DOI：10.1109/JAS.2018.7511144

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Abstract

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In this paper, a new machine learning framework is developed for complex system control, called parallel reinforcement learning. To overcome data deficiency of current data-driven algorithms, a parallel system is built to improve complex learning system by self-guidance. Based on the Markov chain (MC) theory, we combine the transfer learning, predictive learning, deep learning and reinforcement learning to tackle the data and action processes and to express the knowledge. Parallel reinforcement learning framework is formulated and several case studies for real-world problems are finally introduced.

Keywords:

Deep learning

machine learning

parallel reinforcement learning

parallel system

predictive learning

transfer learning

Parallel Reinforcement Learning: A Framework and Case Study

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