State Space Dimensionality Reduction in Markov Decision Processes

Abstract

Learning of autonomous agents during their interaction with the environment is one of the frequent tasks in machine learning. However, the efficiency of the learning process drops dramatically with the complexity of the problem domain.

In this paper, we present a survey of existing methods for reduction of the dimensionality of the state space.

Keywords

• State
• Space
• Dimensionality
• Reduction
• Markov
• Decision
• Processes