Q-Concept-Learning: Generalization with Concept Lattice Representation in Reinforcement Learning

  • Authors:

  • Marc Ricordeau

  • Affiliations:

  • -

  • Venue:
  • ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
  • Year:
  • 2003

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Abstract

One of the very interesting properties of Reinforcement Learning algorithms is that they allow learning without prior knowledge of the environment. However, when the agents use algorithms that enable a generalization of the learning, they are unable to explain their choices. Neural networks are good examples of this problem. After a reminder about the basis of Reinforcement Learning, the Lattice Concept will be introduced. Then, Q-Concept-Learning, a Reinforcement Learning algorithm that enables a generalization of the learning, the use of structured languages as well as an explanation of the agent's choices will be presented.