Factored temporal difference learning in the new ties environment

Authors

  • Viktor Gyenes
  • Ákos Bontovics
  • András Lőrincz

Abstract

Although reinforcement learning is a popular method for training an agent for decision making based on rewards, well studied tabular methods are not applicable for large, realistic problems. In this paper, we experiment with a factored version of temporal difference learning, which boils down to a linear function approximation scheme utilising natural features coming from the structure of the task. We conducted experiments in the New Ties environment, which is a novel platform for multi-agent simulations. We show that learning utilising a factored representation is effective even in large state spaces, furthermore it outperforms tabular methods even in smaller problems both in learning speed and stability, because of its generalisation capabilities.

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Published

2008-01-01

How to Cite

Gyenes, V., Bontovics, Ákos, & Lőrincz, A. (2008). Factored temporal difference learning in the new ties environment. Acta Cybernetica, 18(4), 651–668. Retrieved from https://cyber.bibl.u-szeged.hu/index.php/actcybern/article/view/3743

Issue

Section

Regular articles