Online Adaptable Learning Rates for the Game Connect-4

—Learning board games by self-play has a long tradition in computational intelligence for games. Based on Tesauro’s seminal success with TD-Gammon in 1994, many successful agents use temporal difference learning today. But in order to be successful with temporal difference learning on game tasks, often a careful selection of features and a large number of training games is necessary. Even for board games of moderate complexity like Connect-4, we found in previous work that a very rich initial feature set and several millions of game plays are required. In this work we investigate different approaches of online-adaptable learning rates like Incremental Delta Bar Delta (IDBD) or Temporal Coherence Learning (TCL) whether they have the potential to speed up learning for such a complex task. We propose a new variant of TCL with geometric step size changes. We compare those algorithms with several other state-of-the-art learning rate adaptation algorithms and perform a case study on the ...

• Type: Journal
• Year: 2016
• Where: TCIAIG
• Authors: Samineh Bagheri, Markus Thill, Patrick Koch, Wolfgang Konen
• Keywords: Software Engineering, TCIAIG 2016