High-level reinforcement learning in strategy games
By Chris Amato, Guy Shani. 2010
65 citations recorded by the source corpus.
Video games provide a rich testbed for artificial intelligence methods. In particular, creating automated opponents that perform well in strategy games is a difficult task. For instance, human players rapidly discover and exploit the weaknesses of hard coded strategies. To build better strategies, we suggest a reinforcement learning approach for learning a policy that switches between high-level strategies. These strategies are chosen based on different game situations and a fixed opponent strategy. Our learning agents are able to rapidly adapt to fixed opponents and improve deficiencies in the hard coded strategies, as the results demonstrate.
Subfields: Reinforcement Learning
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In Reinforcement Learning
- Karthik Narasimhan
- Regina Barzilay
- Tejas D. Kulkarni
- Matthew J. Hausknecht
- Prithviraj Ammanabrolu
- Jacob Andreas
- Sergey Levine
- Jason Aaron Edward Weston
Citations in the corpus
- Language Understanding for Text-based Games using Deep Reinforcement Learning (2015, methodology): “Q-Learning Reinforcement Learning is a commonly used framework for learning control policies in game environments (Silver et al., 2007; Amato and Shani, 2010; Branavan et al., 2011b; Szita, 2012).”