Technical Note: \cal Q-Learning
Machine Learning
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Pricing in Agent Economies Using Multi-Agent Q-Learning
Autonomous Agents and Multi-Agent Systems
Human-Level AI's Killer Application: Interactive Computer Games
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Generalized model learning for reinforcement learning in factored domains
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
UCT for tactical assault planning in real-time strategy games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Non-linear Monte-Carlo search in civilization II
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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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.