high-level reinforcement learning in strategy games
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.
reinforcement learning video games virtual agents