A multi-expert FreeCiv agent that used reinforcement learning to pick among conflicting human rules beat every single-expert rule set and won in 287 average turns versus 291 for the best expert.
Aha, Matthew Molineaux, and Marc Ponsen
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Playing a Strategy Game with Knowledge-Based Reinforcement Learning
A multi-expert FreeCiv agent that used reinforcement learning to pick among conflicting human rules beat every single-expert rule set and won in 287 average turns versus 291 for the best expert.