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.
URL: https://www.w3.org/TR/2014/REC-rdf11-concepts-20140225/ (visited on 05/08/2019)
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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.