CMAC models learn to play soccer

Traditional reinforcement learning methods require a function approximator (FA) for learning value functions in large or continuous state spaces. We describe a novel combination of CMAC-based FAs and adaptive world models (WMs) estimating transition probabilities and rewards. Simple variants are tested in multiagent soccer environments where they outperform the evolutionary method PIPE which performed best in previous comparisons.