Abstract
We report on a series of computational experiments with artificial agents learning in the context of games. Two kinds of learning are investigated: (1) a simple form of associative learning, called Q-learning, which occurs in state space, and (2) a simple form of learning, which we introduce here, that occurs in policy space. We compare the two methods on a number of repeated 2×2 games. We conclude that learning in policy space is an effective and promising method for learning in games.
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