{'fraction_non_alphanumeric': 0.026155187445510025, 'fraction_numerical': 0.0, 'mean_word_length': 4.8274111675126905, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Efficient Learning in Games

We consider the problem of learning strategy selection in games. The theoretical solution to this problem is a distribution over strategies that responds to a Nash equilibrium of the game. When the payoff function of the game is not known to the participants, such a distribution must be approximated directly through repeated play. Full knowledge of the payoff function, on the other hand, restricts agents to be strictly rational. In this classical approach, agents are bound to a Nash equilibrium, even when a globally better solution is obtainable. In this paper, we present an algorithm that allows agents to capitalize on their very lack of information about the payoff structure. The principle we propose is that agents resort to the manipulation of their own payoffs, during the course of learning, to find a ``game'' that gives them a higher payoff than when no manipulation occurs. In essence, the payoffs are considered an extension of the strategy set. At all times, agents remain rational vis-a-vis the information available. In self-play, the algorithm affords a globally efficient payoff (if it exists).