OpenAI has demonstrated that competitive self-play can enable simulated AI agents to develop complex physical skills without explicit programming. By pitting agents against increasingly skilled versions of themselves in simple games, OpenAI observed the emergence of behaviors like tackling, faking, and diving. This method also showed that agents trained via self-play can transfer learned skills to novel situations, outperforming agents trained with traditional reinforcement learning. AI
RANK_REASON OpenAI published a paper detailing a new method for training AI agents using competitive self-play.
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