Self Play

Self-play, a reinforcement learning technique where agents train by interacting with copies of themselves, aims to create robust and adaptable AI agents. Current research focuses on applying self-play across diverse domains, including robotics, autonomous driving, language modeling, and multi-agent games, often employing model architectures like transformers and algorithms such as Monte Carlo Tree Search and population-based training. This approach is proving valuable for generating high-quality training data, improving model generalization, and fostering the development of more sophisticated AI systems capable of handling complex, real-world scenarios. The resulting advancements have significant implications for both theoretical understanding of multi-agent systems and practical applications in various fields.