AlphaZero db
Explores diversity in AI decision-making by training a league of agents with distinct playing styles, revealing multiple qualitatively different ways to play chess at a superhuman level.
Artificial Intelligence (AI) systems have surpassed human intelligence in a variety of computational tasks. However, AI systems, like humans, make mistakes, have blind spots, hallucinate, and struggle to generalize to new situations. We explored whether AI systems can benefit from creative decision-making mechanisms when pushed to the limits of its computational rationality.
AlphaZerodb is a league of AlphaZero agents, represented via a latent-conditioned architecture, and trained with quality-diversity techniques to generate a wider range of ideas. It then selects the most promising ones with sub-additive planning. AlphaZerodb plays chess in diverse ways, solves more puzzles as a group and outperforms a more homogeneous team.