A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

AlphaZero (Science, December 2018) generalizes AlphaGo Zero's self-play approach to any two-player zero-sum game of perfect information. A single algorithm, with identical hyperparameters, starts from random play and within hours of training achieves superhuman performance in chess (surpassing Stockfish), shogi (surpassing Elmo), and Go (surpassing AlphaGo Zero). The critical insight is that the neural network architecture and MCTS formulation are general — they don't encode domain-specific knowledge about any of the three games. AlphaZero effectively demonstrates that general reinforcement learning is real: one system, one algorithm, and self-play can produce expert-level performance across diverse domains. This was a proof of concept for the kind of generally capable learning system Hassabis has always argued is the right direction for AGI. The chess results were particularly striking: AlphaZero developed attacking chess styles very different from Stockfish's positional play, suggesting the system found genuinely novel strategic principles rather than imitating human master games.