📝 Editorial Gaming has been at the center of the ML renaissance for the last few years. In many ways, the deep learning race was kicked off by AlphaGo performance again Go’s legend Le Sedol. However, most of the advancements in ML for gaming have been specialized in either perfect or imperfect information games but never both at the same time. Models that mastered chess and Go struggle with imperfect games such as Poker. Even models like AlphaZero, which learned to play multiple games simultaneously, were constrained to perfect information environments. The reason for this is based on the intrinsic dynamics of both types of game environments. Perfect information games, like Chess and Go, are a good fit for ML techniques relying on self-play learning and local min-max search of the gamespace, while imperfect information games like Poker rely on game-reasoning techniques. Earlier this week, DeepMind unveiled a new ML method that can change these constraints forever.
🕹 A Massive Leap in ML for Gaming
🕹 A Massive Leap in ML for Gaming
🕹 A Massive Leap in ML for Gaming
📝 Editorial Gaming has been at the center of the ML renaissance for the last few years. In many ways, the deep learning race was kicked off by AlphaGo performance again Go’s legend Le Sedol. However, most of the advancements in ML for gaming have been specialized in either perfect or imperfect information games but never both at the same time. Models that mastered chess and Go struggle with imperfect games such as Poker. Even models like AlphaZero, which learned to play multiple games simultaneously, were constrained to perfect information environments. The reason for this is based on the intrinsic dynamics of both types of game environments. Perfect information games, like Chess and Go, are a good fit for ML techniques relying on self-play learning and local min-max search of the gamespace, while imperfect information games like Poker rely on game-reasoning techniques. Earlier this week, DeepMind unveiled a new ML method that can change these constraints forever.