Goal 4: Solve a wide variety of games using a single agent
We aim to train an agent capable enough to solve any game in our initial metric. Games are virtual mini-worlds that are very diverse, and learning to play games quickly and well will require significant advances in generative models and reinforcement learning(opens in a new window). (We are inspired by the pioneering work of DeepMind(opens in a new window), who have produced impressive(opens in a new window) results(opens in a new window) in this area in the past few years.)
Our projects and fundamental research all have shared cores, so progress on any is likely to benefit the others. Each captures a different aspect of goal-solving, and was chosen for its potential to significantly move our metric.
We’re just getting started on these projects, and the details may change as we gain additional data. We also expect to add new projects over time.
圍棋沒了，遊戲也會的。
在職業選手也打不過AI的時代，也許正好可以都一起想想，我們為什麼打遊戲。