What’s Neural Population Learning?

The need for diverse policies for strategy games like StarCraft and poker are addressed by growing a robust policy population by iteratively training new policies against the existing ones. However, the approach has two challenges: Firstly, under a limited budget, the best response operators need truncating, resulting in under-trained good responses. Secondly, repeated learning of basic skills is wasteful and becomes intractable against stronger opponents. Now, DeepMind and University College London have developed Neural Population Learning (NeuPL) to solve both issues. The researchers discovered that NeuPL guarantees the best responses under mild assumptions by showcasing a single conditional model policy. Moreover, NeuPL helps in transfer learning across policies. The research sho