game theory | Synced

by Synced 2022-02-17 3

DeepMind & UCL Propose Neural Population Learning: An Efficient and General Framework That Learns Strategically Diverse Policies for Real-World Games

A research team from DeepMind and University College London proposes Neural Population Learning (NeuPL), an efficient and general framework that learns and represents diverse policies in symmetric zero-sum games within a single conditional network.

by Synced 2021-12-08 1

AI Machine Learning & Data Science Research

DeepMind’s PoG Excels in Perfect and Imperfect Information Games, Advancing Research on General Algorithms for Arbitrary Environments

DeepMind researchers introduce Player of Games (PoG), a general-purpose algorithm that applies self-play learning, search, and game-theoretic reasoning to perfect and imperfect information games, taking an important step toward truly general algorithms for arbitrary environments.