In our latest paper, published in the journal Scientific Reports, we use a branch of game theory to analyse asymmetric games -  games where each player has different strategies, goals and rewards.  Our results give us new insights into these situations and reveal a surprisingly simple way to analyse them, giving us a new tool which may be of use in economics, evolutionary biology and the analysis of environments where multiple AI systems must interact with each other.

DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. - deepmind/dm_control