text: The authors present an algorithm for training ensembles of policy networks that regularly mixes different policies in the ensemble together by distilling a mixture of two policies into a single policy network, adding it to the ensemble and selecting the strongest networks to remain (under certain definitions of a "strong" network).  The experiments compare favorably against PPO and A2C baselines on a variety of MuJoCo tasks,  although I would appreciate a wall-time comparison as well,  as training the "crossover" network is presumably time-consuming. It seems that for much of the paper, the authors could dispense with the genetic terminology altogether - and I mean that as a compliment.  There are few if any valuable ideas in the field of evolutionary computing  and I am glad to see the authors use sensible gradient-based learning for GPO, even if it makes it depart from what many in the field would consider "evolutionary" computing.  Another point on terminology that is important to emphasize - the method for training the crossover network by direct supervised learning from expert trajectories is technically not imitation learning but behavioral cloning.  I would perhaps even call this a distillation network rather than a crossover network.  In many robotics tasks behavioral cloning is known for overfitting to expert trajectories,  but that may not be a problem in this setting as "expert" trajectories can be generated in unlimited quantities.