prediction and control test by having it not just learn from reward signal because it might be very sparse but have the agent absorb other signals of course nothing else we want to supervise as a human because then it becomes very tedious but self supervising those things that are available in the environment that the agent could try to learn from even if it's not exactly rewards signal so the unreal agent which stands for unsupervised reinforced planning and auxiliary learning showed it tenants improvement in data