  Neural control of memory-constrained, agile robots requires small, yet highly
performant models. We leverage graph hyper networks to learn graph hyper
policies trained with off-policy reinforcement learning resulting in networks
that are two orders of magnitude smaller than commonly used networks yet encode
policies comparable to those encoded by much larger networks trained on the
same task. We show that our method can be appended to any off-policy
reinforcement learning algorithm, without any change in hyperparameters, by
showing results across locomotion and manipulation tasks. Further, we obtain an
array of working policies, with differing numbers of parameters, allowing us to
pick an optimal network for the memory constraints of a system. Training
multiple policies with our method is as sample efficient as training a single
policy. Finally, we provide a method to select the best architecture, given a
constraint on the number of parameters. Project website:
https://sites.google.com/usc.edu/graphhyperpolicy
