  Goal-conditioned Reinforcement Learning (RL) aims at learning optimal
policies, given goals encoded in special command inputs. Here we study
goal-conditioned neural nets (NNs) that learn to generate deep NN policies in
form of context-specific weight matrices, similar to Fast Weight Programmers
and other methods from the 1990s. Using context commands of the form "generate
a policy that achieves a desired expected return," our NN generators combine
powerful exploration of parameter space with generalization across commands to
iteratively find better and better policies. A form of weight-sharing
HyperNetworks and policy embeddings scales our method to generate deep NNs.
Experiments show how a single learned policy generator can produce policies
that achieve any return seen during training. Finally, we evaluate our
algorithm on a set of continuous control tasks where it exhibits competitive
performance. Our code is public.
