  The Reinforcement Learning (RL) building blocks, i.e. Q-functions and policy
networks, usually take elements from the cartesian product of two domains as
input. In particular, the input of the Q-function is both the state and the
action, and in multi-task problems (Meta-RL) the policy can take a state and a
context. Standard architectures tend to ignore these variables' underlying
interpretations and simply concatenate their features into a single vector. In
this work, we argue that this choice may lead to poor gradient estimation in
actor-critic algorithms and high variance learning steps in Meta-RL algorithms.
To consider the interaction between the input variables, we suggest using a
Hypernetwork architecture where a primary network determines the weights of a
conditional dynamic network. We show that this approach improves the gradient
approximation and reduces the learning step variance, which both accelerates
learning and improves the final performance. We demonstrate a consistent
improvement across different locomotion tasks and different algorithms both in
RL (TD3 and SAC) and in Meta-RL (MAML and PEARL).
