  In this work we discuss the incorporation of quadratic neurons into policy
networks in the context of model-free actor-critic reinforcement learning.
Quadratic neurons admit an explicit quadratic function approximation in
contrast to conventional approaches where the the non-linearity is induced by
the activation functions. We perform empiric experiments on several MuJoCo
continuous control tasks and find that when quadratic neurons are added to MLP
policy networks those outperform the baseline MLP whilst admitting a smaller
number of parameters. The top returned reward is in average increased by
$5.8\%$ while being about $21\%$ more sample efficient. Moreover, it can
maintain its advantage against added action and observation noise.
