  Deep reinforcement learning has achieved remarkable performance in various
domains by leveraging deep neural networks for approximating value functions
and policies. However, using neural networks to approximate value functions or
policy functions still faces challenges, including low sample efficiency and
overfitting. In this paper, we introduce OMNet, a novel learning paradigm
utilizing multiple subnetworks within a single network, offering diverse
outputs efficiently. We provide a systematic pipeline, including
initialization, training, and sampling with OMNet. OMNet can be easily applied
to various deep reinforcement learning algorithms with minimal additional
overhead. Through comprehensive evaluations conducted on MuJoCo benchmark, our
findings highlight OMNet's ability to strike an effective balance between
performance and computational cost.
