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Deep reinforcement learning has achieved remarkable performance in variousdomains by leveraging deep neural networks for approximating value functionsand policies. However, using neural networks to approximate value functions orpolicy functions still faces challenges, including low sample efficiency andoverfitting. In this paper, we introduce OMNet, a novel learning paradigmutilizing multiple subnetworks within a single network, offering diverseoutputs efficiently. We provide a systematic pipeline, includinginitialization, training, and sampling with OMNet. OMNet can be easily appliedto various deep reinforcement learning algorithms with minimal additionaloverhead. Through comprehensive evaluations conducted on MuJoCo benchmark, ourfindings highlight OMNet's ability to strike an effective balance betweenperformance and computational cost.

One is More: Diverse Perspectives within a Single Network for Efficient DRL