Abstract: Efficient exploration is crucial to sample efficient reinforcement learning. In this paper, we present a scalable exploration method called \emph{HyperDQN}, which builds on the famous Deep Q-Network (DQN) \citep{mnih2015human} and extends the idea of hyper model \citep{dwaracher20hypermodel} for deep reinforcement learning. In particular, \emph{HyperDQN} maintains a probabilistic meta-model that captures the epistemic uncertainty of the $Q$-value function over the parameter space. This meta-model samples randomized $Q$-value functions, which will generate exploratory action sequences for deep exploration. The proposed method requires fewer samples to achieve substantially better performance than DQN and BootstrappedDQN \citep{osband16boostrapdqn} on hard-exploration tasks, including deep sea, grid world, and mountain car. The numerical results demonstrate that the developed approach can lead to efficient exploration with limited computation resources.