Recently, accelerator/network co-search has shown great promise in reducing the complexity of co-design and achieving higher model accuracy. Unlike the manual design methodology, it automatically learns the optimal network architecture and corresponding accelerator under the given constraints. However, prior works do not consider the direct feedback of searched accelerators on the network search in each step, which leads to low converge speed and sub-optimal solutions. To address this issue, we propose DAN, a reinforcement learning-based framework for fast and accurate accelerator/network co-search. The fundamental idea is to model the co-search as interleaved network-aware accelerator search (AS) and accelerator-aware network search (NS) using separate RL agents, which improves the performance of AS and NS, and encourages a tight interaction between AS and NS. Experimental results show that our proposed method consistently outperforms single-agent based method in terms of converge speed and performance.