Explore task-driven network discovery through deep reinforcement learning in this 28-minute lecture from the Deep Learning and Combinatorial Optimization 2021 conference. Delve into the challenges of working with incomplete network data and learn how to improve network observability for more accurate analyses. Discover the Network Actor Critic (NAC) framework, which utilizes task-specific network embeddings to reduce state space complexity and learn offline policies for network discovery. Examine the performance of NAC compared to competitive online-discovery algorithms and understand the importance of planning in addressing sparse and changing reward signals. Gain insights into selective harvesting, policy functions, and modeling future rewards in network analysis. Explore real-world applications, including the control of pandemics, and get an overview of the COANET system for tackling complex network problems.