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Unmanned aerial vehicles (UAVs) play a critical role in radio surveillance to decipher malicious messages, thanks to their flexibility, mobility, and likely line-of-sight (LoS) to ground targets. Reconfigurable intelligent surfaces (RISs) can potentially create radio surveillance channels towards the UAVs by passively configuring the radio environments without raising suspicion. This paper presents a new deep reinforcement learning (DRL)-driven framework for radio surveillance, where a fixed-wing UAV is employed to acquire the radio fingerprint of a suspicious transmitter (Tx) with the aid of a benign RIS. A new Twin Delayed Deep Deterministic policy gradient (TD3) model is designed to allow the UAV to learn its trajectory and the RIS configuration based on its observed transmit rate of the suspicious Tx, eliminating the need for channel state information to and from the RIS. The novel contributions include the …