Today, video analytics are becoming extremely popular due to the increasing need for extracting valuable information from videos available in public sharing services through camera-driven streams. Typically, video analytics are organized as a set of separate tasks, each of which has different resource requirements (e.g., computational- vs. memory-intensive tasks). The serverless computing paradigm forms a very promising approach for mapping such types of applications, as it enables fine-grained deployment and management in a per-function manner. However, modern serverless frameworks suffer from performance variability issues, due to i) the interference introduced due to the co-location of third-party workloads with the serverless functions and ii) the increasing hardware heterogeneity introduced in public clouds. To this end, this work introduces Darly, a QoS- and heterogeneity-aware Deep Reinforcement Learning-based Scheduler for serverless video analytics deployments. The proposed framework incorporates a DRL agent which exploits low-level performance counters to identify the levels of interference and the degree of heterogeneity in the underlying infrastructure and combines this information along with userdefined QoS requirements to dynamically optimize resource allocations by deciding the placement, migration, or horizontal scaling of serverless functions. Promising results are produced within our experiments, which are accompanied by the intent to further build upon this groundwork.