The problem of scheduling of workloads onto heterogeneous processors (e.g., CPUs, GPUs, FPGAs) is of fundamental importance in modern datacenters. Current system schedulers rely on application/system-specific heuristics that have to be built on a case-by-case basis. Recent work has demonstrated ML techniques to automate this heuristic search using black box approaches which require significant training data and time, which make them challenging to use in practice. This paper addresses the challenge in two ways: (i) a domain-driven Bayesian reinforcement learning (RL) model for scheduling, which inherently models the resource dependencies identified from the system architecture; and (ii) a sampling-based technique which allows the computation of gradients of a Bayesian model without performing full probabilistic inference. Together, these techniques reduce both the amount of training-data and -time required to produce scheduling policies that significantly outperform black box approaches by up to 2.2×.