Dynamic resource allocation in networked systems is needed to continuously achieve end-to-end management objectives. Recent research has shown that reinforcement learning can achieve near-optimal resource allocation policies for realistic system configurations. However, most current solutions require expensive retraining when changes in the system occur. We address this problem and introduce an efficient method to adapt a given base policy to system changes, e.g., to a change in the service offering. In our approach, we adapt a base control policy using a rollout mechanism, which transforms the base policy into an improved rollout policy. We perform extensive evaluations on a testbed where we run applications on a service mesh based on the Istio and Kubernetes platforms. The experiments provide insights into the performance of different rollout algorithms. We find that our approach produces policies that are equally effective as those obtained by offline retraining. On our testbed, effective policy adaptation takes seconds when using rollout, compared to minutes or hours when using retraining. Our work demonstrates that rollout, which has been applied successfully in other domains, is an effective approach for policy adaptation in networked systems.