Designing network protocols that work well under a variety of network conditions typically involves a large amount of manual tuning and guesswork, particularly when choosing dynamic update strategies for numeric parameters. The situation is made more complex by adding the Quality of Service (QoS) requirements to a network protocol. A fundamentally different approach for designing protocols is via Reinforcement Learning (RL) algorithms which allow protocols to be automatically optimized through network simulation. However, getting RL to work well in practice requires considerable expertise and carries a significant implementation overhead. To help overcome this challenge, recent work has developed the programming paradigm of Adaptation-Based Programming (ABP), which allows programmers who are not RL-experts to write self-optimizing “adaptive programs”. In this work, we study the potential of applying ABP to the problem of designing network protocols via simulation. We demonstrate the flexibility of our design method via a number of case studies, each of which investigates the performance of an adaptive program written for the backoff mechanism of the MAC layer in the 802.11 standard. Our results show that the learned protocols typically outperform 802.11 on a number of evaluation metrics and network conditions.