By increasing the step frequency of the runners, it is possible to reduce the risk of injuries due to overload. Techniques like auditory pacing help the athletes to have better control over their step frequency. Nevertheless, synchronizing to a continuous external rhythm costs energy. For this reason, the use of intermittent pacing may be more energy-efficient and more user-friendly for the athlete. We propose using experimental data from previous studies, that analyzed the response of runners to intermittent pacing, to find the most efficient approach for providing the pacing. For this purpose we use reinforcement learning techniques to learn and train our target behavior. This behavior is represented as the target policy and the experimental data is assumed to be sampled using a stochastic sampling policy. However, using only a single batch of initial training data presents a problem due to the continuously increasing difference between the initial sampling policy and the target policy being learned. The use of a batch off-policy algorithm with a standard deviation correction (OPPOSD) presented in (Liu et al., 2019) is then proposed. This algorithm benefits from the advantages of the sampling efficiency characteristic of the off-policy approaches and also introduces a fixing term to tackle the mismatch between the policies. To train and evaluate the learned policies based on the algorithm, a pace behavior simulator was developed from the data of the experiments. A Markov Decision Problem (MDP) was defined on top of the simulator that determines the rules of the pacing environment that the algorithm is set to learn. After translating the experimental data into MDP-like transitions, the OPPOSD algorithm is able to learn a relatively good target policy for the pacing problem. For a future application, the resulting trained model could be deployed for real runners while still having a continuous improvement of the policy in an on-policy or off-policy approach.