Deep reinforcement learning for improving competitive cycling performance

Abstract

Developing expert systems that make use of artificial intelligence (AI) to provide predictive analytics as well as targeted recommendations for decision support has been gaining momentum in recent years. Both academia and industry are looking into creating such systems to solve real-world problems and tackle specific challenges. In our work, we investigate the potential application of different machine learning approaches to solutions around competitive cycling. Specifically, we build and evaluate prediction models that are capable of accurately predicting a cyclist’s speed and heart rate using sensory information collected during bike rides. In addition, we create a recommendation module that is able to provide real-time action suggestions to cyclists regarding their posture with the goal of improving their overall performance. We achieve this using a combination of model-based reinforcement learning (RL) and deep RL. In particular, we use model-based RL to learn a “simulator” of bike rides using the prediction models and action profiles extracted from sensors placed on the cyclists’ back. We then use deep Q-learning in the simulator to extract policies that improve a cyclist’s behavior during a bike ride. Our evaluation shows that by recommending specific actions throughout the ride, cyclists can increase their overall average speed with only a minimal impact on their heart rate. The results presented in this paper constitute clear evidence that advanced AI techniques are a prime candidate for further developing intelligent solutions in competitive cycling and other similar areas.