Reinforcement learning enhances training efficiency in high-performance athletes

Skilled athletes need powerful movement strategies to solve tasks effectively. Typically, athletes learn these strategies with instruction-based teaching methods where coaches offer athletes a correct solution. Inspired by recent evidence from decision neuroscience, we asked whether skilled athletes learn strategy choices better with an evaluation-based training strategy (reinforcement learning). To address this question, we conducted a three-day learning experiment with skilled alpine ski racers (n=98) designed to improve their performance on flat slopes on slalom with four strategies at their disposal to achieve this goal. We compared performance and strategy choices of three groups: a reinforcement learning group, that only received feedback about their race times after every run, a supervised (free choice) learning group, that received strategy instructions from their coach, and a supervised (target skill) learning group, being coached to use the theoretically optimal strategy for skiing well on flats. We found that despite making similar strategy choices, the skiers in the reinforcement learning group, showed greater improvements in their race times during the training sessions than their counterparts in the supervised (free choice) learning group and outperformed them during a subsequent retention test. Surprisingly, the skiers in the reinforcement learning group even showed descriptively (but not significantly) better performance than those in the supervised (target skill) learning group. Our findings show that reinforcement learning can be an effective training strategy for improving strategy choices and performance among skilled athletes, even among the best ones.