Personalized Recommender Systems for Gym Workouts: A Reinforcement Learning Approach

Abstract

A good workout is more than a list of exercises. In the gym, recommendations must also decide how much work a user should do, whether that workload is realistic, and how the next recommendation should adapt when a user starts skipping exercises. This makes gym workout recommendation a sequential decision problem rather than a standard item-ranking task. This thesis studies whether reinforcement learning (RL) improves workout recommendation when the problem is extended from exercise selection to full prescription. Starting from the Home-Fitness RL framework of Tragos et al., we develop a simulator-based gym recommendation framework with four environments: exercise-only and full-prescription settings, each with and without skip-based interaction. The full-prescription environments recommend exercise, sets, repetitions, and load, while the skip-enabled environments use skip-only feedback for online personalization. Because suitable realworld gym interaction data was not available, synthetic user pools were used for training and evaluation under static, dynamic, and stress-test conditions. The results show that the value of reinforcement learning depends strongly on the structure of the problem. In the exercise-only setting, the RL algorithm Proximal Policy Optimization (PPO) clearly outperforms random recommendation and remains competitive with Particle Swarm Optimization (PSO), but it does not outperform a strong greedy baseline. In the full-prescription setting, PPO becomes the strongest method and outperforms all baselines, showing that reinforcement learning becomes more useful once the recommendation task includes dose and user-specific capacity. Skip-enabled environments lead to more adherence-aware behavior, but also introduce trade-offs between completion and other reward components. Finally, PPO remains stable under realistic gradual user drift, while highly chaotic user changes substantially reduce performance, especially when online personalization is involved. Overall, the thesis shows that reinforcement learning is not uniformly superior for workout recommendation, but becomes clearly more convincing when the problem is extended to realistic gym prescription and user interaction.