To adapt to real-time variations, stop-skipping control has been widely adopted in public transport systems. However, solving the optimal stopping pattern for buses in static bus scheduling problems is challenging due to its combinatorial nature. The complexity of this problem increases exponentially as the number of stations increases, making it difficult to find the best solution in real time. To overcome such a limitation, this paper proposes an adaptive scheduling game model approach using the stop skipping control strategy. The adaptive game model aims to minimize passenger delay by adjusting bus stops, demonstrating the effectiveness of continuous schedule adaptation against a fixed, pre-determined schedule. This game is then solved with Reinforcement Learning (RL) to optimize the bus scheduling sequence based the current system’s state. We compare this approach against the Simulated Annealing metaheuristic algorithm in finding a near-optimal schedule. Our results show that the RL-based adaptive scheduling outperforms the schedule found statically and all-stop schedules, reducing waiting, ride, and total trip times.