Application of Dynamic Multi-Armed Bandit in Real-Time System: Comparative Study of Thompson Sampling and Ucb Algorithm

This study compares the performance of Thompson Sampling (TS) and Upper Confidence Bound (UCB) algorithms under the framework of dynamic multi-arm slot machines. In the experiments of three environments: static, gradient and mutation, the cumulative regret rate of the adaptive Thompson sampling in the mutation environment (286.6) was significantly lower than that of the standard UCB (376.5) and the standard Thompson sampling (346.8), with the performance improved by 24% and 17.4% respectively, and the average reward was 0.71. The cumulative regret rate of the hybrid algorithm in the three environments (264.2) is close to that of the adaptive Thompson sampling (264.0), and its robustness is outstanding. The dynamic environment significantly affects the algorithm differences. The gap between the optimal and worst algorithms in the static environment is 25.7, and the gap in the sudden change environment expands to 89.9. The adaptive mechanism dynamically adjusts the response fluctuations of posterior parameters, and the hybrid algorithm balances exploration and utilization, which has significant application value in video streams and recommendation systems. This research provides a quantitative basis for the selection of dynamic decision-making algorithms and reveals the applicable boundaries of adaptive strategies and hybrid strategies.