The episode describes Model-Based Transfer Learning (MBTL), an innovative method to enhance the resilience of reinforcement learning models. MBTL addresses the issues of poor generalization and high computational costs associated with traditional methods by optimizing the selection of training tasks using Gaussian processes and Bayesian optimization. Experiments in domains such as urban traffic control and continuous control benchmarks demonstrate MBTL's superiority in terms of efficiency and generalization capabilities, significantly reducing cumulative regret. Finally, the episode outlines potential future developments, such as extending the approach to multi-dimensional scenarios and managing out-of-distribution generalization.