Due to the realization that deep reinforcement learning algorithms trained on
high-dimensional tasks can strongly overfit to their training environments,
there have been several studies that investigated the generalization
performance of these algorithms. However, there has been no sim
这项研究探索了元强化学习（Meta RL），通过对定义泛化限制和确保收敛的深入研究。通过采用一种创新的理论框架，评估了 Meta RL 算法的有效性和性能。研究分析了影响 Meta RL 适应性的因素，揭示了算法设计与任务复杂性之间的关系。此外，我们根据已经证明的条件确保 Meta RL 策略收敛于解决方案。该研究全面了解了 Meta RL 算法在各种情况下的收敛行为，从而深入探究了其长期性能的驱动力，包括收敛和实时效率，提供了对这些算法的能力的透视。