Nested Rollout Policy Adaptation

Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm used to optimize sequential decision-making problems, aiming to find near-optimal solutions efficiently. Current research focuses on improving NRPA's performance through techniques like limiting repetitive searches, incorporating neighborhood search heuristics, and adapting it for specific applications such as vehicle routing and humanoid control, often leveraging transformer-based models for the latter. These advancements demonstrate NRPA's versatility in tackling complex combinatorial optimization and control problems across diverse domains, offering improvements over existing methods in terms of solution quality and computational efficiency.