Continuous Space

Continuous space research focuses on developing methods to efficiently model and analyze systems with infinitely many possible states and actions, a challenge arising in diverse fields from robotics and machine learning to game theory and physics. Current research emphasizes developing efficient algorithms and model architectures, such as neural networks and hierarchical transformers, to handle the computational complexity of continuous spaces, often incorporating techniques like sampling-based planning, gradient flows, and optimal transport. These advancements are crucial for improving the performance and scalability of AI systems, enabling more realistic simulations, and facilitating the solution of complex optimization and control problems in various scientific and engineering domains.