Maze Navigation

Maze navigation research explores efficient and robust methods for agents, both single and multi-agent, to traverse complex environments. Current research focuses on developing advanced algorithms, including reinforcement learning with various neural network architectures (e.g., Value Iteration Networks, hybrid quantum neural networks), and hierarchical approaches to improve planning and skill diversity, often incorporating elements of decision theory or predictive processing. This field is significant for advancing artificial intelligence, particularly in robotics, by enabling autonomous systems to operate effectively in unknown or dynamic settings, with applications ranging from search and rescue to exploration of hazardous environments.