Space Optimality

Space optimality research focuses on finding efficient solutions within constrained spaces, whether it's the state space of a reinforcement learning problem, the parameter space of a contract, or the function space of a neural network. Current research explores algorithms and architectures that achieve near-optimal solutions within these bounds, including model-based reinforcement learning leveraging value function spaces and novel Banach spaces defined by regularization operators for neural networks. These advancements improve the scalability and efficiency of complex systems, impacting fields like control theory, optimization, and machine learning by enabling the design of more efficient and effective algorithms for diverse applications.