Meta-Learning and Planning Algorithms

Meta-learning a planning algorithm during inference can enhance the performance of a function approximator. The discussion emphasizes that modern systems should advance beyond traditional planning methods like MCTS, which have limitations in learning effective searches. Future developments may focus on creating systems that learn to plan independently rather than relying on predetermined algorithms. There is also recognition that recurrent neural networks incorporate a form of learning feedback from their actions, contributing to an evolving understanding of planning.