Model Based Adaptation

Model-based adaptation focuses on enabling systems, particularly AI agents and robots, to rapidly and efficiently adjust to new or changing environments using learned models of the world. Current research emphasizes improving the sample efficiency of adaptation, often leveraging techniques like prioritized experience replay and incorporating large language models to extract and dynamically update rules for reasoning and decision-making. This approach holds significant promise for enhancing the robustness and adaptability of AI systems across diverse applications, from robotics and reinforcement learning to knowledge graph reasoning and continual learning.