Agent Learning
Agent learning focuses on developing algorithms that enable artificial agents to learn complex tasks and adapt to dynamic environments, primarily through reinforcement learning and interaction with their surroundings. Current research emphasizes improving agent learning efficiency by incorporating human guidance, leveraging symbolic reasoning and large language models for self-improvement, and designing robust reward structures for multi-agent systems. These advancements are significant for enhancing the capabilities of AI systems in various applications, from robotics and game playing to personalized mobile assistance and complex problem-solving in areas like scheduling and resource allocation.
Papers
October 31, 2024
September 18, 2024
June 26, 2024
May 28, 2024
April 1, 2024
March 12, 2024
February 12, 2024
January 10, 2024
January 4, 2024
October 19, 2023
September 21, 2023
April 4, 2023
November 27, 2022
September 13, 2022
December 30, 2021
December 7, 2021