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