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https://arxiv.org/abs/2406.04151 AgentGym: Evolving Large Language Model-based Agents across Diverse EnvironmentsBuilding generalist agents that can handle diverse tasks and evolve themselves across different environments is a long-term goal in the AI community. Large language models (LLMs) are considered a promising foundation to build such agents due to their generarxiv.org 여기서는 직접 LLM을 학습하는 A..

https://arxiv.org/abs/2311.05997 JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language ModelsAchieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle whenarxiv.orgJARVIS-1은 멀티모달..

https://arxiv.org/abs/2402.17574 Agent-Pro: Learning to Evolve via Policy-Level Reflection and OptimizationLarge Language Models (LLMs) exhibit robust problem-solving capabilities for diverse tasks. However, most LLM-based agents are designed as specific task solvers with sophisticated prompt engineering, rather than agents capable of learning and evolving throarxiv.org 여태까지는 한 판의 게임을 어떻게 이길까, 목..

DEPS2024.11.27 - [인공지능/논문 리뷰 or 진행] - Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents - 논문 리뷰 Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents - 논https://arxiv.org/abs/2302.01560 Describe, Explain, Plan and Select: Interactive Planning with Large Langua..

https://arxiv.org/abs/2302.01560 Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task AgentsWe investigate the challenge of task planning for multi-task embodied agents in open-world environments. Two main difficulties are identified: 1) executing plans in an open-world environment (e.g., Minecraft) necessitates accurate and multi-step..