SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge
  Refinement
Authors: name: Runnan Fang, name: Xiaobin Wang, name: Yuan Liang, name: Shuofei Qiao, name: Jialong Wu, name: Zekun Xi, name: Ningyu Zhang, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Huajun Chen

In the interaction between agents and their environments, agents expand their
capabilities by planning and executing actions. However, LLM-based agents face
substantial challenges when deployed in novel environments or required to
navigate unconventional action spaces. To empower agents to autonomously
explore environments, optimize workflows, and enhance their understanding of
actions, we propose SynWorld, a framework that allows agents to synthesize
possible scenarios with multi-step action invocation within the action space
and perform Monte Carlo Tree Search (MCTS) exploration to effectively refine
their action knowledge in the current environment. Our experiments demonstrate
that SynWorld is an effective and general approach to learning action knowledge
in new environments. Code is available at https://github.com/zjunlp/SynWorld.