  Recent endeavors towards directly using large language models (LLMs) as agent
models to execute interactive planning tasks have shown commendable results.
Despite their achievements, however, they still struggle with brainless
trial-and-error in global planning and generating hallucinatory actions in
local planning due to their poor understanding of the ''real'' physical world.
Imitating humans' mental world knowledge model which provides global prior
knowledge before the task and maintains local dynamic knowledge during the
task, in this paper, we introduce parametric World Knowledge Model (WKM) to
facilitate agent planning. Concretely, we steer the agent model to
self-synthesize knowledge from both expert and sampled trajectories. Then we
develop WKM, providing prior task knowledge to guide the global planning and
dynamic state knowledge to assist the local planning. Experimental results on
three complex real-world simulated datasets with three state-of-the-art
open-source LLMs, Mistral-7B, Gemma-7B, and Llama-3-8B, demonstrate that our
method can achieve superior performance compared to various strong baselines.
Besides, we analyze to illustrate that our WKM can effectively alleviate the
blind trial-and-error and hallucinatory action issues, providing strong support
for the agent's understanding of the world. Other interesting findings include:
1) our instance-level task knowledge can generalize better to unseen tasks, 2)
weak WKM can guide strong agent model planning, and 3) unified WKM training has
promising potential for further development. Code will be available at
https://github.com/zjunlp/WKM.
