Recently, large language models (LLMs) have demonstrated remarkable potential
as an intelligent agent. However, existing researches mainly focus on enhancing
the agent's reasoning or decision-making abilities through well-designed prompt
engineering or task-specific fine-tuning, ignoring the procedure of exploration
and exploitation. When addressing complex tasks within open-world interactive
environments, these methods exhibit limitations. Firstly, the lack of global
information of environments leads to greedy decisions, resulting in sub-optimal
solutions. On the other hand, irrelevant information acquired from the
environment not only adversely introduces noise, but also incurs additional
cost. This paper proposes a novel approach, Weak Exploration to Strong
Exploitation (WESE), to enhance LLM agents in solving open-world interactive
tasks. Concretely, WESE involves decoupling the exploration and exploitation
process, employing a cost-effective weak agent to perform exploration tasks for
global knowledge. A knowledge graph-based strategy is then introduced to store
the acquired knowledge and extract task-relevant knowledge, enhancing the
stronger agent in success rate and efficiency for the exploitation task. Our
approach is flexible enough to incorporate diverse tasks, and obtains
significant improvements in both success rates and efficiency across four
interactive benchmarks.

使用弱探索强利用（WESE）方法，将大型语言模型（LLMs）代理在解决开放世界互动任务时的成功率和效率显著提高，通过分离探索和利用过程，并使用成本效益高的弱代理执行探索任务获取全局知识，然后引入基于知识图谱的策略来存储获取的知识并提取任务相关知识，加强更强的代理在利用任务中的成功率和效率。

WESE: LLM 代理的弱探索强开发

WESE: Weak Exploration to Strong Exploitation for LLM Agents

Multi-agent path finding (MAPF) is the problem of finding collision-free
paths for a team of agents to reach their goal locations. State-of-the-art
classical MAPF solvers typically employ heuristic search to find solutions for
hundreds of agents but are typically centralized and can struggle to scale when
run with short timeouts. Machine learning (ML) approaches that learn policies
for each agent are appealing as these could enable decentralized systems and
scale well while maintaining good solution quality. Current ML approaches to
MAPF have proposed methods that have started to scratch the surface of this
potential. However, state-of-the-art ML approaches produce "local" policies
that only plan for a single timestep and have poor success rates and
scalability. Our main idea is that we can improve a ML local policy by using
heuristic search methods on the output probability distribution to resolve
deadlocks and enable full horizon planning. We show several model-agnostic ways
to use heuristic search with learnt policies that significantly improve the
policies' success rates and scalability. To our best knowledge, we demonstrate
the first time ML-based MAPF approaches have scaled to high congestion
scenarios (e.g. 20% agent density).

多智能体路径规划是解决一组智能体到达目标位置的无碰撞路径问题。先进的经典多智能体路径规划求解器通常采用启发式搜索方法以找到数百个智能体的解决方案，但通常是集中式的，而在短时间内可能难以扩展。机器学习方法学习每个智能体的策略非常吸引人，因为它们可以实现分散式系统，并在保持良好解决方案质量的同时具备良好的扩展性。我们的主要思想是，我们可以通过使用启发式搜索方法来改进机器学习的局部策略，以解决死锁和实现完全水平的规划。我们展示了几种无模型方法来使用带有学习策略的启发式搜索，这些方法显著提高了策略的成功率和可扩展性。据我们所知，我们首次证明了基于机器学习的多智能体路径规划方法在高拥塞场景（例如，20％智能体密度）中的可扩展性。