LLMs have shown impressive progress in robotics (e.g., manipulation and
navigation) with natural language task descriptions. The success of LLMs in
these tasks leads us to wonder: What is the ability of LLMs to solve vehicle
routing problems (VRPs) with natural language task descriptions? In this work,
we study this question in three steps. First, we construct a dataset with 21
types of single- or multi-vehicle routing problems. Second, we evaluate the
performance of LLMs across four basic prompt paradigms of text-to-code
generation, each involving different types of text input. We find that the
basic prompt paradigm, which generates code directly from natural language task
descriptions, performs the best for GPT-4, achieving 56% feasibility, 40%
optimality, and 53% efficiency. Third, based on the observation that LLMs may
not be able to provide correct solutions at the initial attempt, we propose a
framework that enables LLMs to refine solutions through self-reflection,
including self-debugging and self-verification. With GPT-4, our proposed
framework achieves a 16% increase in feasibility, a 7% increase in optimality,
and a 15% increase in efficiency. Moreover, we examine the sensitivity of GPT-4
to task descriptions, specifically focusing on how its performance changes when
certain details are omitted from the task descriptions, yet the core meaning is
preserved. Our findings reveal that such omissions lead to a notable decrease
in performance: 4% in feasibility, 4% in optimality, and 5% in efficiency.
Website: this https URL

LLMs 在车辆路径问题的自然语言任务描述中显示出令人印象深刻的进展，在这项工作中，我们通过构建数据集、评估 LLMs 的性能以及提出自反思的框架，研究了 LLMs 解决车辆路径问题的能力和敏感性。

从词语到路线：应用大型语言模型于车辆路径规划

From Words to Routes: Applying Large Language Models to Vehicle Routing

Developing an agent capable of adapting to unseen environments remains a
difficult challenge in imitation learning. In this work, we present Adaptive
Return-conditioned Policy (ARP), an efficient framework designed to enhance the
agent's generalization ability using natural language task descriptions and
pre-trained multimodal encoders. Our key idea is to calculate a similarity
between visual observations and natural language instructions in the
pre-trained multimodal embedding space (such as CLIP) and use it as a reward
signal. We then train a return-conditioned policy using expert demonstrations
labeled with multimodal rewards. Because the multimodal rewards provide
adaptive signals at each timestep, our ARP effectively mitigates the goal
misgeneralization. This results in superior generalization performances even
when faced with unseen text instructions, compared to existing text-conditioned
policies. To improve the quality of rewards, we also introduce a fine-tuning
method for pre-trained multimodal encoders, further enhancing the performance.
Video demonstrations and source code are available on the project website:
this https URL

通过使用自然语言任务描述和预训练的多模态编码器，我们提出了一种名为自适应返回条件策略（ARP）的高效框架，以增强代理的泛化能力，通过在预训练的多模态嵌入空间（如 CLIP）中计算视觉观察和自然语言指令之间的相似度作为奖励信号，并使用它训练具有多模态奖励标签的返回条件策略，从而有效地减轻目标误泛化。即使面对未知的文本指令，相比于现有的文本条件策略，我们的 ARP 通过提供每个时间步骤上的自适应信号，表现出卓越的泛化性能。为了提高奖励的质量，我们还引入了一种用于预训练的多模态编码器的微调方法，进一步增强性能。

自适应多模态奖励引导智能体

Guide Your Agent with Adaptive Multimodal Rewards

Strong inductive biases give humans the ability to quickly learn to perform a
variety of tasks. Although meta-learning is a method to endow neural networks
with useful inductive biases, agents trained by meta-learning may sometimes
acquire very different strategies from humans. We show that co-training these
agents on predicting representations from natural language task descriptions
and programs induced to generate such tasks guides them toward more human-like
inductive biases. Human-generated language descriptions and program induction
models that add new learned primitives both contain abstract concepts that can
compress description length. Co-training on these representations result in
more human-like behavior in downstream meta-reinforcement learning agents than
less abstract controls (synthetic language descriptions, program induction
without learned primitives), suggesting that the abstraction supported by these
representations is key.

通过在自然语言任务描述和生成这些任务的程序中预测表示，并指导它们对更具人类化的归纳偏差进行联合训练，结果表明在下游元增强学习代理中，这些表示的抽象支持是关键。