VecCity: A Taxonomy-guided Library for Map Entity Representation Learning

Electronic maps consist of diverse entities, such as points of interest (POIs), road networks, and land parcels, playing a vital role in applications like ITS and LBS. Map entity representation learning (MapRL) generates versatile and reusable data representations, providing essential tools for efficiently managing and utilizing map entity data. Despite the progress in MapRL, two key challenges constrain further development. First, existing research is fragmented, with models classified by the type of map entity, limiting the reusability of techniques across different tasks. Second, the lack of unified benchmarks makes systematic evaluation and comparison of models difficult. To address these challenges, we propose a novel taxonomy for MapRL that organizes models based on functional module-such as encoders, pre-training tasks, and downstream tasks-rather than by entity type. Building on this taxonomy, we present a taxonomy-driven library, VecCity, which offers easy-to-use interfaces for encoding, pre-training, fine-tuning, and evaluation. The library integrates datasets from nine cities and reproduces 21 mainstream MapRL models, establishing the first standardized benchmarks for the field. VecCity also allows users to modify and extend models through modular components, facilitating seamless experimentation. Our comprehensive experiments cover multiple types of map entities and evaluate 21 VecCity pre-built models across various downstream tasks. Experimental results demonstrate the effectiveness of VecCity in streamlining model development and provide insights into the impact of various components on performance. By promoting modular design and reusability, VecCity offers a unified framework to advance research and innovation in MapRL. The code is available at https://github.com/Bigscity-VecCity/VecCity.

Efficient Diversity-based Experience Replay for Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) has achieved remarkable success in solving complex decision-making problems by combining the representation capabilities of deep learning with the decision-making power of reinforcement learning. However, learning in sparse reward environments remains challenging due to insufficient feedback to guide the optimization of agents, especially in real-life environments with high-dimensional states. To tackle this issue, experience replay is commonly introduced to enhance learning efficiency through past experiences. Nonetheless, current methods of experience replay, whether based on uniform or prioritized sampling, frequently struggle with suboptimal learning efficiency and insufficient utilization of samples. This paper proposes a novel approach, diversity-based experience replay (DBER), which leverages the deterministic point process to prioritize diverse samples in state realizations. We conducted extensive experiments on Robotic Manipulation tasks in MuJoCo, Atari games, and realistic in-door environments in Habitat. The results show that our method not only significantly improves learning efficiency but also demonstrates superior performance in sparse reward environments with high-dimensional states, providing a simple yet effective solution for this field.

Large Language Model Enhanced Knowledge Representation Learning: A Survey

The integration of Large Language Models (LLMs) with Knowledge Representation Learning (KRL) signifies a pivotal advancement in the field of artificial intelligence, enhancing the ability to capture and utilize complex knowledge structures. This synergy leverages the advanced linguistic and contextual understanding capabilities of LLMs to improve the accuracy, adaptability, and efficacy of KRL, thereby expanding its applications and potential. Despite the increasing volume of research focused on embedding LLMs within the domain of knowledge representation, a thorough review that examines the fundamental components and processes of these enhanced models is conspicuously absent. Our survey addresses this by categorizing these models based on three distinct Transformer architectures, and by analyzing experimental data from various KRL downstream tasks to evaluate the strengths and weaknesses of each approach. Finally, we identify and explore potential future research directions in this emerging yet underexplored domain, proposing pathways for continued progress.

A General Early-Stopping Module for Crowdsourced Ranking

Crowdsourcing can be used to determine a total order for an object set (e.g., the top-10 NBA players) based on crowd opinions. This ranking problem is often decomposed into a set of microtasks (e.g., pairwise comparisons). These microtasks are passed to a large number of workers and their answers are aggregated to infer the ranking. The number of microtasks depends on the budget allocated for the problem. Intuitively, the higher the number of microtask answers, the more accurate the ranking becomes. However, it is often hard to decide the budget required for an accurate ranking. We study how a ranking process can be terminated early, and yet achieve a high-quality ranking and great savings in the budget. We use statistical tools to estimate the quality of the ranking result at any stage of the crowdsourcing process and terminate the process as soon as the desired quality is achieved. Our proposed early-stopping module can be seamlessly integrated with most existing inference algorithms and task assignment methods. We conduct extensive experiments and show that our early-stopping module is better than other existing general stopping criteria. We also implement a prototype system to demonstrate the usability and effectiveness of our approach in practice.