DeepMind Lab2D

Pgx：面向强化学习的硬件加速并行游戏模拟器

Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning

本文提出基于 JAX 并针对 GPU/TPU 优化的棋盘游戏强化学习环境套件 Pgx，通过自动向量化与并行化实现比 Python 现有实现快 10-100 倍的模拟速度，并提供基准环境、小型游戏集和基线模型以加速 RL 研究。

面向高性能强化学习的训练环境

Training Environment for High Performance Reinforcement Learning

本文提出开源强化学习训练环境Tunnel，将F16三维非线性飞行动力学集成到OpenAI Gymnasium，使研究人员和任务规划者能在数天内快速定制观测、动作与训练方法，以应对自主空战中快速变化的对手和传感器能力。

Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments

The capability of a reinforcement learning (RL) agent heavily depends on the diversity of the learning scenarios generated by the environment. Generation of diverse realistic scenarios is challenging for real-time strategy (RTS) environments. The RTS environments are characterized by intelligent entities/non-RL agents cooperating and competing with the RL agents with large state and action spaces over a long period of time, resulting in an infinite space of feasible, but not necessarily realistic, scenarios involving complex interaction among different RL and non-RL agents. Yet, most of the ex…

Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning

Training self-driving cars is often challenging since they require a vast amount of labeled data in multiple real-world contexts, which is computationally and memory intensive. Researchers often resort to driving simulators to train the agent and transfer the knowledge to a real-world setting. Since simulators lack realistic behavior, these methods are quite inefficient. To address this issue, we introduce a framework (perception, planning, and control) in a real-world driving environment that transfers the real-world environments into gaming environments by setting up a reliable Markov Decisi…

Scilab-RL：面向高效强化学习与认知建模研究的软件框架

Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research

针对认知建模与强化学习研究缺乏整合多类功能的模块化工具套件的问题，提出聚焦目标条件强化学习的Scilab-RL软件框架，可降低实验时间成本、提升研究效率。

YuLan-OneSim：迈向基于大语言模型的下一代社会模拟器

YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models

本文提出YuLan-OneSim社会模拟器，具备免代码场景构建、50个默认场景、可进化模拟、支持10万智能体及AI社会研究员功能，旨在提升社会模拟的易用性与规模。

EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine

There has been significant progress in developing reinforcement learning (RL) training systems. Past works such as IMPALA, Apex, Seed RL, Sample Factory, and others, aim to improve the system's overall throughput. In this paper, we aim to address a common bottleneck in the RL training system, i.e., parallel environment execution, which is often the slowest part of the whole system but receives little attention. With a curated design for paralleling RL environments, we have improved the RL environment simulation speed across different hardware setups, ranging from a laptop and a modest workstat…

利用LLM智能体模拟影响力动态

Simulating Influence Dynamics with LLM Agents

本文提出了一款结合舆论动态原理与LLM智能体的开源模拟器，用于建模社交网络中的竞争性影响与反虚假信息策略，降低社会科学等领域研究者的编程门槛。

一个多用途的基于场景的智能家居环境模拟器

A Multi-Purpose Scenario-based Simulator for Smart House Environments

开发智能家居系统一直是这一领域的研究人员和工程师面临的巨大挑战，因为这些系统的实施和评估过程成本高，同时非常耗时。在实际建造之前测试设计的智能房屋被认为是高效智能房屋项目的障碍。这是因为各种传感器，家用电器和设备可用于真正的智能环境。在本文中，我们提出了一个多用途的智能家居模拟系统的设计和实现，设计和模拟智能家居环境的各个方面。该模拟器提供了在虚拟房屋环境的二维的模型中设计房屋平面图和不同虚拟传感器和器具的能力。该模拟器可以连接到任何外部智能家居远程控制系统，比以前更容易为他们的系统提供评估功能。它还支持详细添加新出现的传感器和设备，以帮助保持其与未来仿真需求的兼容性。还可以定义场景来测试设备状态的各种可能组合;因此可以简单地评估不同的标准和变量，而无需在真实环境中进行实验。

FL_PyTorch: optimization research simulator for federated learning

Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared machine learning model while keeping training data locally on the device, thereby removing the need to store and access the full data in the cloud. However, FL is difficult to implement, test and deploy in practice considering heterogeneity in common edge device settings, making it fundamentally hard for researchers to efficiently prototype and test their optimization algorithms. In this work, our aim is to alleviate this problem by introducing FL_PyTorch : a suite of open-source sof…

SIERRA: A Modular Framework for Research Automation and Reproducibility

Modern intelligent systems researchers form hypotheses about system behavior and then run experiments using one or more independent variables to test their hypotheses. We present SIERRA, a novel framework structured around that idea for accelerating research development and improving reproducibility of results. SIERRA accelerates research by automating the process of generating executable experiments from queries over independent variables(s), executing experiments, and processing the results to generate deliverables such as graphs and videos. It shifts the paradigm for testing hypotheses from…

SenseNet：三维物体数据库和触觉模拟器

SenseNet: 3D Objects Database and Tactile Simulator

大多数人工智能研究，因为它涉及到生物感官，一直专注于视觉。最近机器学习，特别是深度学习的爆炸式发展，部分原因是为算法发布了高质量的数据集，这些数据集可以用来对世界进行建模。因此，这些数据集中的大多数都是由图像组成的。我们相信，专注于感觉运动系统和触觉反馈将创造出更好地模仿人类智能的算法。在这里，我们介绍了SenseNet：触觉模拟器的集合和用于操作的3D对象的大规模数据集。SenseNet是为了研究和训练人工智能（AI）通过感觉运动神经系统和触觉反馈与环境交互而创建的。我们的目标是加速图像处理领域的同样爆炸式发展，但针对触觉反馈和感觉运动研究领域。我们希望SenseNet能够为机器学习和计算神经科学社区的研究人员提供全新的探索机会和途径。

一种基于大语言模型的对话推荐系统可控、可扩展、人机协同用户模拟器框架

A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems

针对现有对话推荐系统用户模拟器的局限性，提出基于LLM的CSHI可控可扩展人机协同模拟器框架，通过插件管理器管控模拟行为，适配多场景并生成高拟真用户反馈，助力CRS评估与数据集构建。