Emergent communication in artificial agents has been studied to understand language evolution, as well as to develop artificial systems that learn to communicate with humans. We show that agents performing a cooperative navigation task in various gridworld environments learn an interpretable communication protocol that enables them to efficiently, and in many cases, optimally, solve the task. An analysis of the agents' policies reveals that emergent signals spatially cluster the state space, with signals referring to specific locations and spatial directions such as "left", "up", or "upper left room". Using populations of agents, we show that the emergent protocol has basic compositional structure, thus exhibiting a core property of natural language.
Multi-agent reinforcement learning (MARL) has shown recent success in increasingly complex fixed-team zero-sum environments. However, the real world is not zero-sum nor does it have fixed teams; humans face numerous social dilemmas and must learn when to cooperate and when to compete. To successfully deploy agents into the human world, it may be important that they be able to understand and help in our conflicts. Unfortunately, selfish MARL agents typically fail when faced with social dilemmas. In this work, we show evidence of emergent direct reciprocity, indirect reciprocity and reputation, …
We present ESCELL, a method for developing an emergent symbolic language of communication between multiple agents reasoning about cells. We show how agents are able to cooperate and communicate successfully in the form of symbols similar to human language to accomplish a task in the form of a referential game (Lewis' signaling game). In one form of the game, a sender and a receiver observe a set of cells from 5 different cell phenotypes. The sender is told one cell is a target and is allowed to send one symbol to the receiver from a fixed arbitrary vocabulary size. The receiver relies on the i…
Peking University（北京大学）；Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院）
Being able to predict the mental states of others is a key factor to effective social interaction. It is also crucial for distributed multi-agent systems, where agents are required to communicate and cooperate. In this paper, we introduce such an important social-cognitive skill, i.e. Theory of Mind (ToM), to build socially intelligent agents who are able to communicate and cooperate effectively to accomplish challenging tasks. With ToM, each agent is capable of inferring the mental states and intentions of others according to its (local) observation. Based on the inferred states, the agents d…
Simon Fraser University（西蒙菲莎大学）；IIT Kanpur（坎pur印度理工学院）；UIUC（伊利诺伊大学厄巴纳-香槟分校）
Communication between embodied AI agents has received increasing attention in recent years. Despite its use, it is still unclear whether the learned communication is interpretable and grounded in perception. To study the grounding of emergent forms of communication, we first introduce the collaborative multi-object navigation task CoMON. In this task, an oracle agent has detailed environment information in the form of a map. It communicates with a navigator agent that perceives the environment visually and is tasked to find a sequence of goals. To succeed at the task, effective communication i…
We introduce a method for real-time navigation and tracking with differentiably rendered world models. Learning models for control has led to impressive results in robotics and computer games, but this success has yet to be extended to vision-based navigation. To address this, we transfer advances in the emergent field of differentiable rendering to model-based control. We do this by planning in a learned 3D spatial world model, combined with a pose estimation algorithm previously used in the context of TSDF fusion, but now tailored to our setting and improved to incorporate agent dynamics. We…
Georgia Institute of Technology（佐治亚理工学院）；FAIR, Meta AI（元人工智能基础人工智能研究院）；Simon Fraser University（西蒙弗雷泽大学）；Google Research Atlanta（谷歌亚特兰大研究院）；Oregon State University（俄勒冈州立大学）
Animal navigation research posits that organisms build and maintain internal spatial representations, or maps, of their environment. We ask if machines -- specifically, artificial intelligence (AI) navigation agents -- also build implicit (or 'mental') maps. A positive answer to this question would (a) explain the surprising phenomenon in recent literature of ostensibly map-free neural-networks achieving strong performance, and (b) strengthen the evidence of mapping as a fundamental mechanism for navigation by intelligent embodied agents, whether they be biological or artificial. Unlike animal…
Communication-Free Collective Navigation for a Swarm of UAVs via LiDAR-Based Deep Reinforcement Learning
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology（机械工程系，釜山国立科学技术研究院）；Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology（机械工程系，韩国科学技术院）
A 3D Isovist World Model -- Revealing a City's Unseen Geometry and Its Emergent Cross-City Signature
The Bartlett School of Sustainable Construction University College London, UK（可持续建设学院伦敦大学学院，英国）；Department of Geography University College London, UK（地理系伦敦大学学院，英国）；School of Project Management, Faculty of Engineering The University of Sydney, AU（工程学院项目管理学院悉尼大学，澳大利亚）；School of Engineering Cardiff University, UK（工程学院卡迪夫大学，英国）；School of Architecture Tsinghua University, Beijing, CN（建筑学院清华大学，北京，中国）
EURECOM（欧洲通信学院）；Technical University of Munich（慕尼黑工业大学）
Harvesting data from distributed Internet of Things (IoT) devices with multiple autonomous unmanned aerial vehicles (UAVs) is a challenging problem requiring flexible path planning methods. We propose a multi-agent reinforcement learning (MARL) approach that, in contrast to previous work, can adapt to profound changes in the scenario parameters defining the data harvesting mission, such as the number of deployed UAVs, number, position and data amount of IoT devices, or the maximum flying time, without the need to perform expensive recomputations or relearn control policies. We formulate the pa…
Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation
Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Biological Engineering, Massachusetts Institute of Technology（原子分子力学实验室（LAMM），生物工程系，麻省理工学院）；Department of Biomedical Engineering, Tufts University（生物医学工程系，塔夫茨大学）；Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Department of Mechanical Engineering, Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology（原子分子力学实验室（LAMM），土木与环境工程系，机械工程系，计算科学与工程中心，计算机科学学院，麻省理工学院）
