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Found 15 papers, 7 papers with code
Debunking Disinformation: Revolutionizing Truth with NLP in Fake News Detection
The Internet and social media have altered how individuals access news in the age of instantaneous information distribution.
ProAgent: Building Proactive Cooperative Agents with Large Language Models
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems.
Maximum Entropy Heterogeneous-Agent Reinforcement Learning
We embed cooperative MARL problems into probabilistic graphical models, from which we derive the maximum entropy (MaxEnt) objective for MARL.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Heterogeneous-Agent Reinforcement Learning
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI research.
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library
A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible development for multi-agent tasks and algorithm combinations, while obviating the need to consider compatibility issues.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
Abstract semantic 3D scene understanding is a problem of critical importance in robotics.
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding
Semantic 3D scene understanding is a problem of critical importance in robotics.
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL
In this study, we quantify the agent's behavior difference and build its relationship with the policy performance via {\bf Role Diversity}, a metric to measure the characteristics of MARL tasks.
Neural Trees for Learning on Graphs
We also prove that the number of parameters needed to achieve an $\epsilon$-approximation of the distribution function is exponential in the treewidth of the input graph, but linear in its size.
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers
Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task.
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers
Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task.
Multi-view Drone-based Geo-localization via Style and Spatial Alignment
In this paper, we focus on the task of multi-view multi-source geo-localization, which serves as an important auxiliary method of GPS positioning by matching drone-view image and satellite-view image with pre-annotated GPS tag.
Accelerated Inference in Markov Random Fields via Smooth Riemannian Optimization
We show that this approach, named Fast Unconstrained SEmidefinite Solver (FUSES), can solve large problems in milliseconds.
Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models
Perceptual aliasing is one of the main causes of failure for Simultaneous Localization and Mapping (SLAM) systems operating in the wild.
Robotics 65K05, 62F10, 68T40, 68W40, 68W25, I.2.9; G.1.6
