1

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. Current …
Cite Code
STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning
Centralized Training with Decentralized Execution (CTDE) has been proven to be an effective paradigm in cooperative multi-agent …
PDF Cite
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents
The generalization of decision-making agents encompasses two fundamental elements: learning from past experiences and reasoning in …
PDF Cite Code Poster
Maximum Entropy Heterogeneous-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years. However, existing …
PDF Cite
BeaverTails: A Human-Preference Dataset for LLM Harmlessness Alignment
In this paper, we introduce the BeaverTails dataset, aimed at fostering research on safety alignment in large language models (LLMs). …
PDF Cite
Safety Gymnasium: A Unified Safe Reinforcement Learning Benchmark
Artificial intelligence (AI) systems possess significant potential to drive societal progress. However, their deployment often faces …
PDF Cite
Unidexgrasp++: Improving Dexterous Grasping Policy Learning via Geometry-Aware Curriculum and Iterative Generalist-Specialist Learning
We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud …
PDF Cite
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning …
PDF Cite
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
In order for agents in multi-agent systems (MAS) to be safe, they need to take into account the risks posed by the actions of other …
PDF Cite
Regret-Minimizing Double Oracle for Extensive-Form Games
By incorporating regret minimization, double oracle methods have demonstrated rapid convergence to Nash Equilibrium (NE) in normal-form …
PDF Cite