activity
20182025
most citedInvestigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering

69 citations · 151 across the 30 of their papers we have counts for

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6 papers · 1 filter

cs.AI2025

EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making

Yang Cheng, Weiyu Ma, Zilai Wang +6

Complex decision-making often requires agents to progress through intermediate tasks rather than solve the final target directly. Existing LLM self-refinement methods typically ite…

cs.AI2025

Empowering Economic Simulation for Massively Multiplayer Online Games through Generative Agent-Based Modeling

Bihan Xu, Shiwei Zhao, Runze Wu +10

Within the domain of Massively Multiplayer Online (MMO) economy research, Agent-Based Modeling (ABM) has emerged as a robust tool for analyzing game economics, evolving from rule-b…

cs.AI2023★ 1 cited

AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model

Zibin Dong, Yifu Yuan, Jianye Hao +7

Aligning agent behaviors with diverse human preferences remains a challenging problem in reinforcement learning (RL), owing to the inherent abstractness and mutability of human pre…

cs.AI2023★ 1 cited

Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition

Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas +27

To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 20…

cs.AI2023★ 2 cited

Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning

Rundong Wang, Longtao Zheng, Wei Qiu +7

Recent advances in multi-agent reinforcement learning (MARL) allow agents to coordinate their behaviors in complex environments. However, common MARL algorithms still suffer from s…

cs.AI2020★ 8 cited

Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning

Hangtian Jia, Yujing Hu, Yingfeng Chen +4

The development of deep reinforcement learning (DRL) has benefited from the emergency of a variety type of game environments where new challenging problems are proposed and new alg…