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Found 7 papers, 4 papers with code
Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model
The direct preference optimization (DPO) method, effective in fine-tuning large language models, eliminates the necessity for a reward model.
Emergent collective intelligence from massive-agent cooperation and competition
Since these learned group strategies arise from individual decisions without an explicit coordination mechanism, we claim that artificial collective intelligence emerges from massive-agent cooperation and competition.
Multi-Agent Path Finding via Tree LSTM
In recent years, Multi-Agent Path Finding (MAPF) has attracted attention from the fields of both Operations Research (OR) and Reinforcement Learning (RL).
The NeurIPS 2022 Neural MMO Challenge: A Massively Multiagent Competition with Specialization and Trade
In this paper, we present the results of the NeurIPS-2022 Neural MMO Challenge, which attracted 500 participants and received over 1, 600 submissions.
Real-Time Neural Style Transfer for Videos
More specifically, a hybrid loss is proposed to capitalize on the content information of input frames, the style information of a given style image, and the temporal information of consecutive frames.
A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments
Performance of the system with an increasing number of UAVs in several indoor scenarios with obstacles is tested.
Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO
We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions.
