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pro vyhledávání: '"Suarez, Joseph"'
Autor:
Suarez, Joseph
You have an environment, a model, and a reinforcement learning library that are designed to work together but don't. PufferLib makes them play nice. The library provides one-line environment wrappers that eliminate common compatibility problems and f
Externí odkaz:
http://arxiv.org/abs/2406.12905
We present Meta MMO, a collection of many-agent minigames for use as a reinforcement learning benchmark. Meta MMO is built on top of Neural MMO, a massively multiagent environment that has been the subject of two previous NeurIPS competitions. Our wo
Externí odkaz:
http://arxiv.org/abs/2406.05071
Autor:
Suárez, Joseph, Isola, Phillip, Choe, Kyoung Whan, Bloomin, David, Li, Hao Xiang, Pinnaparaju, Nikhil, Kanna, Nishaanth, Scott, Daniel, Sullivan, Ryan, Shuman, Rose S., de Alcântara, Lucas, Bradley, Herbie, Castricato, Louis, You, Kirsty, Jiang, Yuhao, Li, Qimai, Chen, Jiaxin, Zhu, Xiaolong
Neural MMO 2.0 is a massively multi-agent environment for reinforcement learning research. The key feature of this new version is a flexible task system that allows users to define a broad range of objectives and reward signals. We challenge research
Externí odkaz:
http://arxiv.org/abs/2311.03736
Autor:
Liu, Enhong, Suarez, Joseph, You, Chenhui, Wu, Bo, Chen, Bingcheng, Hu, Jun, Chen, Jiaxin, Zhu, Xiaolong, Zhu, Clare, Togelius, Julian, Mohanty, Sharada, Hong, Weijun, Du, Rui, Zhang, Yibing, Wang, Qinwen, Li, Xinhang, Yuan, Zheng, Li, Xiang, Huang, Yuejia, Zhang, Kun, Yang, Hanhui, Tang, Shiqi, Isola, Phillip
In this paper, we present the results of the NeurIPS-2022 Neural MMO Challenge, which attracted 500 participants and received over 1,600 submissions. Like the previous IJCAI-2022 Neural MMO Challenge, it involved agents from 16 populations surviving
Externí odkaz:
http://arxiv.org/abs/2311.03707
Autor:
Suarez, Joseph
Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems. Existing environments feature subsets of these properties, but Neural MMO is the first
Externí odkaz:
https://hdl.handle.net/1721.1/151250
Most reinforcement learning methods rely heavily on dense, well-normalized environment rewards. DreamerV3 recently introduced a model-based method with a number of tricks that mitigate these limitations, achieving state-of-the-art on a wide range of
Externí odkaz:
http://arxiv.org/abs/2310.17805
Autor:
Chen, Yangkun, Suarez, Joseph, Zhang, Junjie, Yu, Chenghui, Wu, Bo, Chen, Hanmo, Zhu, Hengman, Du, Rui, Qian, Shanliang, Liu, Shuai, Hong, Weijun, He, Jinke, Zhang, Yibing, Zhao, Liang, Zhu, Clare, Togelius, Julian, Mohanty, Sharada, Chen, Jiaxin, Li, Xiu, Zhu, Xiaolong, Isola, Phillip
We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-agent systems: participants train teams of agents to complete a multi-t
Externí odkaz:
http://arxiv.org/abs/2308.15802
Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems. Existing environments feature subsets of these properties, but Neural MMO is the first
Externí odkaz:
http://arxiv.org/abs/2110.07594
Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks
Progress in multiagent intelligence research is fundamentally limited by the number and quality of environments available for study. In recent years, simulated games have become a dominant research platform within reinforcement learning, in part due
Externí odkaz:
http://arxiv.org/abs/2001.12004
Autor:
Suarez, Joseph
Generative Adversarial Networks (GANs) have become a dominant class of generative models. In recent years, GAN variants have yielded especially impressive results in the synthesis of a variety of forms of data. Examples include compelling natural and
Externí odkaz:
http://arxiv.org/abs/1904.00724