Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning

• URL: http://arxiv.org/abs/2311.03736v1
• Date: Tue, 7 Nov 2023 05:36:39 GMT
• Title: Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
• Authors: Joseph Su\'arez, Phillip Isola, Kyoung Whan Choe, David Bloomin, Hao Xiang Li, Nikhil Pinnaparaju, Nishaanth Kanna, Daniel Scott, Ryan Sullivan, Rose S. Shuman, Lucas de Alc\^antara, Herbie Bradley, Louis Castricato, Kirsty You, Yuhao Jiang, Qimai Li, Jiaxin Chen, Xiaolong Zhu
• Abstract summary: Neural MMO 2.0 is a massively multi-agent environment for reinforcement learning research. It features a flexible task system that allows users to define a broad range of objectives and reward signals. Version 2.0 is a complete rewrite of its predecessor with three-fold improved performance and compatibility with CleanRL.
• Score: 36.03451274861878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 researchers to train agents capable of generalizing to tasks, maps, and opponents never seen during training. Neural MMO features procedurally generated maps with 128 agents in the standard setting and support for up to. Version 2.0 is a complete rewrite of its predecessor with three-fold improved performance and compatibility with CleanRL. We release the platform as free and open-source software with comprehensive documentation available at neuralmmo.github.io and an active community Discord. To spark initial research on this new platform, we are concurrently running a competition at NeurIPS 2023.

Related papers

• OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization [66.22117723598872]
  We introduce an open-source framework designed to facilitate the development of multimodal web agent. We first train the base model with imitation learning to gain the basic abilities. We then let the agent explore the open web and collect feedback on its trajectories.
  arXiv  Detail & Related papers  (2024-10-25T15:01:27Z)
• OpenHands: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
  We introduce OpenHands, a platform for the development of AI agents that interact with the world in similar ways to a human developer. We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T17:50:43Z)
• Massively Multiagent Minigames for Training Generalist Agents [1.2762029466132794]
  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.
  arXiv  Detail & Related papers  (2024-06-07T16:41:05Z)
• The NeurIPS 2022 Neural MMO Challenge: A Massively Multiagent Competition with Specialization and Trade [41.639843908635875]
  The NeurIPS-2022 Neural MMO Challenge attracted 500 participants and received over 1,600 submissions. This year's competition runs on the latest v1.6 Neural MMO, which introduces new equipment, combat, trading, and a better scoring system. This paper summarizes the design and results of the challenge, explores the potential of this environment as a benchmark for learning methods, and presents some practical reinforcement learning approaches for complex tasks with sparse rewards.
  arXiv  Detail & Related papers  (2023-11-07T04:14:45Z)
• TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play [19.98100026335148]
  TiZero is a self-evolving, multi-agent system that learns from scratch. It outperforms previous systems by a large margin on the Google Research Football environment.
  arXiv  Detail & Related papers  (2023-02-15T08:19:18Z)
• MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge [70.47759528596711]
  We introduce MineDojo, a new framework built on the popular Minecraft game. We propose a novel agent learning algorithm that leverages large pre-trained video-language models as a learned reward function. Our agent is able to solve a variety of open-ended tasks specified in free-form language without any manually designed dense shaping reward.
  arXiv  Detail & Related papers  (2022-06-17T15:53:05Z)
• The Neural MMO Platform for Massively Multiagent Research [49.51549968445566]
  Neural MMO is a research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems. We present Neural MMO as free and open source software with active support, ongoing development, documentation, and additional training, logging, and visualization tools.
  arXiv  Detail & Related papers  (2021-10-14T17:54:49Z)
• Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks [48.5733173329785]
  We present Neural MMO, a massively multiagent game environment inspired by MMOs. We discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO.
  arXiv  Detail & Related papers  (2020-01-31T18:50:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.