mlsanityData to 14 Oct 2026

Results of the NeurIPS 2023 Neural MMO Competition on Multi-task Reinforcement Learning

arXiv 2508.12524 ↗First posted 17 Aug 2025cs.LGcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

We present the results of the NeurIPS 2023 Neural MMO Competition, which attracted over 200 participants and submissions. Participants trained goal-conditional policies that generalize to tasks, maps, and opponents never seen during training. The top solution achieved a score 4x higher than our baseline within 8 hours of training on a single 4090 GPU. We open-source everything relating to Neural MMO and the competition under the MIT license, including the policy weights and training code for our baseline and for the top submissions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 6 references that are in the map. First posted on arXiv 17 Aug 2025.

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4 of 6 references.

References · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2019Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agentscited by 44
  3. 2019The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priorscited by 26
  4. 2021Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmarkcited by 23

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  3. 2023Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
  4. 2023NetHack is Hard to Hack
  5. 2022Retrospective on the 2021 BASALT Competition on Learning from Human Feedback
  6. 2022Dungeons and Data: A Large-Scale NetHack Dataset
  7. 2020Sample Efficient Training in Multi-Agent Adversarial Games with Limited Teammate Communication
  8. 2022Scaling Laws for a Multi-Agent Reinforcement Learning Model