Results of the NeurIPS 2023 Neural MMO Competition on Multi-task Reinforcement Learning
This work addresses the challenge of cross-task and cross-environment generalization in multi-task reinforcement learning. We propose a unified training framework based on goal-conditioned policies and instantiate it in Neural MMO—a high-complexity, open-world multi-agent environment. Methodologically, we decouple goal representation from the policy network and jointly optimize generalization across three previously unseen dimensions: tasks, maps, and opponent policies. Experiments demonstrate that our best configuration achieves four times the baseline score within eight hours on a single GPU, while significantly improving zero-shot transfer performance. To foster reproducibility and community advancement, we open-source all code and model weights; the project has already attracted over 200 researchers. This work establishes a rigorous, reproducible benchmark and a principled technical paradigm for generalization in open-world multi-agent reinforcement learning.