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Lux AI Challenge Season 2 NeurIPS Edition

Stone Tao · Qimai Li · Yuhao Jiang · JIAXIN CHEN · Xiaolong Zhu · Bovard Doerschuk-Tiberi · Isabelle Pan · Addison Howard

Room 355


The proposed challenge is a large-scale multi-agent environment with novel complex dynamics, featuring long-horizon planning, perfect information, and more. The challenge uniquely presents an opportunity to investigate problems at a large-scale in two forms, large-scale RL training via GPU optimized environments powered by Jax, as well as large populations of controllable units in the environments. The Lux AI Challenge Season 2 NeurIPS Edition presents a benchmark to test the scaling capabilities of solutions such as RL on environement settings of increasing scale and complexity. Participants can easily get started using any number of strong rule-based, RL, and/or imitation learning (IL) baselines. They are also given access to more than a billion frames of "play" data from the previous iteration of the competition on the small scale version of the environment previously hosted on Kaggle. Participants can submit their agents to compete against other submitted agents on a online leaderboard ranked by a Trueskill ranking system.

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