[R] NeurIPS 2019: The MineRL Competition for Sample-Efficient Reinforcement Learning
Starting June 1st, we are holding a NeurIPS 2019 competition on sample-efficient reinforcement learning using human priors.
Standard methods require months to years of game time to attain human performance in complex games such as Go and StarCraft. We want to catalyze research on reinforcement learning algorithms that don’t require hundreds of years of samples a day to solve complex tasks, lowering the computational barrier to entry.
In our competition, participants develop a system to obtain a diamond in Minecraft using only four days of training time.
To enable environment-sample efficiency we have created one of the largest imitation learning datasets *MineRL-v0* with over 60 million frames of recorded human player data. Our dataset includes a set of tasks which highlights sparse rewards and hierarchical policies.
To improve the experience for competition participants, we have developed our own Minecraft Gym environment *MineRLEnv* on top of Malmo to support many new features, including synchronous ticking, pausing, and extremely fast stepping (1000 FPS with head!)
This isn’t your traditional RL competition; to ensure real progress is made on sample-efficiency, *we train and evaluate your models from scratch.* (A huge thanks to Microsoft Research for sponsoring the compute needed to pull this competition off!) Here’s how it works
Among those teams who make it the furthest, the top 3 will be awarded GPUs from NVIDIA and more prizes from additional sponsors to come! All winners of the competition will be given travel grants so that they can attend the workshop at NeurIPS. We’ll also be providing travel grants and computation grants/scholarships for underrepresented groups at NeurIPS, see http://minerl.io/competition
A huge thank you to our partner Preferred Networks who will be preparing some baselines for the competition in the next coming months!
The contest will run from June 1st to October 25th. Here’s a full schedule!
The organizing team consists of:
The advisory committee consists of: