OpenAI co-organizes the MineRL 2020 Competition to advance sample-efficient reinforcement learning algorithms that leverage human demonstrations. Participants compete to obtain a diamond in Minecraft using only 8 million simulator samples and 4 days of single-GPU training, with access to a 60+ million frame human demonstration dataset.
We’re excited to announce that OpenAI is co-organizing two NeurIPS 2020 competitions with AIcrowd, Carnegie Mellon University, and DeepMind, using Procgen Benchmark and MineRL.
# Procgen and MineRL Competitions
Source: [https://openai.com/index/procgen-minerl-competitions/](https://openai.com/index/procgen-minerl-competitions/)
To further catalyze research in this direction, we are co\-organizing the[MineRL 2020 Competition\(opens in a new window\)](https://www.aicrowd.com/challenges/neurips-2020-minerl-challenge)which aims to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments\. To that end, participants will compete to develop systems which can obtain a diamond in[Minecraft\(opens in a new window\)](http://minercraft.net/)from raw pixels using only 8,000,000 samples from the[MineRL simulator\(opens in a new window\)](http://minerl.io/docs)and 4 days of training on a single GPU machine\. Participants will be provided the MineRL\-v0 dataset \([website\(opens in a new window\)](http://minerl.io/dataset/),[paper\(opens in a new window\)](https://arxiv.org/abs/1907.13440)\), a large\-scale collection of over 60 million frames of human demonstrations, enabling them to utilize expert trajectories to minimize their algorithm’s interactions with the Minecraft simulator\.
This competition is a follow\-up to the[MineRL 2019 Competition\(opens in a new window\)](https://www.aicrowd.com/challenges/neurips-2019-minerl-competition)in which the[top team’s agent\(opens in a new window\)](https://arxiv.org/pdf/1912.08664v2.pdf)was able to[obtain an iron pickaxe\(opens in a new window\)](https://www.youtube.com/watch?v=GHo8B4JMC38&feature=youtu.be)\(the penultimate goal of the competition\) under this extremely limited compute and simulator\-interaction budget\. Put in perspective, state\-of\-the\-art standard reinforcement learning systems require hundreds of millions of environment interactions on large multi\-GPU systems to achieve the same goal\. This year, we anticipate competitors will push the state\-of\-the\-art even further\.
To guarantee that competitors develop truly sample efficient algorithms, the MineRL competition organizers train the top team’s final round models from scratch with strict constraints on the hardware, compute, and simulator\-interaction available\. The MineRL 2020 Competition also features a novel measure to avoid hand engineering features and overfitting solutions to the domain\. More details on the competition structure can be found[here\(opens in a new window\)](https://www.aicrowd.com/challenges/neurips-2020-minerl-challenge)\.
OpenAI introduced Video PreTraining (VPT), a semi-supervised method that trains neural networks to play Minecraft by learning from 70,000 hours of unlabeled human gameplay video combined with a small labeled dataset. The model learns complex sequential tasks using the native human interface (keyboard and mouse) and demonstrates capabilities like crafting diamond tools and pillar jumping, representing progress toward general computer-using agents.
OpenAI introduces Procgen Benchmark, a suite of procedurally generated environments designed to evaluate generalization in reinforcement learning agents across diverse tasks, addressing overfitting issues in traditional benchmarks like Atari.
OpenAI launched the Retro Contest, a transfer learning competition that evaluates RL algorithms on unseen video game levels from classic SEGA Genesis games, running from April to June 2018. The contest uses Gym Retro platform and includes baseline implementations and a technical benchmark paper demonstrating that current RL algorithms significantly underperform humans on generalization tasks.
Google DeepMind's Project Genie is a unified world model that generates and interacts with diverse video games by treating them as conditional video prediction tasks.
OpenAI releases OpenAI Gym, a public beta toolkit for developing and comparing reinforcement learning algorithms with a growing suite of environments and a platform for reproducible research. The toolkit aims to standardize RL benchmarks and address the lack of diverse, easy-to-use environments for the research community.