![]() The competition environment features sparse-rewards, long-term planning, vision and sub-task hierarchies. In the third MineRL Diamond competition, participants continue to develop algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve a complex task in Minecraft. William Guss (OpenAI Inc., Carnegie Mellon University), Alara Dirik (Bogazici University), Byron Galbraith (Talla), Brandon Houghton (OpenAI Inc.), Anssi Kanervisto (University of Eastern Finland), Noboru Kuno (Microsoft Research), Stephanie Milani (Carnegie Mellon University), Sharada Mohanty (AICrowd), Karolis Ramanauskas ( N/A ), Ruslan Salakhutdinov (Carnegie Mellon University), Rohin Shah (UC Berkeley ), Nicholay Topin (Carnegie Mellon University), Steven Wang (UC Berkeley ), Cody Wild (UC Berkeley). ![]() We will provide Azure cloud compute credit to participants with promising ideas without necessary infrastructure to develop their submissions.ĭiamond: A MineRL Competition on Training Sample-Efficient Agents The anticipated impact is an understanding of the ideas that apply at a billion-point scale, bridging communities that work on ANNS problems, and a platform for newer researchers to contribute and develop this relatively new research area. We will use two recent indexing algorithms, DiskANN and FAISS, as baselines for tracks T1 and T2. There are three tracks depending on hardware settings: (T1) limited memory (T2) limited main memory + SSD (T3) any hardware configuration including accelerators and custom silicon. The competition uses six representative billion-scale datasets - many newly released for this competition - with their associated accuracy metrics. This competition aims at pushing the scale to out-of-memory billion-scale datasets and other hardware configurations that are realistic in many current applications. Thanks to efforts like, the state of the art for ANNS on million-scale datasets is quite clear. ANNS algorithms optimize a tradeoff between search speed, memory usage and accuracy with respect to an exact sequential search. Harsha Vardhan Simhadri (Microsoft Research India), George Williams (GSI Technology), Martin Aumüller (IT University of Copenhagen), Artem Babenko(Yandex), Dmitry Baranchuk (Yandex), Qi Chen (Microsoft Research Asia), Matthijs Douze (Facebook AI Research), Ravishankar Krishnaswamy (Microsoft Research India, IIT Madras), Gopal Srinivasa (Microsoft Research India), Suhas Jayaram Subramanya (Carnegie Mellon University), Jingdong Wang (Microsoft Research Asia).Īpproximate Nearest Neighbor Search (ANNS) amounts to finding nearby points to a given query point in a high-dimensional vector space. Submitted agents will be evaluated based on how well they complete the tasks, as judged by humans given the same description of the tasks.īillion-Scale Approximate Nearest Neighbor Search Challenge We expect typical solutions will use imitation learning, or learning from comparisons. Participants will train agents for these tasks using their preferred methods. ![]() We provide tasks consisting of a simple English language description alongside a Gym environment, without any associated reward function, but with expert demos. The Benchmark for Agents that Solve Almost-Lifelike Tasks (BASALT) competition aims to promote research in the area of learning from human feedback in order to enable agents that can pursue tasks that do not have crisp, easily defined reward functions. Wang (UC Berkeley), Neel Alex (UC Berkeley), Brandon Houghton (OpenAI), William Guss (OpenAI), Sharada Mohanty (AIcrowd), Stephanie Milani (Carnegie Mellon University), Nicholay Topin (Carnegie Mellon University), Pieter Abbeel (UC Berkeley), Stuart Russell (UC Berkeley), Anca Dragan (UC Berkeley). Rohin Shah, (UC Berkeley), Cody Wild (UC Berkeley), Steven H. Please note that all information is subject to change, please visit the competition websites regularly and contact the organizers of each competition directly for more information.īASALT: A MineRL Competition on Solving Human-Judged Tasks Regular competitions take place before the NeurIPS, whereas live competitions will have their final phase during the competition session are listed in alphabetical order, all prizes are tentative and depend solely on the organizing team of each competition and the corresponding sponsors. Below you will find a brief summary of accepted competitions NeurIPS 2021.
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