Timezone: »

 
Diamond: A MineRL Competition on Training Sample-Efficient Agents + Q&A
William Guss · Alara Dirik · Byron Galbraith · Brandon Houghton · Anssi Kanervisto · Noboru Kuno · Stephanie Milani · Sharada Mohanty · Karolis Ramanauskas · Ruslan Salakhutdinov · Rohin Shah · Nicholay Topin · Steven Wang · Cody Wild

Tue Dec 07 11:45 AM -- 12:05 PM (PST) @
in Competition Track Day 1: Overviews + Breakout Sessions »
Event URL: https://www.aicrowd.com/challenges/neurips-2021-minerl-diamond-competition »

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. The competition environment features sparse-rewards, long-term planning, vision and sub-task hierarchies. To ensure that truly sample-efficient are developed, organizers re-train submitted systems on a fixed cloud-computing environment for a limited number of samples (4 days or 8 million samples). To ease the entry to machine learning research, the competition features two tracks: introduction, which allows agents developed using any method ranging from end-to-end machine learning solutions to programmatic approaches; and research, which requires participants develop novel imitation and reinforcement learning algorithms to solve this difficult sample-limited task.

Author Information

William Guss (Carnegie Mellon University)
Alara Dirik (Boğaziçi University)
Byron Galbraith (Talla)

Byron Galbraith is the CTO of Seva, where he works to translate the latest advancements in machine learning and natural language processing to build AI-powered conversational agents. Byron has a PhD in Cognitive and Neural Systems from Boston University and an MS in Bioinformatics from Marquette University. His research expertise includes brain-computer interfaces, neuromorphic robotics, spiking neural networks, high-performance computing, and natural language processing. Byron has also held several software engineering roles including back-end system engineer, full stack web developer, office automation consultant, and game engine developer at companies ranging in size from a two-person startup to a multi-national enterprise.

Brandon Houghton (OpenAI)
Anssi Kanervisto (University of Eastern Finland)

3rd year Ph.D student, with work focusing on video games and use of them in deep reinforcement learning research. Occasional work on speaker recognition and spoof detection.

Noboru Kuno (Microsoft)

Sean Kuno is a Senior Research Program Manager of Microsoft Research Outreach. He is based in Redmond U.S.S. and he is a member of Artificial Intelligence Outreach team. Kuno leads the ideation, design and launch of community programs for AI projects such as Project Malmo, working in partnership with universities and government agencies worldwide. Kuno joined Microsoft Research Asia in 2009 as a University Relations Manager in Japan. Before he joined Microsoft, he worked for the Japan Science and Technology Agency (JST), the second largest funding agency in Japan, where he had more than four years’ experience of project funding, program management and program evaluation and promotion of basic science research projects and academic exchange events. Before JST, he worked as a manager of marketing and product & business development in the cable and satellite industry in Japan. He received a bachelor degree (1996) and a master’s degree (1998) in Quantum Engineering and Systems Science from the Graduate School of Engineering, the University of Tokyo.

Stephanie Milani (Carnegie Mellon University)
Sharada Mohanty (AIcrowd SA)
Karolis Ramanauskas (-)
Karolis Ramanauskas

PhD Student in Reinforcement Learning

Ruslan Salakhutdinov (Carnegie Mellon University)
Rohin Shah (DeepMind)

Rohin is a Research Scientist on the technical AGI safety team at DeepMind. He completed his PhD at the Center for Human-Compatible AI at UC Berkeley, where he worked on building AI systems that can learn to assist a human user, even if they don't initially know what the user wants. He is particularly interested in big picture questions about artificial intelligence. What techniques will we use to build human-level AI systems? How will their deployment affect the world? What can we do to make this deployment go better? He writes up summaries and thoughts about recent work tackling these questions in the Alignment Newsletter.

Nicholay Topin (Carnegie Mellon University)
Steven Wang (UC Berkeley)
Cody Wild (Google Research)

More from the Same Authors