Timezone: »
Project Malmo is an open source artificial intelligence (AI) experimentation platform, designed to support fundamental research. Rapid progress in many areas of AI research requires experimentation in interactive settings (agents interact with an environment) that are complex, diverse, dynamic and open, and that provide increasingly more difficult challenges as technology progresses. Project Malmo achieves such flexibility by building on top of Minecraft, a popular computer game with millions of players. The game Minecraft is particularly appealing due to its open ended nature, collaboration with other players, and creativity in game-play.
In this demo, we show the capabilities of the Project Malmo platform and the kind of research they enable. These range from 3D navigation tasks to interactive scenarios where agents converse, compete or collaborate with one another or humans to achieve a goal. The platform is designed to foster collaboration and openness. The result is a cross-platform (Windows, MacOS, Linux), cross-language (e.g., C/C++, Java, C#, Python, Lua) experimentation environment that uses standard data formats to easily exchange tasks and recorded data. Recently, the platform was publicly released as open source software.
Author Information
Katja Hofmann (Microsoft Research)
Dr. Katja Hofmann is a Principal Researcher at the [Game Intelligence](http://aka.ms/gameintelligence/) group at [Microsoft Research Cambridge, UK](https://www.microsoft.com/en-us/research/lab/microsoft-research-cambridge/). There, she leads a research team that focuses on reinforcement learning with applications in modern video games. She and her team strongly believe that modern video games will drive a transformation of how we interact with AI technology. One of the projects developed by her team is [Project Malmo](https://www.microsoft.com/en-us/research/project/project-malmo/), which uses the popular game Minecraft as an experimentation platform for developing intelligent technology. Katja's long-term goal is to develop AI systems that learn to collaborate with people, to empower their users and help solve complex real-world problems. Before joining Microsoft Research, Katja completed her PhD in Computer Science as part of the [ILPS](https://ilps.science.uva.nl/) group at the [University of Amsterdam](https://www.uva.nl/en). She worked with Maarten de Rijke and Shimon Whiteson on interactive machine learning algorithms for search engines.
Matthew A Johnson (Microsoft Research)
Fernando Diaz (Google)
Fernando Diaz is a research scientist at Google Brain Montréal. His research focuses on the design of information access systems, including search engines, music recommendation services and crisis response platforms is particularly interested in understanding and addressing the societal implications of artificial intelligence more generally. Previously, Fernando was the assistant managing director of Microsoft Research Montréal and a director of research at Spotify, where he helped establish its research organization on recommendation, search, and personalization. Fernando’s work has received awards at SIGIR, WSDM, ISCRAM, and ECIR. He is the recipient of the 2017 British Computer Society Karen Spärck Jones Award. Fernando has co-organized workshops and tutorials at SIGIR, WSDM, and WWW. He has also co-organized several NIST TREC initiatives, WSDM (2013), Strategic Workshop on Information Retrieval (2018), FAT* (2019), SIGIR (2021), and the CIFAR Workshop on Artificial Intelligence and the Curation of Culture (2019)
Alekh Agarwal (Google Research)
Tim Hutton (Microsoft)
David Bignell (Microsoft)
Evelyne Viegas (Microsoft Research)
More from the Same Authors
-
2021 : Artsheets for Art Datasets »
Ramya Srinivasan · Emily Denton · Jordan Famularo · Negar Rostamzadeh · Fernando Diaz · Beth Coleman -
2022 : Exposure Fairness in Music Recommendation »
Rebecca Salganik · Fernando Diaz · Golnoosh Farnadi -
2022 : Provable Benefits of Representational Transfer in Reinforcement Learning »
Alekh Agarwal · Yuda Song · Kaiwen Wang · Mengdi Wang · Wen Sun · Xuezhou Zhang -
2022 : Striving for data-model efficiency: Identifying data externalities on group performance »
Esther Rolf · Ben Packer · Alex Beutel · Fernando Diaz -
2022 : Contextual Squeeze-and-Excitation »
Massimiliano Patacchiola · John Bronskill · Aliaksandra Shysheya · Katja Hofmann · Sebastian Nowozin · Richard Turner -
2022 : Imitating Human Behaviour with Diffusion Models »
Tim Pearce · Tabish Rashid · Anssi Kanervisto · David Bignell · Mingfei Sun · Raluca Georgescu · Sergio Valcarcel Macua · Shan Zheng Tan · Ida Momennejad · Katja Hofmann · Sam Devlin -
2022 Workshop: Cultures of AI and AI for Culture »
Alex Hanna · Rida Qadri · Fernando Diaz · Nick Seaver · Morgan Scheuerman -
2022 : Panel »
Hannah Korevaar · Manish Raghavan · Ashudeep Singh · Fernando Diaz · Chloé Bakalar · Alana Shine -
2022 Poster: Uni[MASK]: Unified Inference in Sequential Decision Problems »
Micah Carroll · Orr Paradise · Jessy Lin · Raluca Georgescu · Mingfei Sun · David Bignell · Stephanie Milani · Katja Hofmann · Matthew Hausknecht · Anca Dragan · Sam Devlin -
2022 Poster: On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL »
Jinglin Chen · Aditya Modi · Akshay Krishnamurthy · Nan Jiang · Alekh Agarwal -
2022 Poster: Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity »
Alekh Agarwal · Tong Zhang -
2022 Poster: Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification »
Massimiliano Patacchiola · John Bronskill · Aliaksandra Shysheya · Katja Hofmann · Sebastian Nowozin · Richard Turner -
2022 : NeurIPS Competitions – Evolution and Opportunities »
Isabelle Guyon · Evelyne Viegas -
2021 : Towards RL applications in video games and with human users »
Katja Hofmann -
2021 : Methods:: Understanding Human-like Behavior in Video Game Navigation »
Evelyn Zuniga · Stephanie Milani · Katja Hofmann -
2021 Poster: Bellman-consistent Pessimism for Offline Reinforcement Learning »
Tengyang Xie · Ching-An Cheng · Nan Jiang · Paul Mineiro · Alekh Agarwal -
2021 : IGLU: Interactive Grounded Language Understanding in a Collaborative Environment + Q&A »
Julia Kiseleva · Ziming Li · Mohammad Aliannejadi · Maartje Anne ter Hoeve · Mikhail Burtsev · Alexey Skrynnik · Artem Zholus · Aleksandr Panov · Katja Hofmann · Kavya Srinet · arthur szlam · Michel Galley · Ahmed Awadallah -
2021 Oral: Bellman-consistent Pessimism for Offline Reinforcement Learning »
Tengyang Xie · Ching-An Cheng · Nan Jiang · Paul Mineiro · Alekh Agarwal -
2021 Poster: Grounding Spatio-Temporal Language with Transformers »
Tristan Karch · Laetitia Teodorescu · Katja Hofmann · Clément Moulin-Frier · Pierre-Yves Oudeyer -
2021 Poster: Memory Efficient Meta-Learning with Large Images »
John Bronskill · Daniela Massiceti · Massimiliano Patacchiola · Katja Hofmann · Sebastian Nowozin · Richard Turner -
2020 Workshop: Competition Track Saturday »
Hugo Jair Escalante · Katja Hofmann -
2020 Workshop: Algorithmic Fairness through the Lens of Causality and Interpretability »
Awa Dieng · Jessica Schrouff · Matt Kusner · Golnoosh Farnadi · Fernando Diaz -
2020 Workshop: Competition Track Friday »
Hugo Jair Escalante · Katja Hofmann -
2020 : Opening - Competition Track Session »
Katja Hofmann · Hugo Jair Escalante -
2020 : Keynote talk by Isabelle Guyon and Evelyne Viegas - "AI Competitions and the Science Behind Contests" »
Isabelle Guyon · Evelyne Viegas -
2020 Poster: Policy Improvement via Imitation of Multiple Oracles »
Ching-An Cheng · Andrey Kolobov · Alekh Agarwal -
2020 Spotlight: Policy Improvement via Imitation of Multiple Oracles »
Ching-An Cheng · Andrey Kolobov · Alekh Agarwal -
2020 Tutorial: (Track2) Beyond Accuracy: Grounding Evaluation Metrics for Human-Machine Learning Systems Q&A »
Praveen Chandar · Fernando Diaz · Brian St. Thomas -
2020 Poster: FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs »
Alekh Agarwal · Sham Kakade · Akshay Krishnamurthy · Wen Sun -
2020 Poster: PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning »
Alekh Agarwal · Mikael Henaff · Sham Kakade · Wen Sun -
2020 Oral: FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs »
Alekh Agarwal · Sham Kakade · Akshay Krishnamurthy · Wen Sun -
2020 Poster: Safe Reinforcement Learning via Curriculum Induction »
Matteo Turchetta · Andrey Kolobov · Shital Shah · Andreas Krause · Alekh Agarwal -
2020 Poster: Provably Good Batch Reinforcement Learning Without Great Exploration »
Yao Liu · Adith Swaminathan · Alekh Agarwal · Emma Brunskill -
2020 Spotlight: Safe Reinforcement Learning via Curriculum Induction »
Matteo Turchetta · Andrey Kolobov · Shital Shah · Andreas Krause · Alekh Agarwal -
2020 : Discussion Panel: Hugo Larochelle, Finale Doshi-Velez, Devi Parikh, Marc Deisenroth, Julien Mairal, Katja Hofmann, Phillip Isola, and Michael Bowling »
Hugo Larochelle · Finale Doshi-Velez · Marc Deisenroth · Devi Parikh · Julien Mairal · Katja Hofmann · Phillip Isola · Michael Bowling -
2020 Tutorial: (Track2) Beyond Accuracy: Grounding Evaluation Metrics for Human-Machine Learning Systems »
Praveen Chandar · Fernando Diaz · Brian St. Thomas -
2019 : Multi-Task Reinforcement Learning and Generalization »
Katja Hofmann -
2019 : The MineRL competition »
Misa Ogura · Joe Booth · Sophia Sun · Nicholay Topin · Brandon Houghton · William Guss · Stephanie Milani · Oriol Vinyals · Katja Hofmann · JIA KIM · Karolis Ramanauskas · Florian Laurent · Daichi Nishio · Anssi Kanervisto · Alexey Skrynnik · Artemij Amiranashvili · Christian Scheller · KAIXIN WANG · Yanick Schraner -
2019 : Open Space Topic “The Organization of Challenges for the Benefit of More Diverse Communities” »
Adrienne Mendrik · Isabelle Guyon · Wei-Wei Tu · Evelyne Viegas · Ming LI -
2019 Workshop: CiML 2019: Machine Learning Competitions for All »
Adrienne Mendrik · Wei-Wei Tu · Wei-Wei Tu · Isabelle Guyon · Evelyne Viegas · Ming LI -
2019 : Welcome and Opening Remarks »
Adrienne Mendrik · Wei-Wei Tu · Isabelle Guyon · Evelyne Viegas · Ming LI -
2019 Poster: Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck »
Maximilian Igl · Kamil Ciosek · Yingzhen Li · Sebastian Tschiatschek · Cheng Zhang · Sam Devlin · Katja Hofmann -
2019 Poster: Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting »
Aditya Grover · Jiaming Song · Ashish Kapoor · Kenneth Tran · Alekh Agarwal · Eric Horvitz · Stefano Ermon -
2019 Poster: Better Exploration with Optimistic Actor Critic »
Kamil Ciosek · Quan Vuong · Robert Loftin · Katja Hofmann -
2019 Spotlight: Better Exploration with Optimistic Actor Critic »
Kamil Ciosek · Quan Vuong · Robert Loftin · Katja Hofmann -
2019 Poster: Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning »
David Janz · Jiri Hron · Przemysław Mazur · Katja Hofmann · José Miguel Hernández-Lobato · Sebastian Tschiatschek -
2019 Tutorial: Reinforcement Learning: Past, Present, and Future Perspectives »
Katja Hofmann -
2018 : How Players Speak to an Intelligent Game Character Using Natural Language Messages »
Katja Hofmann -
2018 Workshop: CiML 2018 - Machine Learning competitions "in the wild": Playing in the real world or in real time »
Isabelle Guyon · Evelyne Viegas · Sergio Escalera · Jacob D Abernethy -
2018 : Morning Welcome - - Isabelle Guyon and Evelyne Viegas »
Evelyne Viegas -
2018 : AutoML3 - LifeLong ML with concept drift Challenge: Overview and award ceremony »
Hugo Jair Escalante · Isabelle Guyon · Daniel Silver · Evelyne Viegas · Wei-Wei Tu -
2018 Poster: On Oracle-Efficient PAC RL with Rich Observations »
Christoph Dann · Nan Jiang · Akshay Krishnamurthy · Alekh Agarwal · John Langford · Robert Schapire -
2018 Spotlight: On Oracle-Efficient PAC RL with Rich Observations »
Christoph Dann · Nan Jiang · Akshay Krishnamurthy · Alekh Agarwal · John Langford · Robert Schapire -
2017 Workshop: Machine Learning Challenges as a Research Tool »
Isabelle Guyon · Evelyne Viegas · Sergio Escalera · Jacob D Abernethy -
2017 Workshop: OPT 2017: Optimization for Machine Learning »
Suvrit Sra · Sashank J. Reddi · Alekh Agarwal · Benjamin Recht -
2017 : Panel: "How can we characterise the landscape of intelligent systems and locate human-like intelligence in it?" »
Josh Tenenbaum · Gary Marcus · Katja Hofmann -
2017 : Katja Hofmann: 'Video games and the road to collaborative AI' »
Katja Hofmann -
2017 Poster: Off-policy evaluation for slate recommendation »
Adith Swaminathan · Akshay Krishnamurthy · Alekh Agarwal · Miro Dudik · John Langford · Damien Jose · Imed Zitouni -
2017 Oral: Off-policy evaluation for slate recommendation »
Adith Swaminathan · Akshay Krishnamurthy · Alekh Agarwal · Miro Dudik · John Langford · Damien Jose · Imed Zitouni -
2016 Workshop: Challenges in Machine Learning: Gaming and Education »
Isabelle Guyon · Evelyne Viegas · Balázs Kégl · Ben Hamner · Sergio Escalera -
2016 : Welcome »
Evelyne Viegas -
2016 Poster: Efficient Second Order Online Learning by Sketching »
Haipeng Luo · Alekh Agarwal · Nicolò Cesa-Bianchi · John Langford -
2016 Poster: Contextual semibandits via supervised learning oracles »
Akshay Krishnamurthy · Alekh Agarwal · Miro Dudik -
2016 Poster: PAC Reinforcement Learning with Rich Observations »
Akshay Krishnamurthy · Alekh Agarwal · John Langford -
2015 Workshop: Challenges in Machine Learning (CiML 2015): "Open Innovation" and "Coopetitions" »
Isabelle Guyon · Evelyne Viegas · Ben Hamner · Balázs Kégl -
2015 Workshop: Optimization for Machine Learning (OPT2015) »
Suvrit Sra · Alekh Agarwal · Leon Bottou · Sashank J. Reddi -
2015 Poster: Efficient and Parsimonious Agnostic Active Learning »
Tzu-Kuo Huang · Alekh Agarwal · Daniel Hsu · John Langford · Robert Schapire -
2015 Spotlight: Efficient and Parsimonious Agnostic Active Learning »
Tzu-Kuo Huang · Alekh Agarwal · Daniel Hsu · John Langford · Robert Schapire -
2015 Poster: Efficient Non-greedy Optimization of Decision Trees »
Mohammad Norouzi · Maxwell Collins · Matthew A Johnson · David Fleet · Pushmeet Kohli -
2015 Demonstration: CodaLab Worksheets for Reproducible, Executable Papers »
Percy Liang · Evelyne Viegas -
2015 Poster: Fast Convergence of Regularized Learning in Games »
Vasilis Syrgkanis · Alekh Agarwal · Haipeng Luo · Robert Schapire -
2015 Oral: Fast Convergence of Regularized Learning in Games »
Vasilis Syrgkanis · Alekh Agarwal · Haipeng Luo · Robert Schapire -
2014 Workshop: Challenges in Machine Learning workshop (CiML 2014) »
Isabelle Guyon · Evelyne Viegas · Percy Liang · Olga Russakovsky · Rinat Sergeev · Gábor Melis · Michele Sebag · Gustavo Stolovitzky · Jaume Bacardit · Michael S Kim · Ben Hamner -
2014 Workshop: OPT2014: Optimization for Machine Learning »
Zaid Harchaoui · Suvrit Sra · Alekh Agarwal · Martin Jaggi · Miro Dudik · Aaditya Ramdas · Jean Lasserre · Yoshua Bengio · Amir Beck -
2014 Poster: Scalable Non-linear Learning with Adaptive Polynomial Expansions »
Alekh Agarwal · Alina Beygelzimer · Daniel Hsu · John Langford · Matus J Telgarsky -
2013 Workshop: Learning Faster From Easy Data »
Peter Grünwald · Wouter M Koolen · Sasha Rakhlin · Nati Srebro · Alekh Agarwal · Karthik Sridharan · Tim van Erven · Sebastien Bubeck -
2013 Workshop: NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms »
Isabelle Guyon · Leon Bottou · Bernhard Schölkopf · Alexander Statnikov · Evelyne Viegas · james m robins -
2013 Workshop: OPT2013: Optimization for Machine Learning »
Suvrit Sra · Alekh Agarwal -
2012 Workshop: Optimization for Machine Learning »
Suvrit Sra · Alekh Agarwal -
2012 Poster: Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions »
Alekh Agarwal · Sahand N Negahban · Martin J Wainwright -
2011 Workshop: Computational Trade-offs in Statistical Learning »
Alekh Agarwal · Sasha Rakhlin -
2011 Poster: Distributed Delayed Stochastic Optimization »
Alekh Agarwal · John Duchi -
2011 Poster: Stochastic convex optimization with bandit feedback »
Alekh Agarwal · Dean P Foster · Daniel Hsu · Sham M Kakade · Sasha Rakhlin -
2010 Workshop: Learning on Cores, Clusters, and Clouds »
Alekh Agarwal · Lawrence Cayton · Ofer Dekel · John Duchi · John Langford -
2010 Spotlight: Distributed Dual Averaging In Networks »
John Duchi · Alekh Agarwal · Martin J Wainwright -
2010 Poster: Distributed Dual Averaging In Networks »
John Duchi · Alekh Agarwal · Martin J Wainwright -
2010 Oral: Fast global convergence rates of gradient methods for high-dimensional statistical recovery »
Alekh Agarwal · Sahand N Negahban · Martin J Wainwright -
2010 Poster: Fast global convergence rates of gradient methods for high-dimensional statistical recovery »
Alekh Agarwal · Sahand N Negahban · Martin J Wainwright -
2009 Poster: Information-theoretic lower bounds on the oracle complexity of convex optimization »
Alekh Agarwal · Peter Bartlett · Pradeep Ravikumar · Martin J Wainwright -
2009 Spotlight: Information-theoretic lower bounds on the oracle complexity of convex optimization »
Alekh Agarwal · Peter Bartlett · Pradeep Ravikumar · Martin J Wainwright -
2007 Poster: An Analysis of Inference with the Universum »
Fabian H Sinz · Olivier Chapelle · Alekh Agarwal · Bernhard Schölkopf -
2007 Spotlight: An Analysis of Inference with the Universum »
Fabian H Sinz · Olivier Chapelle · Alekh Agarwal · Bernhard Schölkopf