52 citations · 215 across the 22 of their papers we have counts for
23 papers
Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO
Yangkun Chen, Joseph Suarez, Junjie Zhang +18
We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-a…
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Cinematic Demixing Track
Stefan Uhlich, Giorgio Fabbro, Masato Hirano +14
This paper summarizes the cinematic demixing (CDX) track of the Sound Demixing Challenge 2023 (SDX'23). We provide a comprehensive summary of the challenge setup, detailing the str…
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
Giorgio Fabbro, Stefan Uhlich, Chieh-Hsin Lai +24
This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge (SDX'23). We provide a summary of the challenge setup and introduce the task of robust music so…
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas +27
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 20…
Insights From the NeurIPS 2021 NetHack Challenge
Eric Hambro, Sharada Mohanty, Dmitrii Babaev +26
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge. Participants were tasked with developing a program or agent that can win (i.e., 'ascend' i…
The MineRL BASALT Competition on Learning from Human Feedback
Rohin Shah, Cody Wild, Steven H. Wang +10
The last decade has seen a significant increase of interest in deep learning research, with many public successes that have demonstrated its potential. As such, these systems are n…
The Food Recognition Benchmark: Using DeepLearning to Recognize Food on Images
Sharada Prasanna Mohanty, Gaurav Singhal, Eric Antoine Scuccimarra +4
The automatic recognition of food on images has numerous interesting applications, including nutritional tracking in medical cohorts. The problem has received significant research…
Towards robust and domain agnostic reinforcement learning competitions
William Hebgen Guss, Stephanie Milani, Nicholay Topin +26
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Desp…
The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
Jennifer J. Sun, Tomomi Karigo, Dipam Chakraborty +8
Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social…
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
Florian Laurent, Manuel Schneider, Christian Scheller +24
The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling trips in traffic networks and th…
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon +20
The NeurIPS 2020 Procgen Competition was designed as a centralized benchmark with clearly defined tasks for measuring Sample Efficiency and Generalization in Reinforcement Learning…
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
William H. Guss, Mario Ynocente Castro, Sam Devlin +12
Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrink…
Flatland-RL : Multi-Agent Reinforcement Learning on Trains
Sharada Mohanty, Erik Nygren, Florian Laurent +11
Efficient automated scheduling of trains remains a major challenge for modern railway systems. The underlying vehicle rescheduling problem (VRSP) has been a major focus of Operatio…
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning
Stephanie Milani, Nicholay Topin, Brandon Houghton +5
To facilitate research in the direction of sample efficient reinforcement learning, we held the MineRL Competition on Sample Efficient Reinforcement Learning Using Human Priors at…
Investment Ranking Challenge: Identifying the best performing stocks based on their semi-annual returns
Shanka Subhra Mondal, Sharada Prasanna Mohanty, Benjamin Harlander +7
In the IEEE Investment ranking challenge 2018, participants were asked to build a model which would identify the best performing stocks based on their returns over a forward six mo…
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
William H. Guss, Cayden Codel, Katja Hofmann +9
Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinf…
Artificial Intelligence for Prosthetics - challenge solutions
Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty +47
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a giv…
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
Diego Perez-Liebana, Katja Hofmann, Sharada Prasanna Mohanty +5
Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and differ…
Adversarial Vision Challenge
Wieland Brendel, Jonas Rauber, Alexey Kurakin +5
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks…
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong +26
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle c…
Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning
Łukasz Kidziński, Sharada P. Mohanty, Carmichael Ong +5
Synthesizing physiologically-accurate human movement in a variety of conditions can help practitioners plan surgeries, design experiments, or prototype assistive devices in simulat…
Learning to Recognize Musical Genre from Audio
Michaël Defferrard, Sharada P. Mohanty, Sean F. Carroll +1
We here summarize our experience running a challenge with open data for musical genre recognition. Those notes motivate the task and the challenge design, show some statistics abou…
Using Deep Learning for Image-Based Plant Disease Detection
Sharada Prasanna Mohanty, David Hughes, Marcel Salathe
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The…