activity
20162022
most citedThe MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors

14 citations · 46 across the 10 of their papers we have counts for

collaborators

17 papers

cs.LG20221 cited

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…

cs.LG20214 cited

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…

cs.AI2021

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…

cs.LG20211 cited

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…

cs.AI20216 cited

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…

cs.LG20217 cited

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…