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06/05/2022
Use-Case-Grounded Simulations for Explanation Evaluation
A growing body of research runs human subject evaluations to study wheth...
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05/25/2022
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning
Many recent breakthroughs in multi-agent reinforcement learning (MARL) r...
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02/17/2022
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
Reinforcement learning competitions advance the field by providing appro...
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02/17/2022
A Survey of Explainable Reinforcement Learning
Explainable reinforcement learning (XRL) is an emerging subfield of expl...
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07/05/2021
The MineRL BASALT Competition on Learning from Human Feedback
The last decade has seen a significant increase of interest in deep lear...
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06/07/2021
Towards robust and domain agnostic reinforcement learning competitions
Reinforcement learning competitions have formed the basis for standard r...
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02/25/2021
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods
Current work in explainable reinforcement learning generally produces po...
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01/26/2021
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
Although deep reinforcement learning has led to breakthroughs in many di...
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05/12/2020
Guaranteeing Reproducibility in Deep Learning Competitions
To encourage the development of methods with reproducible and robust tra...
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03/10/2020
The MineRL Competition on Sample-Efficient Reinforcement Learning Using Human Priors: A Retrospective
To facilitate research in the direction of sample-efficient reinforcemen...
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03/10/2020
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning
To facilitate research in the direction of sample-efficient reinforcemen...
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07/29/2019
MineRL: A Large-Scale Dataset of Minecraft Demonstrations
The sample inefficiency of standard deep reinforcement learning methods ...
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07/02/2019
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy
There is a growing desire in the field of reinforcement learning (and ma...
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05/28/2019
Generation of Policy-Level Explanations for Reinforcement Learning
Though reinforcement learning has greatly benefited from the incorporati...
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04/22/2019
The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors
Though deep reinforcement learning has led to breakthroughs in many diff...
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08/23/2017
Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates
In this paper, we show a phenomenon, which we named "super-convergence",...
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02/14/2017
Exploring loss function topology with cyclical learning rates
We present observations and discussion of previously unreported phenomen...
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11/02/2016
