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09/22/2022
A Generalist Neural Algorithmic Learner
The cornerstone of neural algorithmic reasoning is the ability to solve ...
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03/22/2022
Insights From the NeurIPS 2021 NetHack Challenge
In this report, we summarize the takeaways from the first NeurIPS 2021 N...
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01/31/2022
You May Not Need Ratio Clipping in PPO
Proximal Policy Optimization (PPO) methods learn a policy by iteratively...
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01/11/2022
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
Recent multi-task learning research argues against unitary scalarization...
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09/27/2021
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
The progress in deep reinforcement learning (RL) is heavily driven by th...
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03/01/2021
Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing
Recent research has shown that Graph Neural Networks (GNNs) can learn po...
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10/05/2020
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control
Multitask Reinforcement Learning is a promising way to obtain models wit...
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09/27/2019
Deep Coordination Graphs
This paper introduces the deep coordination graph (DCG) for collaborativ...
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09/26/2019
Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning
We present GQSAT, a branching heuristic in a Boolean SAT solver trained ...
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02/18/2019
Fast Efficient Hyperparameter Tuning for Policy Gradients
The performance of policy gradient methods is sensitive to hyperparamete...
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11/08/2018
Learning from Demonstration in the Wild
Learning from demonstration (LfD) is useful in settings where hand-codin...
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10/08/2018
CAML: Fast Context Adaptation via Meta-Learning
We propose CAML, a meta-learning method for fast adaptation that partiti...
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05/31/2017
The Atari Grand Challenge Dataset
Recent progress in Reinforcement Learning (RL), fueled by its combinatio...
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05/12/2017