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Learning to Prove from Synthetic Theorems
A major challenge in applying machine learning to automated theorem prov...
06/19/2020 ∙ by Eser Aygün, et al. ∙
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Automated curricula through setter-solver interactions
Reinforcement learning algorithms use correlations between policies and ...
09/27/2019 ∙ by Sebastien Racanière, et al. ∙
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At Human Speed: Deep Reinforcement Learning with Action Delay
There has been a recent explosion in the capabilities of game-playing ar...
10/16/2018 ∙ by Vlad Firoiu, et al. ∙
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IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
In this work we aim to solve a large collection of tasks using a single ...
02/05/2018 ∙ by Lasse Espeholt, et al. ∙
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Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning
There has been a recent explosion in the capabilities of game-playing ar...
02/21/2017 ∙ by Vlad Firoiu, et al. ∙
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Automatic Inference for Inverting Software Simulators via Probabilistic Programming
Models of complex systems are often formalized as sequential software si...
05/31/2015 ∙ by Ardavan Saeedi, et al. ∙
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