571 citations · 817 across the 9 of their papers we have counts for
9 papers
Improving alignment of dialogue agents via targeted human judgements
Amelia Glaese, Nat McAleese, Maja Trębacz +31
We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines. We use reinforcement lear…
Proving Theorems using Incremental Learning and Hindsight Experience Replay
Eser Aygün, Laurent Orseau, Ankit Anand +5
Traditional automated theorem provers for first-order logic depend on speed-optimized search and many handcrafted heuristics that are designed to work best over a wide range of dom…
Training a First-Order Theorem Prover from Synthetic Data
Vlad Firoiu, Eser Aygun, Ankit Anand +6
A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models.…
Learning to Prove from Synthetic Theorems
Eser Aygün, Zafarali Ahmed, Ankit Anand +5
A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models.…
Automated curricula through setter-solver interactions
Sebastien Racaniere, Andrew K. Lampinen, Adam Santoro +3
Reinforcement learning algorithms use correlations between policies and rewards to improve agent performance. But in dynamic or sparsely rewarding environments these correlations a…
At Human Speed: Deep Reinforcement Learning with Action Delay
Vlad Firoiu, Tina Ju, Josh Tenenbaum
There has been a recent explosion in the capabilities of game-playing artificial intelligence. Many classes of tasks, from video games to motor control to board games, are now solv…
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Lasse Espeholt, Hubert Soyer, Remi Munos +9
In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters. A key challenge is to handle the increased amo…
Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning
Vlad Firoiu, William F. Whitney, Joshua B. Tenenbaum
There has been a recent explosion in the capabilities of game-playing artificial intelligence. Many classes of RL tasks, from Atari games to motor control to board games, are now s…
Automatic Inference for Inverting Software Simulators via Probabilistic Programming
Ardavan Saeedi, Vlad Firoiu, Vikash Mansinghka
Models of complex systems are often formalized as sequential software simulators: computationally intensive programs that iteratively build up probable system configurations given…