Search Results for author: Vlad Firoiu

Found 9 papers, 2 papers with code

Proving Theorems using Incremental Learning and Hindsight Experience Replay

no code implementations20 Dec 2021 Eser Aygün, Laurent Orseau, Ankit Anand, Xavier Glorot, Vlad Firoiu, Lei M. Zhang, Doina Precup, Shibl Mourad

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 domains.

Automated Theorem Proving Incremental Learning

Training a First-Order Theorem Prover from Synthetic Data

no code implementations5 Mar 2021 Vlad Firoiu, Eser Aygun, Ankit Anand, Zafarali Ahmed, Xavier Glorot, Laurent Orseau, Lei Zhang, Doina Precup, Shibl Mourad

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 Theorem Proving

Learning to Prove from Synthetic Theorems

no code implementations19 Jun 2020 Eser Aygün, Zafarali Ahmed, Ankit Anand, Vlad Firoiu, Xavier Glorot, Laurent Orseau, Doina Precup, Shibl Mourad

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 Theorem Proving

Automated curriculum generation through setter-solver interactions

no code implementations ICLR 2020 Sebastien Racaniere, Andrew Lampinen, Adam Santoro, David Reichert, Vlad Firoiu, Timothy Lillicrap

We demonstrate the success of our approach in rich but sparsely rewarding 2D and 3D environments, where an agent is tasked to achieve a single goal selected from a set of possible goals that varies between episodes, and identify challenges for future work.

Automated curricula through setter-solver interactions

no code implementations27 Sep 2019 Sebastien Racaniere, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap

We demonstrate the success of our approach in rich but sparsely rewarding 2D and 3D environments, where an agent is tasked to achieve a single goal selected from a set of possible goals that varies between episodes, and identify challenges for future work.

At Human Speed: Deep Reinforcement Learning with Action Delay

no code implementations16 Oct 2018 Vlad Firoiu, Tina Ju, Josh Tenenbaum

There has been a recent explosion in the capabilities of game-playing artificial intelligence.

Board Games reinforcement-learning

Automatic Inference for Inverting Software Simulators via Probabilistic Programming

no code implementations31 May 2015 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 parameters and initial conditions.

Probabilistic Programming

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