Proving Theorems using Incremental Learning and Hindsight Experience Replay

no code implementations • 20 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

The Option Keyboard: Combining Skills in Reinforcement Learning

no code implementations • NeurIPS 2019 • André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan Hunt, Shibl Mourad, David Silver, Doina Precup

Building on this insight and on previous results on transfer learning, we show how to approximate options whose cumulants are linear combinations of the cumulants of known options.

Management reinforcement-learning +2

Learning to Prove from Synthetic Theorems

no code implementations • 19 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

Learning to cooperate: Emergent communication in multi-agent navigation

no code implementations • 2 Apr 2020 • Ivana Kajić, Eser Aygün, Doina Precup

Emergent communication in artificial agents has been studied to understand language evolution, as well as to develop artificial systems that learn to communicate with humans.