Search Results for author: Sebastien Racaniere

Found 8 papers, 3 papers with code

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.

Reinforcement Learning

Differentiable Game Mechanics

1 code implementation13 May 2019 Alistair Letcher, David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel

The decomposition motivates Symplectic Gradient Adjustment (SGA), a new algorithm for finding stable fixed points in differentiable games.

Towards a Definition of Disentangled Representations

1 code implementation5 Dec 2018 Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende, Alexander Lerchner

Here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world.

Representation Learning

Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search

no code implementations ICLR 2019 Lars Buesing, Theophane Weber, Yori Zwols, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau, Nicolas Heess

In contrast to off-policy algorithms based on Importance Sampling which re-weight data, CF-GPS leverages a model to explicitly consider alternative outcomes, allowing the algorithm to make better use of experience data.

counterfactual Reinforcement Learning

The Mechanics of n-Player Differentiable Games

1 code implementation ICML 2018 David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel

The first is related to potential games, which reduce to gradient descent on an implicit function; the second relates to Hamiltonian games, a new class of games that obey a conservation law, akin to conservation laws in classical mechanical systems.

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