Search Results for author: Jonas Degrave

Found 10 papers, 5 papers with code

Local Search for Policy Iteration in Continuous Control

no code implementations12 Oct 2020 Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz, Josh Merel, Arunkumar Byravan, Abbas Abdolmaleki, Jackie Kay, Jonas Degrave, Julian Schrittwieser, Yuval Tassa, Jonas Buchli, Dan Belov, Martin Riedmiller

We demonstrate that additional computation spent on model-based policy improvement during learning can improve data efficiency, and confirm that model-based policy improvement during action selection can also be beneficial.

Continuous Control

Exchangeable Models in Meta Reinforcement Learning

1 code implementation ICML Workshop LifelongML 2020 Iryna Korshunova, Jonas Degrave, Joni Dambre, Arthur Gretton, Ferenc Huszar

One recent approach to meta reinforcement learning (meta-RL) is to integrate models for task inference with models for control.

Meta Reinforcement Learning

Quinoa: a Q-function You Infer Normalized Over Actions

no code implementations5 Nov 2019 Jonas Degrave, Abbas Abdolmaleki, Jost Tobias Springenberg, Nicolas Heess, Martin Riedmiller

We present an algorithm for learning an approximate action-value soft Q-function in the relative entropy regularised reinforcement learning setting, for which an optimal improved policy can be recovered in closed form.

Normalising Flows

Relative Entropy Regularized Policy Iteration

1 code implementation5 Dec 2018 Abbas Abdolmaleki, Jost Tobias Springenberg, Jonas Degrave, Steven Bohez, Yuval Tassa, Dan Belov, Nicolas Heess, Martin Riedmiller

Our algorithm draws on connections to existing literature on black-box optimization and 'RL as an inference' and it can be seen either as an extension of the Maximum a Posteriori Policy Optimisation algorithm (MPO) [Abdolmaleki et al., 2018a], or as an extension of Trust Region Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) [Abdolmaleki et al., 2017b; Hansen et al., 1997] to a policy iteration scheme.

Continuous Control OpenAI Gym

BRUNO: A Deep Recurrent Model for Exchangeable Data

3 code implementations NeurIPS 2018 Iryna Korshunova, Jonas Degrave, Ferenc Huszár, Yarin Gal, Arthur Gretton, Joni Dambre

We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations.

Anomaly Detection Bayesian Inference +2

Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural Networks

2 code implementations25 Jul 2017 Fréderic Godin, Jonas Degrave, Joni Dambre, Wesley De Neve

A DReLU, which comes with an unbounded positive and negative image, can be used as a drop-in replacement for a tanh activation function in the recurrent step of Quasi-Recurrent Neural Networks (QRNNs) (Bradbury et al. (2017)).

Language Modelling Sentiment Analysis

A Differentiable Physics Engine for Deep Learning in Robotics

no code implementations5 Nov 2016 Jonas Degrave, Michiel Hermans, Joni Dambre, Francis wyffels

Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent.

Q-Learning

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