Search Results for author: Steven Bohez

Found 12 papers, 3 papers with code

Explicit Pareto Front Optimization for Constrained Reinforcement Learning

no code implementations1 Jan 2021 Sandy Huang, Abbas Abdolmaleki, Philemon Brakel, Steven Bohez, Nicolas Heess, Martin Riedmiller, Raia Hadsell

We propose a framework that uses a multi-objective RL algorithm to find a Pareto front of policies that trades off between the reward and constraint(s), and simultaneously searches along this front for constraint-satisfying policies.

Continuous Control

dm_control: Software and Tasks for Continuous Control

1 code implementation22 Jun 2020 Yuval Tassa, Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Piotr Trochim, Si-Qi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess

The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation.

Continuous Control

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

Success at any cost: value constrained model-free continuous control

no code implementations27 Sep 2018 Steven Bohez, Abbas Abdolmaleki, Michael Neunert, Jonas Buchli, Nicolas Heess, Raia Hadsell

We demonstrate the efficiency of our approach using a number of continuous control benchmark tasks as well as a realistic, energy-optimized quadruped locomotion task.

Continuous Control

Transfer Learning with Binary Neural Networks

no code implementations29 Nov 2017 Sam Leroux, Steven Bohez, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt

Binary neural networks are attractive in this case because the logical operations are very fast and efficient when implemented in hardware.

Transfer Learning

Decoupled Learning of Environment Characteristics for Safe Exploration

no code implementations9 Aug 2017 Pieter Van Molle, Tim Verbelen, Steven Bohez, Sam Leroux, Pieter Simoens, Bart Dhoedt

However, when learning a task using reinforcement learning, the agent cannot distinguish the characteristics of the environment from those of the task.

Safe Exploration

Sensor Fusion for Robot Control through Deep Reinforcement Learning

no code implementations13 Mar 2017 Steven Bohez, Tim Verbelen, Elias De Coninck, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt

Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy.

Sensor Fusion

Lazy Evaluation of Convolutional Filters

no code implementations27 May 2016 Sam Leroux, Steven Bohez, Cedric De Boom, Elias De Coninck, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt

In this paper we propose a technique which avoids the evaluation of certain convolutional filters in a deep neural network.

Efficiency Evaluation of Character-level RNN Training Schedules

1 code implementation9 May 2016 Cedric De Boom, Sam Leroux, Steven Bohez, Pieter Simoens, Thomas Demeester, Bart Dhoedt

We present four training and prediction schedules from the same character-level recurrent neural network.

Learning Semantic Similarity for Very Short Texts

no code implementations2 Dec 2015 Cedric De Boom, Steven Van Canneyt, Steven Bohez, Thomas Demeester, Bart Dhoedt

We therefore investigated several text representations as a combination of word embeddings in the context of semantic pair matching.

Information Retrieval Semantic Similarity +2

Cannot find the paper you are looking for? You can Submit a new open access paper.