19 citations · 68 across the 8 of their papers we have counts for
8 papers
Learning Compositional Neural Programs for Continuous Control
Thomas Pierrot, Nicolas Perrin, Feryal Behbahani +4
We propose a novel solution to challenging sparse-reward, continuous control problems that require hierarchical planning at multiple levels of abstraction. Our solution, dubbed Alp…
PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning
Guillaume Matheron, Nicolas Perrin, Olivier Sigaud
The exploration-exploitation trade-off is at the heart of reinforcement learning (RL). However, most continuous control benchmarks used in recent RL research only require local exp…
The problem with DDPG: understanding failures in deterministic environments with sparse rewards
Guillaume Matheron, Nicolas Perrin, Olivier Sigaud
In environments with continuous state and action spaces, state-of-the-art actor-critic reinforcement learning algorithms can solve very complex problems, yet can also fail in envir…
State Representation Learning from Demonstration
Astrid Merckling, Alexandre Coninx, Loic Cressot +2
Robots could learn their own state and world representation from perception and experience without supervision. This desirable goal is the main focus of our field of interest, stat…
Learning Compositional Neural Programs with Recursive Tree Search and Planning
Thomas Pierrot, Guillaume Ligner, Scott Reed +6
We propose a novel reinforcement learning algorithm, AlphaNPI, that incorporates the strengths of Neural Programmer-Interpreters (NPI) and AlphaZero. NPI contributes structural bia…
First-order and second-order variants of the gradient descent in a unified framework
Thomas Pierrot, Nicolas Perrin, Olivier Sigaud
In this paper, we provide an overview of first-order and second-order variants of the gradient descent method that are commonly used in machine learning. We propose a general frame…