18 citations · 57 across the 10 of their papers we have counts for
9 papers · 1 filter
Efficient entity-based reinforcement learning
Vince Jankovics, Michael Garcia Ortiz, Eduardo Alonso
Recent deep reinforcement learning (DRL) successes rely on end-to-end learning from fixed-size observational inputs (e.g. image, state-variables). However, many challenging and int…
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction
Alban Laflaquière, Michael Garcia Ortiz
Despite its omnipresence in robotics application, the nature of spatial knowledge and the mechanisms that underlie its emergence in autonomous agents are still poorly understood. R…
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
Finding a generally accepted formal definition of a disentangled representation in the context of an agent behaving in an environment is an important challenge towards the construc…
S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
We consider the problem of building a state representation model for control, in a continual learning setting. As the environment changes, the aim is to efficiently compress the se…
Generative Models from the perspective of Continual Learning
Timothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz +2
Which generative model is the most suitable for Continual Learning? This paper aims at evaluating and comparing generative models on disjoint sequential image generation tasks. We…
State representation learning with recurrent capsule networks
Louis Annabi, Michael Garcia Ortiz
Unsupervised learning of compact and relevant state representations has been proved very useful at solving complex reinforcement learning tasks. In this paper, we propose a recurre…
Grounding Perception: A Developmental Approach to Sensorimotor Contingencies
Alban Laflaquière, Nikolas Hemion, Michaël Garcia Ortiz +1
Sensorimotor contingency theory offers a promising account of the nature of perception, a topic rarely addressed in the robotics community. We propose a developmental framework to…
Continual State Representation Learning for Reinforcement Learning using Generative Replay
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
We consider the problem of building a state representation model in a continual fashion. As the environment changes, the aim is to efficiently compress the sensory state's informat…
Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning
Hugo Caselles-Dupré, Louis Annabi, Oksana Hagen +2
Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents. It is of lower complexity compared to similar 3D platforms (e.g. De…