44 citations · 140 across the 8 of their papers we have counts for
10 papers
Enhancing Agent Communication and Learning through Action and Language
Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
We introduce a novel category of GC-agents capable of functioning as both teachers and learners. Leveraging action-based demonstrations and language-based instructions, these agent…
On the Sensory Commutativity of Action Sequences for Embodied Agents
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
Perception of artificial agents is one the grand challenges of AI research. Deep Learning and data-driven approaches are successful on constrained problems where perception can be…
DisCoRL: Continual Reinforcement Learning via Policy Distillation
René Traoré, Hugo Caselles-Dupré, Timothée Lesort +4
In multi-task reinforcement learning there are two main challenges: at training time, the ability to learn different policies with a single model; at test time, inferring which of…
Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer
René Traoré, Hugo Caselles-Dupré, Timothée Lesort +3
We focus on the problem of teaching a robot to solve tasks presented sequentially, i.e., in a continual learning scenario. The robot should be able to solve all tasks it has encoun…
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…
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…
Word2Vec applied to Recommendation: Hyperparameters Matter
Hugo Caselles-Dupré, Florian Lesaint, Jimena Royo-Letelier
Skip-gram with negative sampling, a popular variant of Word2vec originally designed and tuned to create word embeddings for Natural Language Processing, has been used to create ite…