36 citations · 133 across the 20 of their papers we have counts for
9 papers · 1 filter
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-Diversity
Achkan Salehi, Stephane Doncieux
Quality-Diversity is a branch of stochastic optimization that is often applied to problems from the Reinforcement Learning and control domains in order to construct repertoires of…
Random Actions vs Random Policies: Bootstrapping Model-Based Direct Policy Search
Elias Hanna, Alex Coninx, Stéphane Doncieux
This paper studies the impact of the initial data gathering method on the subsequent learning of a dynamics model. Dynamics models approximate the true transition function of a giv…
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations
Achkan Salehi, Steffen Rühl, Stephane Doncieux
Model-based Reinforcement Learning and Control have demonstrated great potential in various sequential decision making problem domains, including in robotics settings. However, rea…
Towards QD-suite: developing a set of benchmarks for Quality-Diversity algorithms
Achkan Salehi, Stephane Doncieux
While the field of Quality-Diversity (QD) has grown into a distinct branch of stochastic optimization, a few problems, in particular locomotion and navigation tasks, have become de…
Geodesics, Non-linearities and the Archive of Novelty Search
Achkan Salehi, Alexandre Coninx, Stephane Doncieux
The Novelty Search (NS) algorithm was proposed more than a decade ago. However, the mechanisms behind its empirical success are still not well formalized/understood. This short not…
Discovering and Exploiting Sparse Rewards in a Learned Behavior Space
Giuseppe Paolo, Miranda Coninx, Alban Laflaquière +1
Learning optimal policies in sparse rewards settings is difficult as the learning agent has little to no feedback on the quality of its actions. In these situations, a good strateg…
Exploratory State Representation Learning
Astrid Merckling, Nicolas Perrin-Gilbert, Alex Coninx +1
Not having access to compact and meaningful representations is known to significantly increase the complexity of reinforcement learning (RL). For this reason, it can be useful to p…
Few-shot Quality-Diversity Optimization
Achkan Salehi, Alexandre Coninx, Stephane Doncieux
In the past few years, a considerable amount of research has been dedicated to the exploitation of previous learning experiences and the design of Few-shot and Meta Learning approa…
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…