451 citations · 2.2k across the 99 of their papers we have counts for
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CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Cédric Colas, Pierre Fournier, Olivier Sigaud +2
In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a l…
Diversity-Driven Selection of Exploration Strategies in Multi-Armed Bandits
Fabien C. Y. Benureau, Pierre-Yves Oudeyer
We consider a scenario where an agent has multiple available strategies to explore an unknown environment. For each new interaction with the environment, the agent must select whic…
How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments
Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer
Consistently checking the statistical significance of experimental results is one of the mandatory methodological steps to address the so-called "reproducibility crisis" in deep re…
Curiosity Driven Exploration of Learned Disentangled Goal Spaces
Adrien Laversanne-Finot, Alexandre Péré, Pierre-Yves Oudeyer
Intrinsically motivated goal exploration processes enable agents to autonomously sample goals to explore efficiently complex environments with high-dimensional continuous actions.…
Accuracy-based Curriculum Learning in Deep Reinforcement Learning
Pierre Fournier, Olivier Sigaud, Mohamed Chetouani +1
In this paper, we investigate a new form of automated curriculum learning based on adaptive selection of accuracy requirements, called accuracy-based curriculum learning. Using a r…
Complexity Reduction in the Negotiation of New Lexical Conventions
William Schueller, Vittorio Loreto, Pierre-Yves Oudeyer
In the process of collectively inventing new words for new concepts in a population, conflicts can quickly become numerous, in the form of synonymy and homonymy. Remembering all of…