no code implementations • 15 Oct 2023 • Julien Pourcel, Cédric Colas, Pierre-Yves Oudeyer, Laetitia Teodorescu
We here study automated problem generation in the context of the open-ended space of python programming puzzles.
no code implementations • 15 Jul 2023 • Grgur Kovač, Masataka Sawayama, Rémy Portelas, Cédric Colas, Peter Ford Dominey, Pierre-Yves Oudeyer
We introduce the concept of perspective controllability, which refers to a model's affordance to adopt various perspectives with differing values and personality traits.
no code implementations • 21 May 2023 • Cédric Colas, Laetitia Teodorescu, Pierre-Yves Oudeyer, Xingdi Yuan, Marc-Alexandre Côté
Without relying on any hand-coded goal representations, reward functions or curriculum, we show that LMA3 agents learn to master a large diversity of skills in a task-agnostic text-based environment.
1 code implementation • 13 Feb 2023 • Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, Jacob Andreas
Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function.
no code implementations • 2 Jun 2022 • Cédric Colas, Tristan Karch, Clément Moulin-Frier, Pierre-Yves Oudeyer
Building autonomous agents able to grow open-ended repertoires of skills across their lives is a fundamental goal of artificial intelligence (AI).
1 code implementation • 10 Feb 2022 • Ahmed Akakzia, Olivier Serris, Olivier Sigaud, Cédric Colas
In the quest for autonomous agents learning open-ended repertoires of skills, most works take a Piagetian perspective: learning trajectories are the results of interactions between developmental agents and their physical environment.
no code implementations • 25 May 2021 • Olivier Sigaud, Ahmed Akakzia, Hugo Caselles-Dupré, Cédric Colas, Pierre-Yves Oudeyer, Mohamed Chetouani
In the field of Artificial Intelligence, these extremes respectively map to autonomous agents learning from their own signals and interactive learning agents fully taught by their teachers.
no code implementations • 17 Dec 2020 • Cédric Colas, Tristan Karch, Olivier Sigaud, Pierre-Yves Oudeyer
Developmental RL is concerned with the use of deep RL algorithms to tackle a developmental problem -- the $intrinsically$ $motivated$ $acquisition$ $of$ $open$-$ended$ $repertoires$ $of$ $skills$.
2 code implementations • NeurIPS 2020 • Cédric Colas, Tristan Karch, Nicolas Lair, Jean-Michel Dussoux, Clément Moulin-Frier, Peter Dominey, Pierre-Yves Oudeyer
We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning.
2 code implementations • 9 Oct 2020 • Cédric Colas, Boris Hejblum, Sébastien Rouillon, Rodolphe Thiébaut, Pierre-Yves Oudeyer, Clément Moulin-Frier, Mélanie Prague
Epidemiologists model the dynamics of epidemics in order to propose control strategies based on pharmaceutical and non-pharmaceutical interventions (contact limitation, lock down, vaccination, etc).
no code implementations • ICML Workshop LaReL 2020 • Tristan Karch, Nicolas Lair, Cédric Colas, Jean-Michel Dussoux, Clément Moulin-Frier, Peter Ford Dominey, Pierre-Yves Oudeyer
We introduce the Playground environment and study how this form of goal imagination improves generalization and exploration over agents lacking this capacity.
no code implementations • 12 Jun 2020 • Cédric Colas, Ahmed Akakzia, Pierre-Yves Oudeyer, Mohamed Chetouani, Olivier Sigaud
In the real world, linguistic agents are also embodied agents: they perceive and act in the physical world.
no code implementations • ICML Workshop LaReL 2020 • Cédric Colas, Ahmed Akakzia, Pierre-Yves Oudeyer, Mohamed Chetouani, Olivier Sigaud
In the real world, linguistic agents are also embodied agents: they perceive and act in the physical world.
1 code implementation • ICLR 2021 • Ahmed Akakzia, Cédric Colas, Pierre-Yves Oudeyer, Mohamed Chetouani, Olivier Sigaud
In a second stage (L -> G), it trains a language-conditioned goal generator to generate semantic goals that match the constraints expressed in language-based inputs.
no code implementations • 20 Mar 2020 • Tristan Karch, Cédric Colas, Laetitia Teodorescu, Clément Moulin-Frier, Pierre-Yves Oudeyer
This paper investigates the idea of encoding object-centered representations in the design of the reward function and policy architectures of a language-guided reinforcement learning agent.
no code implementations • 10 Mar 2020 • Rémy Portelas, Cédric Colas, Lilian Weng, Katja Hofmann, Pierre-Yves Oudeyer
Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL). These methods shape the learning trajectories of agents by challenging them with tasks adapted to their capacities.
3 code implementations • 3 Mar 2020 • Cédric Colas, Joost Huizinga, Vashisht Madhavan, Jeff Clune
Quality-Diversity (QD) algorithms, and MAP-Elites (ME) in particular, have proven very useful for a broad range of applications including enabling real robots to recover quickly from joint damage, solving strongly deceptive maze tasks or evolving robot morphologies to discover new gaits.
2 code implementations • 21 Feb 2020 • Cédric Colas, Tristan Karch, Nicolas Lair, Jean-Michel Dussoux, Clément Moulin-Frier, Peter Ford Dominey, Pierre-Yves Oudeyer
We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning.
no code implementations • 8 Nov 2019 • Nicolas Lair, Cédric Colas, Rémy Portelas, Jean-Michel Dussoux, Peter Ford Dominey, Pierre-Yves Oudeyer
We propose LE2 (Language Enhanced Exploration), a learning algorithm leveraging intrinsic motivations and natural language (NL) interactions with a descriptive social partner (SP).
2 code implementations • 16 Oct 2019 • Rémy Portelas, Cédric Colas, Katja Hofmann, Pierre-Yves Oudeyer
We consider the problem of how a teacher algorithm can enable an unknown Deep Reinforcement Learning (DRL) student to become good at a skill over a wide range of diverse environments.
2 code implementations • 15 Apr 2019 • Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer
Consistently checking the statistical significance of experimental results is the first mandatory step towards reproducible science.
no code implementations • 28 Jan 2019 • Pierre Fournier, Olivier Sigaud, Cédric Colas, Mohamed Chetouani
In this paper we study a new reinforcement learning setting where the environment is non-rewarding, contains several possibly related objects of various controllability, and where an apt agent Bob acts independently, with non-observable intentions.
1 code implementation • 15 Oct 2018 • Cédric Colas, Pierre Fournier, Olivier Sigaud, Mohamed Chetouani, Pierre-Yves Oudeyer
In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration.
1 code implementation • 21 Jun 2018 • 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 reinforcement learning.
1 code implementation • ICML 2018 • Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer
In continuous action domains, standard deep reinforcement learning algorithms like DDPG suffer from inefficient exploration when facing sparse or deceptive reward problems.