20 citations · 32 across the 3 of their papers we have counts for
6 papers
Language-Conditioned Goal Generation: a New Approach to Language Grounding for RL
Cédric Colas, Ahmed Akakzia, Pierre-Yves Oudeyer +2
In the real world, linguistic agents are also embodied agents: they perceive and act in the physical world. The notion of Language Grounding questions the interactions between lang…
Deep Sets for Generalization in RL
Tristan Karch, Cédric Colas, Laetitia Teodorescu +2
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 learni…
Automatic Curriculum Learning For Deep RL: A Short Survey
Rémy Portelas, Cédric Colas, Lilian Weng +2
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 cha…
Scaling MAP-Elites to Deep Neuroevolution
Cédric Colas, Joost Huizinga, Vashisht Madhavan +1
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 fr…
Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning
Nicolas Lair, Cédric Colas, Rémy Portelas +3
Autonomous reinforcement learning agents, like children, do not have access to predefined goals and reward functions. They must discover potential goals, learn their own reward fun…
Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments
Rémy Portelas, Cédric Colas, Katja Hofmann +1
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 environmen…