Search Results for author:
Found 7 papers, 3 papers with code
Delayed Geometric Discounts: An Alternative Criterion for Reinforcement Learning
The endeavor of artificial intelligence (AI) is to design autonomous agents capable of achieving complex tasks.
Learning Object-Centered Autotelic Behaviors with Graph Neural Networks
However, these capabilities are highly constrained by their policy and goal space representations.
Help Me Explore: Minimal Social Interventions for Graph-Based Autotelic Agents
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.
Towards Teachable Autotelic Agents
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.
Language-Conditioned Goal Generation: a New Approach to Language Grounding for RL
In the real world, linguistic agents are also embodied agents: they perceive and act in the physical world.
Grounding Language to Autonomously-Acquired Skills via Goal Generation
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.
Language-Conditioned Goal Generation: a New Approach to Language Grounding in RL
In the real world, linguistic agents are also embodied agents: they perceive and act in the physical world.
