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Found 7 papers, 1 papers with code
ACES: Generating Diverse Programming Puzzles with Autotelic Language Models and Semantic Descriptors
We here study automated problem generation in the context of the open-ended space of python programming puzzles.
Augmenting Autotelic Agents with Large Language Models
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.
A Song of Ice and Fire: Analyzing Textual Autotelic Agents in ScienceWorld
We show the importance of selectivity from the social peer's feedback; that experience replay needs to over-sample examples of rare goals; and that following self-generated goal sequences where the agent's competence is intermediate leads to significant improvements in final performance.
Automatic Exploration of Textual Environments with Language-Conditioned Autotelic Agents
In this extended abstract we discuss the opportunities and challenges of studying intrinsically-motivated agents for exploration in textual environments.
Grounding Spatio-Temporal Language with Transformers
While there is an extended literature studying how machines can learn grounded language, the topic of how to learn spatio-temporal linguistic concepts is still largely uncharted.
SpatialSim: Recognizing Spatial Configurations of Objects with Graph Neural Networks
Recognizing precise geometrical configurations of groups of objects is a key capability of human spatial cognition, yet little studied in the deep learning literature so far.
