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Found 7 papers, 2 papers with code
Active World Model Learning in Agent-rich Environments with Progress Curiosity
World models are a family of predictive models that solve self-supervised problems on how the world evolves.
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations
We investigate how to leverage the representations produced by Neural Collages in various tasks, including data compression and generation.
LISA: Learning Interpretable Skill Abstractions from Language
Learning policies that effectively utilize language instructions in complex, multi-task environments is an important problem in sequential decision-making.
Imitation with Neural Density Models
We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Maximum Occupancy Entropy Reinforcement Learning (RL) using the density as a reward.
Active World Model Learning with Progress Curiosity
Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal horizons, and an understanding of the behavioral patterns of other agents.
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation.
Domain Adaptive Imitation Learning
We formalize the Domain Adaptive Imitation Learning (DAIL) problem, which is a unified framework for imitation learning in the presence of viewpoint, embodiment, and dynamics mismatch.
