Search Results for author:
Found 6 papers, 2 papers with code
Animal-AI 3: What's New & Why You Should Care
The Animal-AI Environment is a unique game-based research platform designed to serve both the artificial intelligence and cognitive science research communities.
Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks
These findings open the door to new ways of investigating how and why learning systems employ specific information-processing strategies, and support the principle that the capacity for general-purpose learning critically relies in the system's information dynamics.
Episodic Memory for Subjective-Timescale Models
Planning in complex environments requires reasoning over multi-step timescales.
Fixed $β$-VAE Encoding for Curious Exploration in Complex 3D Environments
Curiosity is a general method for augmenting an environment reward with an intrinsic reward, which encourages exploration and is especially useful in sparse reward settings.
Episodic Memory for Learning Subjective-Timescale Models
In model-based learning, an agent's model is commonly defined over transitions between consecutive states of an environment even though planning often requires reasoning over multi-step timescales, with intermediate states either unnecessary, or worse, accumulating prediction error.
The Animal-AI Environment: Training and Testing Animal-Like Artificial Cognition
Recent advances in artificial intelligence have been strongly driven by the use of game environments for training and evaluating agents.
