682 citations · 1.1k across the 12 of their papers we have counts for
7 papers · 1 filter
SIMA 2: A Generalist Embodied Agent for Virtual Worlds
SIMA team, Adrian Bolton, Alexander Lerchner +63
We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a signifi…
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
A theory of appropriateness with applications to generative artificial intelligence
Joel Z. Leibo, Alexander Sasha Vezhnevets, Manfred Diaz +11
What is appropriateness? Humans navigate a multi-scale mosaic of interlocking notions of what is appropriate for different situations. We act one way with our friends, another with…
Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia
Alexander Sasha Vezhnevets, John P. Agapiou, Avia Aharon +7
Agent-based modeling has been around for decades, and applied widely across the social and natural sciences. The scope of this research method is now poised to grow dramatically as…
Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria
Kavya Kopparapu, Edgar A. Duéñez-Guzmán, Jayd Matyas +7
A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively, but to decide with whom to cooperate. This is particular…
Deep Q-learning from Demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin +11
Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of dat…
Strategic Attentive Writer for Learning Macro-Actions
Alexander, Vezhnevets, Volodymyr Mnih +5
We present a novel deep recurrent neural network architecture that learns to build implicit plans in an end-to-end manner by purely interacting with an environment in reinforcement…