activity
20072026
most citedDeep Reinforcement Learning in Large Discrete Action Spaces

265 citations · 424 across the 21 of their papers we have counts for

collaborators

23 papers

cs.NE2026

A Theory of Appropriateness That Accounts for Norms of Rationality

Joel Z. Leibo, Alexander Sasha Vezhnevets, Manfred Diaz +10

We propose a society-first theory of normative appropriateness where individuals, modeled as pre-trained actors with cognitive architectures analogous to Large Language Models (LLM…

cs.AI2025

Simulation Streams: A Programming Paradigm for Controlling Large Language Models and Building Complex Systems with Generative AI

Peter Sunehag, Joel Z. Leibo

We introduce Simulation Streams, a programming paradigm designed to efficiently control and leverage Large Language Models (LLMs) for complex, dynamic simulations and agentic workf…

cs.AI2024★ 1 cited

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…

cs.MA2023★ 15 cited

A Review of Cooperation in Multi-agent Learning

Yali Du, Joel Z. Leibo, Usman Islam +2

Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous disciplines, including game theory, economics, social sciences, and evolutionary biology. Resea…

cs.AI2023

Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition

Peter Sunehag, Alexander Sasha Vezhnevets, Edgar Duéñez-Guzmán +2

Many environments contain numerous available niches of variable value, each associated with a different local optimum in the space of behaviors (policy space). In such situations i…

cs.MA2022★ 3 cited

Melting Pot 2.0

John P. Agapiou, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán +14

Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsis…