Who's Who of AI

Chris Amato

122 trust researcher @cjdamato.bsky.social · 1,807 followers

Agents & robotics AI research Culture, work & education

Why they matter

Researcher with public evidence across Agents & robotics, AI research, Culture, work & education.

AI signals: 2
Sources: 2
Discussions: 0
Latest signal: 11d ago

Associate Professor at Northeastern University and father of 3. Interests include artificial intelligence, reinforcement learning, and robotics (he/him).

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Articles & links

A new version of my book on cooperative multi-agent reinforcement learning is now available. A longer version will eventually be published with Frans Oliehoek so let us know your thoughts! arxiv.org/abs/2405.06161

An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning arxiv.org

AI Weekly's analysis →

Amato organizes cooperative MARL around three settings: centralized training and execution, centralized training for decentralized execution, and decentralized training and execution.
The paper walks through independent Q-learning, value factorization methods VDN, QMIX and QPLEX, and centralized critic methods MADDPG, COMA and MAPPO.
Amato writes that CTDE is the most common paradigm because it leverages centralized information at training while keeping execution decentralized.

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View on Bluesky · ♥ 26 ↻ 3 ↩ 1 · 2 from the directory shared this · 11d ago

Standard MAPPO has a couple of errors in it but using state-based critics is one of them. It biases the critics, which means (in this case) that they could be arbitrarily bad. This holds in the single-agent and multi-agent cases. E.g., www.jair.org/index.php/ja...

On Centralized Critics in Multi-Agent Reinforcement Learning | Journal of Artificial Intelligence Research jair.org

View on Bluesky · ♥ 1 ↻ 1 ↩ 1 · 16d ago

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