Andrea Tacchetti

Google (United States) — United States

Computer Science · Physical Sciences

18h-index4.3kcitations73works33.62yr avg
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Research Topics

Reinforcement Learning in Robotics(12), Experimental Behavioral Economics Studies(10), Auction Theory and Applications(10), Human Pose and Action Recognition(9), Face Recognition and Perception(8)

Publications72 total

Deep mechanism design: Learning social and economic policies for human benefit
Proceedings of the National Academy of Sciences·2025· 3 citedOpen Access
Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem
Nature Communications·2025· 6 citedOpen Access
Language Agents as Digital Representatives in Collective Decision-Making
arXiv (Cornell University)·2025· 7 citedOpen Access
Scaling Opponent Shaping to High Dimensional Games
2024
Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem
arXiv (Cornell University)·2024· 1 citedOpen Access
Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience
arXiv (Cornell University)·2024· 1 citedOpen Access
Gemma: Open Models Based on Gemini Research and Technology
arXiv (Cornell University)·2024· 217 citedOpen Access
Scaling Opponent Shaping to High Dimensional Games
arXiv (Cornell University)·2023Open Access
Teamwork Reinforcement Learning With Concave Utilities
IEEE Transactions on Mobile Computing·2023
Scaffolding cooperation in human groups with deep reinforcement learning
Nature Human Behaviour·2023· 22 citedOpen Access
Generative Adversarial Equilibrium Solvers
arXiv (Cornell University)·2023Open Access
Scaffolding cooperation in human groups with deep reinforcement learning
2022· 5 citedOpen Access
Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy
Nature Communications·2022· 17 citedOpen Access
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers
arXiv (Cornell University)·2022Open Access
Designing all-pay auctions using deep learning and multi-agent simulation
Scientific Reports·2022· 4 citedOpen Access
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
arXiv (Cornell University)·2022Open Access
Developing, evaluating and scaling learning agents in multi-agent environments
AI Communications·2022· 1 cited
Publisher Correction: Human-centred mechanism design with Democratic AI
Nature Human Behaviour·2022· 4 citedOpen Access
Human-centred mechanism design with Democratic AI
Nature Human Behaviour·2022· 73 citedOpen Access
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent
2022· 2 citedOpen Access
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Frequent Co-authors

Tomaso Poggio(14), Jan Balaguer(11), Matthew Botvinick(10), Lorenzo Rosasco(10), Yoram Bachrach(10), Raphaël Koster(9), Christopher Summerfield(9), Leyla Işık(7), János Kramár(7), Ian Gemp(7), Joel Z. Leibo(6), Jim Mutch(6), Lucy Campbell-Gillingham(6), Thore Graepel(6), Richard Everett(6), Tom Eccles(6), Peter Battaglia(5), Fabio Anselmi(5), Oliver Hauser(5), Kevin R. McKee(5)