13 citations · 25 across the 13 of their papers we have counts for
3 papers · 1 filter
The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind
Andrei Lupu, Timon Willi, Jakob Foerster
As Large Language Models (LLMs) gain agentic abilities, they will have to navigate complex multi-agent scenarios, interacting with human users and other agents in cooperative and c…
Scaling Opponent Shaping to High Dimensional Games
Akbir Khan, Timon Willi, Newton Kwan +5
In multi-agent settings with mixed incentives, methods developed for zero-sum games have been shown to lead to detrimental outcomes. To address this issue, opponent shaping (OS) me…
Model-Free Opponent Shaping
Chris Lu, Timon Willi, Christian Schroeder de Witt +1
In general-sum games, the interaction of self-interested learning agents commonly leads to collectively worst-case outcomes, such as defect-defect in the iterated prisoner's dilemm…