13 citations · 25 across the 13 of their papers we have counts for
3 papers · 1 filter
Adversarial Cheap Talk
Chris Lu, Timon Willi, Alistair Letcher +1
Adversarial attacks in reinforcement learning (RL) often assume highly-privileged access to the victim's parameters, environment, or data. Instead, this paper proposes a novel adve…
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…
COLA: Consistent Learning with Opponent-Learning Awareness
Timon Willi, Alistair Letcher, Johannes Treutlein +1
Learning in general-sum games is unstable and frequently leads to socially undesirable (Pareto-dominated) outcomes. To mitigate this, Learning with Opponent-Learning Awareness (LOL…