13 citations · 25 across the 13 of their papers we have counts for
13 papers
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…
No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery
Alexander Rutherford, Michael Beukman, Timon Willi +3
What data or environments to use for training to improve downstream performance is a longstanding and very topical question in reinforcement learning. In particular, Unsupervised E…
Mixture of Experts in a Mixture of RL settings
Timon Willi, Johan Obando-Ceron, Jakob Foerster +2
Mixtures of Experts (MoEs) have gained prominence in (self-)supervised learning due to their enhanced inference efficiency, adaptability to distributed training, and modularity. Pr…
The Danger Of Arrogance: Welfare Equilibra As A Solution To Stackelberg Self-Play In Non-Coincidental Games
Jake Levi, Chris Lu, Timon Willi +2
The increasing prevalence of multi-agent learning systems in society necessitates understanding how to learn effective and safe policies in general-sum multi-agent environments aga…
Analysing the Sample Complexity of Opponent Shaping
Kitty Fung, Qizhen Zhang, Chris Lu +3
Learning in general-sum games often yields collectively sub-optimal results. Addressing this, opponent shaping (OS) methods actively guide the learning processes of other agents, e…
Mixtures of Experts Unlock Parameter Scaling for Deep RL
Johan Obando-Ceron, Ghada Sokar, Timon Willi +6
The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size. Analog…
Leading the Pack: N-player Opponent Shaping
Alexandra Souly, Timon Willi, Akbir Khan +4
Reinforcement learning solutions have great success in the 2-player general sum setting. In this setting, the paradigm of Opponent Shaping (OS), in which agents account for the lea…
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…
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
Alexander Rutherford, Benjamin Ellis, Matteo Gallici +18
Benchmarks are crucial in the development of machine learning algorithms, with available environments significantly influencing reinforcement learning (RL) research. Traditionally,…
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…
Recurrent Neural Processes
Timon Willi, Jonathan Masci, Jürgen Schmidhuber +1
We extend Neural Processes (NPs) to sequential data through Recurrent NPs or RNPs, a family of conditional state space models. RNPs model the state space with Neural Processes. Giv…