Search Results for author: Alistair Letcher

Found 9 papers, 5 papers with code

Adversarial Cheap Talk

1 code implementation20 Nov 2022 Chris Lu, Timon Willi, Alistair Letcher, Jakob Foerster

More specifically, we show that an ACT Adversary is capable of harming performance by interfering with the learner's function approximation, or instead helping the Victim's performance by outputting useful features.

Meta-Learning Reinforcement Learning (RL)

COLA: Consistent Learning with Opponent-Learning Awareness

1 code implementation8 Mar 2022 Timon Willi, Alistair Letcher, Johannes Treutlein, Jakob Foerster

Finally, in an empirical evaluation on a set of general-sum games, we find that COLA finds prosocial solutions and that it converges under a wider range of learning rates than HOLA and LOLA.

CoLA

Polymatrix Competitive Gradient Descent

no code implementations16 Nov 2021 Jeffrey Ma, Alistair Letcher, Florian Schäfer, Yuanyuan Shi, Anima Anandkumar

In this work we propose polymatrix competitive gradient descent (PCGD) as a method for solving general sum competitive optimization involving arbitrary numbers of agents.

Multi-agent Reinforcement Learning

Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian

no code implementations NeurIPS 2020 Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alex Peysakhovich, Aldo Pacchiano, Jakob Foerster

In the era of ever decreasing loss functions, SGD and its various offspring have become the go-to optimization tool in machine learning and are a key component of the success of deep neural networks (DNNs).

BIG-bench Machine Learning

On the Impossibility of Global Convergence in Multi-Loss Optimization

1 code implementation ICLR 2021 Alistair Letcher

Under mild regularity conditions, gradient-based methods converge globally to a critical point in the single-loss setting.

Open-Ended Question Answering

Differentiable Game Mechanics

1 code implementation13 May 2019 Alistair Letcher, David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel

The decomposition motivates Symplectic Gradient Adjustment (SGA), a new algorithm for finding stable fixed points in differentiable games.

Stable Opponent Shaping in Differentiable Games

no code implementations ICLR 2019 Alistair Letcher, Jakob Foerster, David Balduzzi, Tim Rocktäschel, Shimon Whiteson

A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel -- from GANs and intrinsic curiosity to multi-agent RL.

Automatic Conflict Detection in Police Body-Worn Audio

no code implementations14 Nov 2017 Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu

Automatic conflict detection has grown in relevance with the advent of body-worn technology, but existing metrics such as turn-taking and overlap are poor indicators of conflict in police-public interactions.

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