32 citations · 95 across the 15 of their papers we have counts for
15 papers
Inverting the Bellman Equation: From -Values to World Models
Alistair Letcher, Mattie Fellows, Alexander D. Goldie +3
Model-based and model-free reinforcement learning are traditionally viewed as separate paradigms: instead of learning a model of the transition kernel , model-free agents typica…
Goal-Conditioned Agents that Learn Everything All at Once
Michael Matthews, Matthew Jackson, Michael Beukman +5
A goal-conditioned reinforcement learning agent exploring an environment will see a wealth of information throughout a trajectory, most of which is discarded when only performing o…
DiscoGen: Procedural Generation of Algorithm Discovery Tasks in Machine Learning
Alexander D. Goldie, Zilin Wang, Adrian Hayler +17
Automating the development of machine learning algorithms has the potential to unlock new breakthroughs. However, our ability to improve and evaluate algorithm discovery systems ha…
Evolution Strategies at the Hyperscale
Bidipta Sarkar, Mattie Fellows, Juan Agustin Duque +17
Evolution Strategies (ES) is a class of powerful black-box optimisation methods that are highly parallelisable and can handle non-differentiable and noisy objectives. However, naïv…
An Optimisation Framework for Unsupervised Environment Design
Nathan Monette, Alistair Letcher, Michael Beukman +4
For reinforcement learning agents to be deployed in high-risk settings, they must achieve a high level of robustness to unfamiliar scenarios. One method for improving robustness is…
Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits
Alistair Letcher, Stefan Woerner, Christa Zoufal
The training of a parameterized model largely depends on the landscape of the underlying loss function. In particular, vanishing gradients are a central bottleneck in the scalabili…
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…
Discovered Policy Optimisation
Chris Lu, Jakub Grudzien Kuba, Alistair Letcher +3
Tremendous progress has been made in reinforcement learning (RL) over the past decade. Most of these advancements came through the continual development of new algorithms, which we…
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…
Polymatrix Competitive Gradient Descent
Jeffrey Ma, Alistair Letcher, Florian Schäfer +2
Many economic games and machine learning approaches can be cast as competitive optimization problems where multiple agents are minimizing their respective objective function, which…
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian
Jack Parker-Holder, Luke Metz, Cinjon Resnick +6
Over the last decade, a single algorithm has changed many facets of our lives - Stochastic Gradient Descent (SGD). In the era of ever decreasing loss functions, SGD and its various…
On the Impossibility of Global Convergence in Multi-Loss Optimization
Alistair Letcher
Under mild regularity conditions, gradient-based methods converge globally to a critical point in the single-loss setting. This is known to break down for vanilla gradient descent…
Differentiable Game Mechanics
Alistair Letcher, David Balduzzi, Sebastien Racaniere +4
Deep learning is built on the foundational guarantee that gradient descent on an objective function converges to local minima. Unfortunately, this guarantee fails in settings, such…
Stable Opponent Shaping in Differentiable Games
Alistair Letcher, Jakob Foerster, David Balduzzi +2
A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel -- from GANs and intrinsic curiosity t…
Automatic Conflict Detection in Police Body-Worn Audio
Alistair Letcher, Jelena Trišović, Collin Cademartori +2
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…