Jesse Farebrother

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Max Schwarzer

Jesse Farebrother

Joshua Greaves

Ekin Dogus Cubuk

Rishabh Agarwal

Aaron Courville

Bellemare Marc-Emmanuel

Sergei Kalinin

Igor Mordatch

Pablo Samuel Castro

Kevin M Roccapriore

We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimu… (voir plus)lated by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.

2025-05-19

Advanced Materials Interfaces (publié)

doi.org

arxiv.org

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching

Sanjiban Choudhury

In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Tradit… (voir plus)ionally, IRL is treated as an adversarial game, where an adversary searches over reward models, and a learner optimizes the reward through repeated RL procedures. This game-solving approach is both computationally expensive and difficult to stabilize. In this work, we propose a novel approach to IRL by direct policy optimization: exploiting a linear factorization of the return as the inner product of successor features and a reward vector, we design an IRL algorithm by policy gradient descent on the gap between the learner and expert features. Our non-adversarial method does not require learning a reward function and can be solved seamlessly with existing actor-critic RL algorithms. Remarkably, our approach works in state-only settings without expert action labels, a setting which behavior cloning (BC) cannot solve. Empirical results demonstrate that our method learns from as few as a single expert demonstration and achieves improved performance on various control tasks.

2025-01-21

ICLR.cc/2025/Conference (poster)

doi.org

openreview.net

CALE: Continuous Arcade Learning Environment

Jesse Farebrother

Pablo Samuel Castro

We introduce the Continuous Arcade Learning Environment (CALE), an extension of the well-known Arcade Learning Environment (ALE) [Bellemare … (voir plus)et al., 2013]. The CALE uses the same underlying emulator of the Atari 2600 gaming system (Stella), but adds support for continuous actions. This enables the benchmarking and evaluation of continuous-control agents (such as PPO [Schulman et al., 2017] and SAC [Haarnoja et al., 2018]) and value-based agents (such as DQN [Mnih et al., 2015] and Rainbow [Hessel et al., 2018]) on the same environment suite. We provide a series of open questions and research directions that CALE enables, as well as initial baseline results using Soft Actor-Critic. CALE is available as part of the ALE athttps://github.com/Farama-Foundation/Arcade-Learning-Environment.

2024-09-25

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)

doi.org

openreview.net

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Jesse Farebrother

Jordi Orbay

Quan Vuong

Adrien Ali Taiga

Yevgen Chebotar

Ted Xiao

Alex Irpan

Sergey Levine

Pablo Samuel Castro

Aleksandra Faust

Aviral Kumar

Rishabh Agarwal

Value functions are a central component of deep reinforcement learning (RL). These functions, parameterized by neural networks, are trained … (voir plus)using a mean squared error regression objective to match bootstrapped target values. However, scaling value-based RL methods that use regression to large networks, such as high-capacity Transformers, has proven challenging. This difficulty is in stark contrast to supervised learning: by leveraging a cross-entropy classification loss, supervised methods have scaled reliably to massive networks. Observing this discrepancy, in this paper, we investigate whether the scalability of deep RL can also be improved simply by using classification in place of regression for training value functions. We demonstrate that value functions trained with categorical cross-entropy significantly improves performance and scalability in a variety of domains. These include: single-task RL on Atari 2600 games with SoftMoEs, multi-task RL on Atari with large-scale ResNets, robotic manipulation with Q-transformers, playing Chess without search, and a language-agent Wordle task with high-capacity Transformers, achieving state-of-the-art results on these domains. Through careful analysis, we show that the benefits of categorical cross-entropy primarily stem from its ability to mitigate issues inherent to value-based RL, such as noisy targets and non-stationarity. Overall, we argue that a simple shift to training value functions with categorical cross-entropy can yield substantial improvements in the scalability of deep RL at little-to-no cost.

2024-07-07

Proceedings of the 41st International Conference on Machine Learning (publié)

doi.org

proceedings.mlr.press

The Position Dependence of Electron Beam Induced Effects in 2D Materials with Deep Neural Networks

Kevin M. Roccapriore

Max Schwarzer

Joshua Greaves

Jesse Farebrother

Riccardo Torsi

Rishabh Agarwal

Colton Bishop

Igor Mordatch

Ekin D. Cubuk

Aaron Courville

Bellemare Marc-Emmanuel

Joshua Robinson

Pablo Samuel Castro

Sergei V Kalinin

2024-06-30

Microscopy and Microanalysis (inconnu)

doi.org

Revisiting Successor Features for Inverse Reinforcement Learning

Sanjiban Choudhury

2024-06-16

ICML.cc/2024/Workshop/MFHAIA (poster)

openreview.net

A Distributional Analogue to the Successor Representation

Harley Wiltzer

Jesse Farebrother

Arthur Gretton

Yunhao Tang

Andre Barreto

Will Dabney

Bellemare Marc-Emmanuel

Mark Rowland

This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure … (voir plus)and reward in the learning process. Analogous to how the successor representation (SR) describes the expected consequences of behaving according to a given policy, our distributional successor measure (SM) describes the distributional consequences of this behaviour. We formulate the distributional SM as a distribution over distributions and provide theory connecting it with distributional and model-based reinforcement learning. Moreover, we propose an algorithm that learns the distributional SM from data by minimizing a two-level maximum mean discrepancy. Key to our method are a number of algorithmic techniques that are independently valuable for learning generative models of state. As an illustration of the usefulness of the distributional SM, we show that it enables zero-shot risk-sensitive policy evaluation in a way that was not previously possible.

2024-04-30

ICML.cc/2024/Conference (spotlight)

doi.org

proceedings.mlr.press

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Johan Obando-Ceron

Ghada Sokar

Timon Willi

Clare Lyle

Jesse Farebrother

Jakob Foerster

Karolina Dziugaite

Doina Precup

Pablo Samuel Castro

The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance s… (voir plus)cales proportionally to its size. Analogous scaling laws remain elusive for reinforcement learning domains, however, where increasing the parameter count of a model often hurts its final performance. In this paper, we demonstrate that incorporating Mixture-of-Expert (MoE) modules, and in particular Soft MoEs (Puigcerver et al., 2023), into value-based networks results in more parameter-scalable models, evidenced by substantial performance increases across a variety of training regimes and model sizes. This work thus provides strong empirical evidence towards developing scaling laws for reinforcement learning.

2024-04-30

ICML.cc/2024/Conference (spotlight)

doi.org

proceedings.mlr.press

Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Max Schwarzer

Jesse Farebrother

Joshua Greaves

Kevin Roccapriore

Ekin Dogus Cubuk

Rishabh Agarwal

Aaron Courville

Bellemare Marc-Emmanuel

Sergei Kalinin

Igor Mordatch

Pablo Samuel Castro

We introduce a machine learning approach to determine the transition rates of silicon atoms on a single layer of carbon atoms, when stimulat… (voir plus)ed by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition rates. These rates are then applied to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.

2023-10-26

NeurIPS.cc/2023/Workshop/AI4Mat (spotlight)

openreview.net

Discovering the Electron Beam Induced Transition Rates for Silicon Dopants in Graphene with Deep Neural Networks in the STEM

Kevin M Roccapriore

Max Schwarzer

Joshua Greaves

Jesse Farebrother

Rishabh Agarwal

Colton Bishop

Maxim Ziatdinov

Igor Mordatch

Ekin D Cubuk

Aaron Courville

Pablo Samuel Castro

Bellemare Marc-Emmanuel

Sergei V Kalinin

Journal Article Discovering the Electron Beam Induced Transition Rates for Silicon Dopants in Graphene with Deep Neural Networks in the STEM… (voir plus) Get access Kevin M Roccapriore, Kevin M Roccapriore Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United States Search for other works by this author on: Oxford Academic Google Scholar Max Schwarzer, Max Schwarzer Mila - Québec AI Institute, Montréal, QC, CanadaDepartment of Computer Science and Operations Research, Université de Montréal, Montréal, QC, CanadaGoogle Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Joshua Greaves, Joshua Greaves Google Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Jesse Farebrother, Jesse Farebrother Mila - Québec AI Institute, Montréal, QC, CanadaGoogle Research, Brain TeamSchool of Computer Science, McGill University, Montréal, QC, Canada Search for other works by this author on: Oxford Academic Google Scholar Rishabh Agarwal, Rishabh Agarwal Mila - Québec AI Institute, Montréal, QC, CanadaDepartment of Computer Science and Operations Research, Université de Montréal, Montréal, QC, CanadaGoogle Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Colton Bishop, Colton Bishop Google Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Maxim Ziatdinov, Maxim Ziatdinov Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States Search for other works by this author on: Oxford Academic Google Scholar Igor Mordatch, Igor Mordatch Google Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Ekin D Cubuk, Ekin D Cubuk Google Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Aaron Courville, Aaron Courville Mila - Québec AI Institute, Montréal, QC, CanadaDepartment of Computer Science and Operations Research, Université de Montréal, Montréal, QC, Canada Search for other works by this author on: Oxford Academic Google Scholar ... Show more Pablo Samuel Castro, Pablo Samuel Castro Google Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Marc G Bellemare, Marc G Bellemare Mila - Québec AI Institute, Montréal, QC, CanadaGoogle Research, Brain TeamSchool of Computer Science, McGill University, Montréal, QC, Canada Search for other works by this author on: Oxford Academic Google Scholar Sergei V Kalinin Sergei V Kalinin Department of Materials Science and Engineering, University of Tennessee, Knoxville TN, United States Corresponding author: sergei2@utk.edu Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 1932–1933, https://doi.org/10.1093/micmic/ozad067.1000 Published: 22 July 2023

2023-07-21

Microscopy and Microanalysis (publié)

doi.org

A Novel Stochastic Gradient Descent Algorithm for LearningPrincipal Subspaces

Charline Le Lan

Joshua Greaves

Jesse Farebrother

Mark Rowland

Fabian Pedregosa

Rishabh Agarwal

Bellemare Marc-Emmanuel

In this paper, we derive an algorithm that learns a principal subspace from sample entries, can be applied when the approximate subspace i… (voir plus)s represented by a neural network, and hence can bescaled to datasets with an effectively infinite number of rows and columns. Our method consistsin defining a loss function whose minimizer is the desired principal subspace, and constructing agradient estimate of this loss whose bias can be controlled.

2023-04-10

Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (publié)

doi.org

proceedings.mlr.press

Investigating Multi-Task Pretraining and Generalization in Reinforcement Learning

Bellemare Marc-Emmanuel

Google Brain

2023-01-31