Publications

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 an essential component in deep reinforcement learning (RL), that are typically trained via mean squared error regression… (voir plus) to match bootstrapped target values. However, scaling value-based RL methods to large networks 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 show that training value functions with categorical cross-entropy significantly enhances performance and scalability across various domains, including single-task RL on Atari 2600 games, 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 categorical cross-entropy mitigates issues inherent to value-based RL, such as noisy targets and non-stationarity. We argue that shifting to categorical cross-entropy for training value functions can substantially improve the scalability of deep RL at little-to-no cost.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks
Ziquan Liu
Yufei Cui
Yan Yan
Yi Xu
Xiangyang Ji
Xue (Steve) Liu
Antoni B. Chan
In safety-critical applications such as medical imaging and autonomous driving, where decisions have profound implications for patient healt… (voir plus)h and road safety, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks and reliable uncertainty quantification in decision-making. With extensive research focused on enhancing adversarial robustness through various forms of adversarial training (AT), a notable knowledge gap remains concerning the uncertainty inherent in adversarially trained models. To address this gap, this study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks within the adversarial defense community. It is first unveiled that existing CP methods do not produce informative prediction sets under the commonly used
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
Think Before You Act: Decision Transformers with Working Memory
Jikun Kang
Romain Laroche
Xingdi Yuan
Adam Trischler
Xue (Steve) Liu
Jie Fu
Decision Transformer-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance rel… (voir plus)ies on massive data and computation. We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout training. As a result, training on a new task may deteriorate the model’s performance on previous tasks. In contrast to LLMs’ implicit memory mechanism, the human brain utilizes distributed memory storage, which helps manage and organize multiple skills efficiently, mitigating the forgetting phenomenon. Inspired by this, we propose a working memory module to store, blend, and retrieve information for different downstream tasks. Evaluation results show that the proposed method improves training efficiency and generalization in Atari games and Meta-World object manipulation tasks. Moreover, we demonstrate that memory fine-tuning further enhances the adaptability of the proposed architecture.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
Towards Modular LLMs by Building and Reusing a Library of LoRAs
Oleksiy Ostapenko
Zhan Su
Edoardo Ponti
Laurent Charlin
Nicolas Le Roux
Matheus Pereira
Lucas Caccia
Alessandro Sordoni
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Do Transformer World Models Give Better Policy Gradients?
Michel Ma
Tianwei Ni
Clement Gehring
Pierluca D'Oro
Pierre-Luc Bacon
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Unsupervised Concept Discovery Mitigates Spurious Correlations
Md Rifat Arefin
Yan Zhang
Aristide Baratin
Francesco Locatello
Irina Rish
Dianbo Liu
Kenji Kawaguchi
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
In value-based deep reinforcement learning, a pruned network is a good network
Johan Samir Obando Ceron
Aaron Courville
Pablo Samuel Castro
Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage pri… (voir plus)or insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables {value-based} agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks, using only a small fraction of the full network parameters. Our code is publicly available, see Appendix A for details.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
openreview.net
When is Transfer Learning Possible?
My Phan
Kianté Brantley
Stephanie Milani
Soroush Mehri
Gokul Swamy
Geoff Gordon
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Alexandre Drouin
Maxime Gasse
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
Quentin Cappart
David Vazquez
Nicolas Chapados
Alexandre Lacoste
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
Charles Guille-Escuret
Hiroki Naganuma
Kilian FATRAS
Ioannis Mitliagkas
Understanding the optimization dynamics of neural networks is necessary for closing the gap between theory and practice. Stochastic first-or… (voir plus)der optimization algorithms are known to efficiently locate favorable minima in deep neural networks. This efficiency, however, contrasts with the non-convex and seemingly complex structure of neural loss landscapes. In this study, we delve into the fundamental geometric properties of sampled gradients along optimization paths. We focus on two key quantities, which appear in the restricted secant inequality and error bound. Both hold high significance for first-order optimization. Our analysis reveals that these quantities exhibit predictable, consistent behavior throughout training, despite the stochasticity induced by sampling minibatches. Our findings suggest that not only do optimization trajectories never encounter significant obstacles, but they also maintain stable dynamics during the majority of training. These observed properties are sufficiently expressive to theoretically guarantee linear convergence and prescribe learning rate schedules mirroring empirical practices. We conduct our experiments on image classification, semantic segmentation and language modeling across different batch sizes, network architectures, datasets, optimizers, and initialization seeds. We discuss the impact of each factor. Our work provides novel insights into the properties of neural network loss functions, and opens the door to theoretical frameworks more relevant to prevalent practice.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Prospective Messaging: Learning in Networks with Communication Delays
Ryan Fayyazi
Christian Dietrich Weilbach
Frank Wood
Inter-neuron communication delays are ubiquitous in physically realized neural networks such as biological neural circuits and neuromorphic … (voir plus)hardware. These delays have significant and often disruptive consequences on network dynamics during training and inference. It is therefore essential that communication delays be accounted for, both in computational models of biological neural networks and in large-scale neuromorphic systems. Nonetheless, communication delays have yet to be comprehensively addressed in either domain. In this paper, we first show that delays prevent state-of-the-art continuous-time neural networks called Latent Equilibrium (LE) networks from learning even simple tasks despite significant overparameterization. We then propose to compensate for communication delays by predicting future signals based on currently available ones. This conceptually straightforward approach, which we call prospective messaging (PM), uses only neuron-local information, and is flexible in terms of memory and computation requirements. We demonstrate that incorporating PM into delayed LE networks prevents reaction lags, and facilitates successful learning on Fourier synthesis and autoregressive video prediction tasks.
2024-07-07
ArXiv (prépublication)
doi.org
arxiv.org
A Novel Bifurcation Method for Observation Perturbation Attacks on Reinforcement Learning Agents: Load Altering Attacks on a Cyber Physical Power System
Kiernan Broda-Milian
Ranwa Al-Mallah
Hanane Dagdougui
Components of cyber physical systems, which affect real-world processes, are often exposed to the internet. Replacing conventional control m… (voir plus)ethods with Deep Reinforcement Learning (DRL) in energy systems is an active area of research, as these systems become increasingly complex with the advent of renewable energy sources and the desire to improve their efficiency. Artificial Neural Networks (ANN) are vulnerable to specific perturbations of their inputs or features, called adversarial examples. These perturbations are difficult to detect when properly regularized, but have significant effects on the ANN's output. Because DRL uses ANN to map optimal actions to observations, they are similarly vulnerable to adversarial examples. This work proposes a novel attack technique for continuous control using Group Difference Logits loss with a bifurcation layer. By combining aspects of targeted and untargeted attacks, the attack significantly increases the impact compared to an untargeted attack, with drastically smaller distortions than an optimally targeted attack. We demonstrate the impacts of powerful gradient-based attacks in a realistic smart energy environment, show how the impacts change with different DRL agents and training procedures, and use statistical and time-series analysis to evaluate attacks' stealth. The results show that adversarial attacks can have significant impacts on DRL controllers, and constraining an attack's perturbations makes it difficult to detect. However, certain DRL architectures are far more robust, and robust training methods can further reduce the impact.
2024-07-06
ArXiv (prépublication)
doi.org
arxiv.org