Razvan Pascanu

openreview.net

Agency Is Frame-Dependent

David Abel

Andre Barreto

Michael Bowling

Will Dabney

Shi Dong

Steven Stenberg Hansen

Anna Harutyunyan

Khimya Khetarpal

Clare Lyle

Georgios Piliouras

Doina Precup

Jonathan Richens

Mark Rowland

Tom Schaul

Satinder Singh

Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science… (voir plus), and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.

2025-02-05

ArXiv (prépublication)

Attention as a Hypernetwork

Simon Schug

Seijin Kobayashi

Yassir Akram

João Sacramento

Transformers can under some circumstances generalize to novel problem instances whose constituent parts might have been encountered during t… (voir plus)raining, but whose compositions have not. What mechanisms underlie this ability for compositional generalization? By reformulating multi-head attention as a hypernetwork, we reveal that a composable, low-dimensional latent code specifies key-query specific operations. We find empirically that this latent code is predictive of the subtasks the network performs on unseen task compositions, revealing that latent codes acquired during training are reused to solve unseen problem instances. To further examine the hypothesis that the intrinsic hypernetwork of multi-head attention supports compositional generalization, we ablate whether making the hypernetwork-generated linear value network nonlinear strengthens compositionality. We find that this modification improves compositional generalization on abstract reasoning tasks. In particular, we introduce a symbolic version of the Raven's Progressive Matrices human intelligence test, which gives us precise control over the problem compositions encountered during training and evaluation. We demonstrate on this task how scaling model size and data enables compositional generalization in transformers and gives rise to a functionally structured latent space.

2024-12-31

ICLR (publié)

Hadamard product in deep learning: Introduction, Advances and Challenges.

Grigorios G Chrysos

Yongtao Wu

Philip Torr

Volkan Cevher

2024-12-31

IEEE Transactions on Pattern Analysis and Machine Intelligence (publié)

Meta-learning how to Share Credit among Macro-Actions

Ionel-Alexandru Hosu

Traian Rebedea

One proposed mechanism to improve exploration in reinforcement learning is through the use of macro-actions. Paradoxically though, in many s… (voir plus)cenarios the naive addition of macro-actions does not lead to better exploration, but rather the opposite. It has been argued that this was caused by adding non-useful macros and multiple works have focused on mechanisms to discover effectively environment-specific useful macros. In this work, we take a slightly different perspective. We argue that the difficulty stems from the trade-offs between reducing the average number of decisions per episode versus increasing the size of the action space. Namely, one typically treats each potential macro-action as independent and atomic, hence strictly increasing the search space and making typical exploration strategies inefficient. To address this problem we propose a novel regularization term that exploits the relationship between actions and macro-actions to improve the credit assignment mechanism by reducing the effective dimension of the action space and, therefore, improving exploration. The term relies on a similarity matrix that is meta-learned jointly with learning the desired policy. We empirically validate our strategy looking at macro-actions in Atari games, and the StreetFighter II environment. Our results show significant improvements over the Rainbow-DQN baseline in all environments. Additionally, we show that the macro-action similarity is transferable to related environments. We believe this work is a small but important step towards understanding how the similarity-imposed geometry on the action space can be exploited to improve credit assignment and exploration, therefore making learning more effective.

2024-12-31

arXiv (prépublication)

Reza Babanezhad Harikandeh

openreview.net

Torque-Aware Momentum

Pranshu Malviya

Goncalo Mordido

Aristide Baratin

Gintare Karolina Dziugaite

Sarath Chandar

Efficiently exploring complex loss landscapes is key to the performance of deep neural networks. While momentum-based optimizers are widely … (voir plus)used in state-of-the-art setups, classical momentum can still struggle with large, misaligned gradients, leading to oscillations. To address this, we propose Torque-Aware Momentum (TAM), which introduces a damping factor based on the angle between the new gradients and previous momentum, stabilizing the update direction during training. Empirical results show that TAM, which can be combined with both SGD and Adam, enhances exploration, handles distribution shifts more effectively, and improves generalization performance across various tasks, including image classification and large language model fine-tuning, when compared to classical momentum-based optimizers.

2024-12-24

ArXiv (prépublication)

Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset

Alexandre Galashov

Michalis K. Titsias

Andr'as Gyorgy

Clare Lyle

Yee Whye Teh

Maneesh Sahani

Neural networks are traditionally trained under the assumption that data come from a stationary distribution. However, settings which violat… (voir plus)e this assumption are becoming more popular; examples include supervised learning under distributional shifts, reinforcement learning, continual learning and non-stationary contextual bandits. In this work we introduce a novel learning approach that automatically models and adapts to non-stationarity, via an Ornstein-Uhlenbeck process with an adaptive drift parameter. The adaptive drift tends to draw the parameters towards the initialisation distribution, so the approach can be understood as a form of soft parameter reset. We show empirically that our approach performs well in non-stationary supervised and off-policy reinforcement learning settings.

2024-11-05

ArXiv (prépublication)