Portrait de Harley Wiltzer

Harley Wiltzer

Doctorat - McGill
Superviseur⋅e principal⋅e
David Meger
Co-supervisor
Marc Gendron-Bellemare
Sujets de recherche
Apprentissage par renforcement
Modèles probabilistes
Systèmes dynamiques
Site web
Google Scholar
GitHub

Publications

The Geometry and Topology of Circuits: the Manifolds of Modular Addition
Gabriela Moisescu-Pareja
Gavin McCracken
Harley Wiltzer
Vincent Létourneau
Colin Daniels
Doina Precup
Jonathan Love
The Clock and Pizza interpretations, associated with architectures differing in either uniform or learnable attention, were introduced to ar… (voir plus)gue that different architectural designs can yield distinct circuits for modular addition. In this work, we show that this is not the case, and that both the uniform and trainable attention architectures implement the same algorithm via topologically and geometrically equivalent representations. Our methodology goes beyond the interpretation of individual neurons and weights. Instead, we identify all of the neurons corresponding to each learned representation and then study the collective group of neurons as one entity. This method reveals that each learned representation is a manifold that we can study utilizing tools from topology. Based on this insight, we can statistically analyze the learned representations across hundreds of circuits to demonstrate the similarity between learned modular addition circuits that arise naturally from common deep learning paradigms.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
On the geometry and topology of representations: the manifolds of modular addition
Gabriela Moisescu-Pareja
Gavin McCracken
Harley Wiltzer
Vincent Létourneau
Colin Daniels
Doina Precup
Jonathan Love
The Clock and Pizza interpretations, associated with architectures differing in either uniform or learnable attention, were introduced to ar… (voir plus)gue that different architectural designs can yield distinct circuits for modular addition. In this work, we show that this is not the case, and that both uniform attention and trainable attention architectures implement the same algorithm via topologically and geometrically equivalent representations. Our methodology goes beyond the interpretation of individual neurons and weights. Instead, we identify all of the neurons corresponding to each learned representation and then study the collective group of neurons as one entity. This method reveals that each learned representation is a manifold that we can study utilizing tools from topology. Based on this insight, we can statistically analyze the learned representations across hundreds of circuits to demonstrate the similarity between learned modular addition circuits that arise naturally from common deep learning paradigms.
2025-12-30
arXiv (prépublication)
doi.org
openreview.net
The Geometry and Topology of Modular Addition Representations
Gabriela Moisescu-Pareja
Gavin McCracken
Harley Wiltzer
Colin Daniels
Vincent Létourneau
Doina Precup
Jonathan Love
The Clock and Pizza interpretations, associated with neural architectures differing in either uniform or learnable attention, were introduce… (voir plus)d to argue that different architectural designs can yield distinct circuits for modular addition. Applying geometric and topological analyses to learned representations, we show that this is not the case: Clock and Pizza circuits are topologically and geometrically equivalent and are thus equivalent representations.
2025-11-12
TAG-DS/2025/Conference (poster)
openreview.net
openreview.net
Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning
Yash Jhaveri
Harley Wiltzer
Patrick Shafto
Bellemare Marc-Emmanuel
David Meger
In the pursuit of finding an optimal policy, reinforcement learning (RL) methods generally ignore the properties of learned policies apart f… (voir plus)rom their expected return. Thus, even when successful, it is difficult to characterize which policies will be learned and what they will do. In this work, we present a theoretical framework for policy optimization that guarantees convergence to a particular optimal policy, via vanishing entropy regularization and a temperature decoupling gambit. Our approach realizes an interpretable, diversity-preserving optimal policy as the regularization temperature vanishes and ensures the convergence of policy derived objects--value functions and return distributions. In a particular instance of our method, for example, the realized policy samples all optimal actions uniformly. Leveraging our temperature decoupling gambit, we present an algorithm that estimates, to arbitrary accuracy, the return distribution associated to its interpretable, diversity-preserving optimal policy.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
Zero-Shot Constraint Satisfaction with Forward- Backward Representations
Adriana Hugessen
Harley Wiltzer
Cyrus Neary
Amy Zhang
Glen Berseth
Traditionally, constrained policy optimization with Reinforcement Learning (RL) requires learning a new policy from scratch for any new envi… (voir plus)ronment, goal or cost function, with limited generalization to new tasks and constraints. Given the sample inefficiency of many common deep RL methods, this procedure can be impractical for many real-world scenarios, particularly when constraints or tasks are changing. As an alternative, in the unconstrained setting, various works have sought to pre-train representations from offline datasets to accelerate policy optimization upon specification of a reward. Such methods can permit faster adaptation to new tasks in a given environment, dramatically improving sample efficiency. Recently, zero-shot policy optimization has been explored by leveraging a particular
2025-06-30
rl-conference.cc/RLC/2025/Workshop/RLBrew (publié)
openreview.net
openreview.net
Tractable Representations for Convergent Approximation of Distributional HJB Equations
Julie Alhosh
Harley Wiltzer
David Meger
2025-02-28
arXiv (publié)
doi.org
arxiv.org
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
Arnav Kumar Jain
Harley Wiltzer
Jesse Farebrother
Irina Rish
Glen Berseth
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
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning
Harley Wiltzer
Bellemare Marc-Emmanuel
David Meger
Patrick Shafto
Yash Jhaveri
When decisions are made at high frequency, traditional reinforcement learning (RL) methods struggle to accurately estimate action values. In… (voir plus) turn, their performance is inconsistent and often poor. Whether the performance of distributional RL (DRL) agents suffers similarly, however, is unknown. In this work, we establish that DRL agents are sensitive to the decision frequency. We prove that action-conditioned return distributions collapse to their underlying policy's return distribution as the decision frequency increases. We quantify the rate of collapse of these return distributions and exhibit that their statistics collapse at different rates. Moreover, we define distributional perspectives on action gaps and advantages. In particular, we introduce the superiority as a probabilistic generalization of the advantage -- the core object of approaches to mitigating performance issues in high-frequency value-based RL. In addition, we build a superiority-based DRL algorithm. Through simulations in an option-trading domain, we validate that proper modeling of the superiority distribution produces improved controllers at high decision frequencies.
2024-09-24
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Simplifying Constraint Inference with Inverse Reinforcement Learning
Adriana Hugessen
Harley Wiltzer
Glen Berseth
Learning safe policies has presented a longstanding challenge for the reinforcement learning (RL) community. Various formulations of safe RL… (voir plus) have been proposed; However, fundamentally, tabula rasa RL must learn safety constraints through experience, which is problematic for real-world applications. Imitation learning is often preferred in real-world settings because the experts’ safety preferences are embedded in the data the agent imitates. However, imitation learning is limited in its extensibility to new tasks, which can only be learned by providing the agent with expert trajectories. For safety-critical applications with sub-optimal or inexact expert data, it would be preferable to learn only the safety aspects of the policy through imitation, while still allowing for task learning with RL. The field of inverse constrained RL, which seeks to infer constraints from expert data, is a promising step in this direction. However, prior work in this area has relied on complex tri-level optimizations in order to infer safe behavior (constraints). This challenging optimization landscape leads to sub-optimal performance on several benchmark tasks. In this work, we present a simplified version of constraint inference that performs as well or better than prior work across a collection of continuous-control benchmarks. Moreover, besides improving performance, this simplified framework is easier to implement, tune, and more readily lends itself to various extensions, such as offline constraint inference. Our code is made available at https://github.com/ahugs/simple-icrl.
2024-09-24
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Revisiting Successor Features for Inverse Reinforcement Learning
Arnav Kumar Jain
Harley Wiltzer
Jesse Farebrother
Irina Rish
Glen Berseth
Sanjiban Choudhury
2024-06-16
ICML.cc/2024/Workshop/MFHAIA (poster)
openreview.net
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
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
Nate Rahn
Pierluca D'Oro
Harley Wiltzer
Pierre-Luc Bacon
Bellemare Marc-Emmanuel
Deep reinforcement learning agents for continuous control are known to exhibit significant instability in their performance over time. In th… (voir plus)is work, we provide a fresh perspective on these behaviors by studying the return landscape: the mapping between a policy and a return. We find that popular algorithms traverse noisy neighborhoods of this landscape, in which a single update to the policy parameters leads to a wide range of returns. By taking a distributional view of these returns, we map the landscape, characterizing failure-prone regions of policy space and revealing a hidden dimension of policy quality. We show that the landscape exhibits surprising structure by finding simple paths in parameter space which improve the stability of a policy. To conclude, we develop a distribution-aware procedure which finds such paths, navigating away from noisy neighborhoods in order to improve the robustness of a policy. Taken together, our results provide new insight into the optimization, evaluation, and design of agents.
2023-09-20
NeurIPS.cc/2023/Conference (poster)
doi.org
openreview.net