Harley Wiltzer

The Geometry and Topology of Modular Addition Representations

Gabriela Moisescu-Pareja

Colin Daniels

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)

Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning

Yash Jhaveri

Patrick Shafto

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.

2025-10-09

ArXiv (prépublication)

Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning

Yash Jhaveri

Patrick Shafto

2025-10-09

ArXiv (prépublication)

Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning

Yash Jhaveri

Patrick Shafto

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)

Zero-Shot Constraint Satisfaction with Forward- Backward Representations

Adriana Hugessen

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

2025-07-01

rl-conference.cc/RLC/2025/Workshop/RLBrew (publié)

Tractable Representations for Convergent Approximation of Distributional HJB Equations

Julie Alhosh

2025-03-07

ArXiv (prépublication)

Tractable Representations for Convergent Approximation of Distributional HJB Equations

Julie Alhosh

2025-03-01

arXiv (publié)

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching

Sanjiban Choudhury

2025-01-22

ICLR.cc/2025/Conference (poster)

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.

2024-11-11

ArXiv (prépublication)

Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning