7 papers · 1 filter
Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning
Yash Jhaveri, Harley Wiltzer, Patrick Shafto +2
In the pursuit of finding an optimal policy, reinforcement learning (RL) methods generally ignore the properties of learned policies apart from their expected return. Thus, even wh…
VDFD: Multi-Agent Value Decomposition Framework with Disentangled World Model
Zhizun Wang, David Meger
In this paper, we propose a novel model-based multi-agent reinforcement learning approach named Value Decomposition Framework with Disentangled World Model to address the challenge…
Tractable Representations for Convergent Approximation of Distributional HJB Equations
Julie Alhosh, Harley Wiltzer, David Meger
In reinforcement learning (RL), the long-term behavior of decision-making policies is evaluated based on their average returns. Distributional RL has emerged, presenting techniques…
Fairness in Reinforcement Learning with Bisimulation Metrics
Sahand Rezaei-Shoshtari, Hanna Yurchyk, Scott Fujimoto +2
Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environments. By maximizing their reward without co…
Parseval Regularization for Continual Reinforcement Learning
Wesley Chung, Lynn Cherif, David Meger +1
Loss of plasticity, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequences of tasks -- all referring to the incr…
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning
Harley Wiltzer, Marc G. Bellemare, David Meger +2
When decisions are made at high frequency, traditional reinforcement learning (RL) methods struggle to accurately estimate action values. In turn, their performance is inconsistent…