34 citations · 86 across the 14 of their papers we have counts for
19 papers
The Primacy Bias in Deep Reinforcement Learning
Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro +2
This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. Because of…
Continuous-Time Meta-Learning with Forward Mode Differentiation
Tristan Deleu, David Kanaa, Leo Feng +4
Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm wh…
Neural Algorithmic Reasoners are Implicit Planners
Andreea Deac, Petar Veličković, Ognjen Milinković +3
Implicit planning has emerged as an elegant technique for combining learned models of the world with end-to-end model-free reinforcement learning. We study the class of implicit pl…
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation
Evgenii Nikishin, Romina Abachi, Rishabh Agarwal +1
The shortcomings of maximum likelihood estimation in the context of model-based reinforcement learning have been highlighted by an increasing number of papers. When the model class…
An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning
Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon
How do we formalize the challenge of credit assignment in reinforcement learning? Common intuition would draw attention to reward sparsity as a key contributor to difficult credit…
XLVIN: eXecuted Latent Value Iteration Nets
Andreea Deac, Petar Veličković, Ognjen Milinković +3
Value Iteration Networks (VINs) have emerged as a popular method to incorporate planning algorithms within deep reinforcement learning, enabling performance improvements on tasks r…