143 citations · 308 across the 55 of their papers we have counts for
5 papers · 1 filter
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…
Graph neural induction of value iteration
Andreea Deac, Pierre-Luc Bacon, Jian Tang
Many reinforcement learning tasks can benefit from explicit planning based on an internal model of the environment. Previously, such planning components have been incorporated thro…
TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?
Joshua Romoff, Peter Henderson, David Kanaa +4
We investigate whether Jacobi preconditioning, accounting for the bootstrap term in temporal difference (TD) learning, can help boost performance of adaptive optimizers. Our method…
Policy Evaluation Networks
Jean Harb, Tom Schaul, Doina Precup +1
Many reinforcement learning algorithms use value functions to guide the search for better policies. These methods estimate the value of a single policy while generalizing across ma…
Options of Interest: Temporal Abstraction with Interest Functions
Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert +2
Temporal abstraction refers to the ability of an agent to use behaviours of controllers which act for a limited, variable amount of time. The options framework describes such behav…