24 citations · 52 across the 8 of their papers we have counts for
9 papers · 1 filter
Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning
Matheus R. F. Mendonça, André M. S. Barreto, Artur Ziviani
Network seeding for efficient information diffusion over time-varying graphs~(TVGs) is a challenging task with many real-world applications. There are several ways to model this sp…
The Value Equivalence Principle for Model-Based Reinforcement Learning
Christopher Grimm, André Barreto, Satinder Singh +1
Learning models of the environment from data is often viewed as an essential component to building intelligent reinforcement learning (RL) agents. The common practice is to separat…
Expected Eligibility Traces
Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel +3
The question of how to determine which states and actions are responsible for a certain outcome is known as the credit assignment problem and remains a central research question in…
Temporally-Extended ε-Greedy Exploration
Will Dabney, Georg Ostrovski, André Barreto
Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly complex solutions to the problem. This increase in complexity often comes at the expen…
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning
Will Dabney, André Barreto, Mark Rowland +4
In value-based reinforcement learning (RL), unlike in supervised learning, the agent faces not a single, stationary, approximation problem, but a sequence of value prediction probl…
Disentangled Cumulants Help Successor Representations Transfer to New Tasks
Christopher Grimm, Irina Higgins, Andre Barreto +5
Biological intelligence can learn to solve many diverse tasks in a data efficient manner by re-using basic knowledge and skills from one task to another. Furthermore, many of such…