24 citations · 61 across the 9 of their papers we have counts for
7 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…
Temporal Difference Uncertainties as a Signal for Exploration
Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann +7
An effective approach to exploration in reinforcement learning is to rely on an agent's uncertainty over the optimal policy, which can yield near-optimal exploration strategies in…
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…
Approximating Network Centrality Measures Using Node Embedding and Machine Learning
Matheus R. F. Mendonça, André M. S. Barreto, Artur Ziviani
Extracting information from real-world large networks is a key challenge nowadays. For instance, computing a node centrality may become unfeasible depending on the intended central…