14 citations · 33 across the 12 of their papers we have counts for
13 papers
Multi-Bellman operator for convergence of -learning with linear function approximation
Diogo S. Carvalho, Pedro A. Santos, Francisco S. Melo
We study the convergence of -learning with linear function approximation. Our key contribution is the introduction of a novel multi-Bellman operator that extends the traditional…
Interactively Teaching an Inverse Reinforcement Learner with Limited Feedback
Rustam Zayanov, Francisco S. Melo, Manuel Lopes
We study the problem of teaching via demonstrations in sequential decision-making tasks. In particular, we focus on the situation when the teacher has no access to the learner's mo…
"Guess what I'm doing": Extending legibility to sequential decision tasks
Miguel Faria, Francisco S. Melo, Ana Paiva
In this paper we investigate the notion of legibility in sequential decision tasks under uncertainty. Previous works that extend legibility to scenarios beyond robot motion either…
Limited depth bandit-based strategy for Monte Carlo planning in continuous action spaces
Ricardo Quinteiro, Francisco S. Melo, Pedro A. Santos
This paper addresses the problem of optimal control using search trees. We start by considering multi-armed bandit problems with continuous action spaces and propose LD-HOO, a limi…
A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers
Guilherme S. Varela, Pedro P. Santos, Alberto Sardinha +1
This article proposes a methodology for the development of adaptive traffic signal controllers using reinforcement learning. Our methodology addresses the lack of standardization i…
A Game AI Competition to foster Collaborative AI research and development
Ana Salta, Rui Prada, Francisco S. Melo
Game AI competitions are important to foster research and development on Game AI and AI in general. These competitions supply different challenging problems that can be translated…
MHVAE: a Human-Inspired Deep Hierarchical Generative Model for Multimodal Representation Learning
Miguel Vasco, Francisco S. Melo, Ana Paiva
Humans are able to create rich representations of their external reality. Their internal representations allow for cross-modality inference, where available perceptions can induce…
Class Teaching for Inverse Reinforcement Learners
Manuel Lopes, Francisco Melo
In this paper we propose the first machine teaching algorithm for multiple inverse reinforcement learners. Specifically, our contributions are: (i) we formally introduce the proble…
Playing Games in the Dark: An approach for cross-modality transfer in reinforcement learning
Rui Silva, Miguel Vasco, Francisco S. Melo +2
In this work we explore the use of latent representations obtained from multiple input sensory modalities (such as images or sounds) in allowing an agent to learn and exploit polic…
Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement Learning
João Ribeiro, Francisco S. Melo, João Dias
In this paper we investigate two hypothesis regarding the use of deep reinforcement learning in multiple tasks. The first hypothesis is driven by the question of whether a deep rei…
Learning multimodal representations for sample-efficient recognition of human actions
Miguel Vasco, Francisco S. Melo, David Martins de Matos +2
Humans interact in rich and diverse ways with the environment. However, the representation of such behavior by artificial agents is often limited. In this work we present \textit{m…
Empathic Robot for Group Learning: A Field Study
Patricia Alves-Oliveira, Pedro Sequeira, Francisco S. Melo +2
This work explores a group learning scenario with an autonomous empathic robot. We address two research questions: (1) Can an autonomous robot designed with empathic competencies f…
Multi-class Generalized Binary Search for Active Inverse Reinforcement Learning
Francisco Melo, Manuel Lopes
This paper addresses the problem of learning a task from demonstration. We adopt the framework of inverse reinforcement learning, where tasks are represented in the form of a rewar…