14 citations · 33 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…