5 papers
Implicit Repair with Reinforcement Learning in Emergent Communication
Fábio Vital, Alberto Sardinha, Francisco S. Melo
Conversational repair is a mechanism used to detect and resolve miscommunication and misinformation problems when two or more agents interact. One particular and underexplored form…
Making Friends in the Dark: Ad Hoc Teamwork Under Partial Observability
João G. Ribeiroa, Cassandro Martinhoa, Alberto Sardinhaa +1
This paper introduces a formal definition of the setting of ad hoc teamwork under partial observability and proposes a first-principled model-based approach which relies only on pr…
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…
Learning to Perceive in Deep Model-Free Reinforcement Learning
Gonçalo Querido, Alberto Sardinha, Francisco S. Melo
This work proposes a novel model-free Reinforcement Learning (RL) agent that is able to learn how to complete an unknown task having access to only a part of the input observation.…