14 citations · 17 across the 6 of their papers we have counts for
6 papers
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.…
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning
Pedro P. Santos, Diogo S. Carvalho, Miguel Vasco +4
We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communicati…
Geometric Multimodal Contrastive Representation Learning
Petra Poklukar, Miguel Vasco, Hang Yin +3
Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity…
Learning Collective Action under Risk Diversity
Ramona Merhej, Fernando P. Santos, Francisco S. Melo +2
Collective risk dilemmas (CRDs) are a class of n-player games that represent societal challenges where groups need to coordinate to avoid the risk of a disastrous outcome. Multi-ag…
The Impact of Data Distribution on Q-learning with Function Approximation
Pedro P. Santos, Diogo S. Carvalho, Alberto Sardinha +1
We study the interplay between the data distribution and Q-learning-based algorithms with function approximation. We provide a unified theoretical and empirical analysis as to how…
How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents
Miguel Vasco, Hang Yin, Francisco S. Melo +1
This work addresses the problem of sensing the world: how to learn a multimodal representation of a reinforcement learning agent's environment that allows the execution of tasks un…