Search Results for author: Miguel Vasco

Found 8 papers, 5 papers with code

Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning

1 code implementation12 Oct 2022 Pedro P. Santos, Diogo S. Carvalho, Miguel Vasco, Alberto Sardinha, Pedro A. Santos, Ana Paiva, Francisco S. Melo

We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully perform cooperative tasks with any communication level at execution time by taking advantage of information-sharing among the agents.

Multi-agent Reinforcement Learning reinforcement-learning +1

Perceive, Represent, Generate: Translating Multimodal Information to Robotic Motion Trajectories

no code implementations6 Apr 2022 Fábio Vital, Miguel Vasco, Alberto Sardinha, Francisco Melo

We present Perceive-Represent-Generate (PRG), a novel three-stage framework that maps perceptual information of different modalities (e. g., visual or sound), corresponding to a sequence of instructions, to an adequate sequence of movements to be executed by a robot.

Geometric Multimodal Contrastive Representation Learning

1 code implementation7 Feb 2022 Petra Poklukar, Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva, Danica Kragic

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 of data obtained from different channels.

reinforcement Learning Representation Learning

How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents

1 code implementation7 Oct 2021 Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva

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 under incomplete perceptual conditions.

Atari Games reinforcement-learning +1

Explainable Agency by Revealing Suboptimality in Child-Robot Learning Scenarios

1 code implementation6 Nov 2020 Silvia Tulli, Marta Couto, Miguel Vasco, Elmira Yadollahi, Francisco Melo, Ana Paiva

In the application scenario, the child and the robot learn together how to play a zero-sum game that requires logical and mathematical thinking.

Explanation Generation

Playing Games in the Dark: An approach for cross-modality transfer in reinforcement learning

1 code implementation28 Nov 2019 Rui Silva, Miguel Vasco, Francisco S. Melo, Ana Paiva, Manuela Veloso

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 policies over different subsets of input modalities.

OpenAI Gym reinforcement-learning +1

Learning multimodal representations for sample-efficient recognition of human actions

no code implementations6 Mar 2019 Miguel Vasco, Francisco S. Melo, David Martins de Matos, Ana Paiva, Tetsunari Inamura

In this work we present \textit{motion concepts}, a novel multimodal representation of human actions in a household environment.

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