Search Results for author: Alberto Sardinha

Found 7 papers, 2 papers with code

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.

Onception: Active Learning with Expert Advice for Real World Machine Translation

1 code implementation9 Mar 2022 Vânia Mendonça, Ricardo Rei, Luisa Coheur, Alberto Sardinha

Moreover, since we not know in advance which query strategy will be the most adequate for a certain language pair and set of Machine Translation models, we propose to dynamically combine multiple strategies using prediction with expert advice.

Active Learning Machine Translation +1

Assisting Unknown Teammates in Unknown Tasks: Ad Hoc Teamwork under Partial Observability

no code implementations10 Jan 2022 João G. Ribeiro, Cassandro Martinho, Alberto Sardinha, Francisco S. Melo

In this paper, we present a novel Bayesian online prediction algorithm for the problem setting of ad hoc teamwork under partial observability (ATPO), which enables on-the-fly collaboration with unknown teammates performing an unknown task without needing a pre-coordination protocol.

Understanding the Impact of Data Distribution on Q-learning with Function Approximation

no code implementations23 Nov 2021 Pedro P. Santos, Francisco S. Melo, Alberto Sardinha, Diogo S. Carvalho

Second, we provide a novel four-state MDP that highlights the impact of the data distribution in the performance of a Q-learning algorithm with function approximation, both in online and offline settings.

Q-Learning

A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers

no code implementations24 Jan 2021 Guilherme S. Varela, Pedro P. Santos, Alberto Sardinha, Francisco S. Melo

Our methodology addresses the lack of standardization in the literature that renders the comparison of approaches in different works meaningless, due to differences in metrics, environments, and even experimental design and methodology.

Experimental Design reinforcement-learning

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