Search Results for author: João Pedro Araújo

Found 3 papers, 2 papers with code

NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action

no code implementations CVPR 2023 Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou, João Pedro Araújo, Jeffrey Gu, Karen Liu, Serena Yeung

Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.

3D Reconstruction Human Mesh Recovery +1

Control with adaptive Q-learning

1 code implementation3 Nov 2020 João Pedro Araújo, Mário A. T. Figueiredo, Miguel Ayala Botto

The main difference between AQL and SPAQL is that the latter learns time-invariant policies, where the mapping from states to actions does not depend explicitly on the time step.

OpenAI Gym Q-Learning +1

Single-partition adaptive Q-learning

1 code implementation14 Jul 2020 João Pedro Araújo, Mário Figueiredo, Miguel Ayala Botto

This paper introduces single-partition adaptive Q-learning (SPAQL), an algorithm for model-free episodic reinforcement learning (RL), which adaptively partitions the state-action space of a Markov decision process (MDP), while simultaneously learning a time-invariant policy (i. e., the mapping from states to actions does not depend explicitly on the episode time step) for maximizing the cumulative reward.

Q-Learning Reinforcement Learning (RL)

Cannot find the paper you are looking for? You can Submit a new open access paper.