498 citations · 924 across the 7 of their papers we have counts for
8 papers
Learning to Cooperate with Unseen Agent via Meta-Reinforcement Learning
Rujikorn Charakorn, Poramate Manoonpong, Nat Dilokthanakul
Ad hoc teamwork problem describes situations where an agent has to cooperate with previously unseen agents to achieve a common goal. For an agent to be successful in these scenario…
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result
Maytus Piriyajitakonkij, Sirawaj Itthipuripat, Theerawit Wilaiprasitporn +1
Supervised deep convolutional neural networks (DCNNs) are currently one of the best computational models that can explain how the primate ventral visual stream solves object recogn…
MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
Phairot Autthasan, Rattanaphon Chaisaen, Thapanun Sudhawiyangkul +7
Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded…
MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning
Nannapas Banluesombatkul, Pichayoot Ouppaphan, Pitshaporn Leelaarporn +8
Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the…
An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection
Rujikorn Charakorn, Yuttapong Thawornwattana, Sirawaj Itthipuripat +3
Visual data can be understood at different levels of granularity, where global features correspond to semantic-level information and local features correspond to texture patterns.…
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
Nat Dilokthanakul, Christos Kaplanis, Nick Pawlowski +1
The problem of sparse rewards is one of the hardest challenges in contemporary reinforcement learning. Hierarchical reinforcement learning (HRL) tackles this problem by using a set…
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo +4
We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep gene…
Classifying Options for Deep Reinforcement Learning
Kai Arulkumaran, Nat Dilokthanakul, Murray Shanahan +1
In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the…