Nat Dilokthanakul

According to our database, Nat Dilokthanakul authored at least 19 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Hybrid learning mechanisms under a neural control network for various walking speed generation of a quadruped robot.

Yanbin Zhang, Mathias Thor, Nat Dilokthanakul, Zhendong Dai, Poramate Manoonpong

Neural Networks, October, 2023

Generating Diverse Cooperative Agents by Learning Incompatible Policies.

Rujikorn Charakorn, Poramate Manoonpong, Nat Dilokthanakul

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Visual Goal Human-Robot Communication Framework With Few-Shot Learning: A Case Study in Robot Waiter System.

Guntitat Sawadwuthikul, Tanyatep Tothong, Thanawat Lodkaew, Puchong Soisudarat, Sarana Nutanong, Poramate Manoonpong, Nat Dilokthanakul

IEEE Trans. Ind. Informatics, 2022

MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification.

Phairot Autthasan, Rattanaphon Chaisaen, Thapanun Sudhawiyangkul, Phurin Rangpong, Suktipol Kiatthaveephong, Nat Dilokthanakul, Gun Bhakdisongkhram, Huy Phan, Cuntai Guan, Theerawit Wilaiprasitporn

IEEE Trans. Biomed. Eng., 2022

GRAB: GRAdient-Based Shape-Adaptive Locomotion Control.

Sujet Phodapol, Thirawat Chuthong, Binggwong Leung, Arthicha Srisuchinnawong, Poramate Manoonpong, Nat Dilokthanakul

IEEE Robotics Autom. Lett., 2022

2021

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, Payongkit Lakhan, Busarakum Chaitusaney, Nattapong Jaimchariyatam, Ekapol Chuangsuwanich, Wei Chen, Huy Phan, Nat Dilokthanakul, Theerawit Wilaiprasitporn

IEEE J. Biomed. Health Informatics, 2021

Learning to Cooperate with Unseen Agent via Meta-Reinforcement Learning.

Rujikorn Charakorn, Poramate Manoonpong, Nat Dilokthanakul

CoRR, 2021

Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result.

Maytus Piriyajitakonkij, Sirawaj Itthipuripat, Theerawit Wilaiprasitporn, Nat Dilokthanakul

CoRR, 2021

Advanced Collaborative Robots for the Factory of the Future.

Kongkiat Rothomphiwat, Atthanat Harnkhamen, Tanyatep Tothong, Tachadol Suthisomboon, Nat Dilokthanakul, Poramate Manoonpong

Proceedings of the IEEE/SICE International Symposium on System Integration, 2021

Learning to Cooperate with Unseen Agents Through Meta-Reinforcement Learning.

Rujikorn Charakorn, Poramate Manoonpong, Nat Dilokthanakul

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020

MetaSleepLearner: Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning.

Nannapas Banluesombatkul, Pichayoot Ouppaphan, Pitshaporn Leelaarporn, Payongkit Lakhan, Busarakum Chaitusaney, Nattapong Jaimchariyatam, Ekapol Chuangsuwanich, Nat Dilokthanakul, Theerawit Wilaiprasitporn

CoRR, 2020

An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection.

Rujikorn Charakorn, Yuttapong Thawornwattana, Sirawaj Itthipuripat, Nick Pawlowski, Poramate Manoonpong, Nat Dilokthanakul

CoRR, 2020

Dynamical State Forcing on Central Pattern Generators for Efficient Robot Locomotion Control.

Thirawat Chuthong, Binggwong Leung, Kawee Tiraborisute, Potiwat Ngamkajornwiwat, Poramate Manoonpong, Nat Dilokthanakul

Proceedings of the Neural Information Processing - 27th International Conference, 2020

Investigating Partner Diversification Methods in Cooperative Multi-agent Deep Reinforcement Learning.

Rujikorn Charakorn, Poramate Manoonpong, Nat Dilokthanakul

Proceedings of the Neural Information Processing - 27th International Conference, 2020

2019

Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning.

Nat Dilokthanakul, Christos Kaplanis, Nick Pawlowski, Murray Shanahan

IEEE Trans. Neural Networks Learn. Syst., 2019

2018

Towards better data efficiency in deep reinforcement learning.

Nat Dilokthanakul

PhD thesis, 2018

Deep Reinforcement Learning with Risk-Seeking Exploration.

Nat Dilokthanakul, Murray Shanahan

Proceedings of the From Animals to Animats 15, 2018

2016

Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders.

Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew C. H. Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan

CoRR, 2016

Classifying Options for Deep Reinforcement Learning.

Kai Arulkumaran, Nat Dilokthanakul, Murray Shanahan, Anil Anthony Bharath

CoRR, 2016