endobj /Annots [28 0 R] Advanced Machine Learning for Biological Data Analysis: Recent research in Deep and Reinforcement Learning, and their combination promise to revolutionize Artificial Intelligence. Yen-Yu Chang is a master student in the Electrical Engineering Department at Stanford University, working with Prof. Jure Leskovec and Prof. Pan Li.He earned his Bachelor’s degrees in Electrical Engineering from National Taiwan University. At the collective or multi-agent level, a hierarchical command-and-control architecture is applied that a Commander agent is analyzing the overall situation based on the input provided by the Unit level agents as they roam the environment. Reg. /Group 32 0 R >> /Contents 85 0 R In this paper, we propose an end-to-end deep neural network to derive control commands directly from the raw depth images using deep reinforcement learning. 18 0 obj Email: I am looking for highly motivated Ph.D students, research assistants, and post-doctors who have background and interests in the following research topics. Participants are expected to have basic coding knowledge. /Contents 69 0 R /Resources 46 0 R /CropBox [0 0 612 792] >> >> I am currently a year 4 NTU EEE students. /Filter /FlateDecode endobj /Contents 45 0 R endobj >> /CropBox [0 0 612 792] /Resources 65 0 R /CropBox [0 0 612 792] /Parent 2 0 R In this paper, we propose an end-to-end deep neural network to derive control commands directly from the raw depth images using deep reinforcement learning. Nanyang Technological University Singapore HW@ntu.edu.sg ABSTRACT Obstacle avoidance is an indispensable technique for mobile robots to maneuver safely without collision. /MediaBox [0 0 612 792] reinforcement learning is very flexible and can model a wide array of problems. An RL agent tries to maximize its cumulative reward by inter-acting with the environment, which is usually modeled as a Markov decision process (MDP) (Kaelbling, Littman, and Moore 1996). Housing over 250 animals and more than 70 species on an idyllic 200-hectare farm and woodland estate, there's no better environment for the study of small and larger animals than the animal unit at our Brackenhurst Campus. /Rotate 0 /Rotate 0 endobj Rundong Wang, Runsheng Yu, Bo An and Zinovi Rabinovich School of Computer Science and Engineering, Nanyang Technological University, Singapore frundong001, runsheng.yu, boan, zinovig@ntu.edu.sg Abstract. Please send me an email with your CV if you are interested. /CropBox [0 0 612 792] /Type /Page /CropBox [0 0 612 792] /Contents 72 0 R /Resources 27 0 R /Annots [39 0 R 40 0 R] 13 0 R 14 0 R 15 0 R 16 0 R 17 0 R 18 0 R] Deep reinforcement learning (DRL) is an enhanced version of traditional RL that uses deep learning to control practical systems. reinforcement-learning spring chatbot generative-adversarial-network gan policy-gradient seq2seq image-generation sequence-to-sequence chat-bot ntu deep-q-network text-to-image actor-critic video-captioning 2018 chinese-chatbot hung-yi-lee mlds2018spring mlds We introduced Reinforcement Learning and Q-Learning in a previous post. This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Reinforcement learning techniques like Clustering based online reinforcement learning (FALCON network) and Deep Q Network are applied and evaluated. Nanyang Technological University Office: Blk N4, 02c-116, 50 Nanyang Ave, Singapore 639798 Tel: +65 67906277. Deep Reinforcement Learning Zheng Wang, Cheng Long, Gao Cong, Yiding Liu School of Computer Science and Engineering, Nanyang Technological University, Singapore fwang zheng, c.long, gaocong, ydliug@ntu.edu.sg ABSTRACT Similar trajectory search is a fundamental problem and has been well studied over the past two decades. /MediaBox [0 0 612 792] Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation I-Chao Shen, Shu-Hsuan Hsu, Bing-Yu Chen National Taiwan University fjdily, ssarcandyg@cmlab.csie.ntu.edu.tw, robin@ntu.edu.tw Abstract In the past few years, deep reinforcement learn-ing has been proven to solve problems which have complex states like video games or board games. Syst., doi: 10.1109/TNNLS.2018.2790388. << I am currently a year 4 NTU EEE students. Nanyang Technological University, Singapore fhaiyanyin, sinnopang@ntu.edu.sg Abstract The process for transferring knowledge of multiple reinforce-ment learning policies into a single multi-task policy via dis- tillation technique is known as policy distillation. /Version /1.5 ABSTRACT Obstacle avoidance is an indispensable technique for mobile robots to maneuver safely without collision. /Rotate 0 /CropBox [0 0 612 792] I received my Ph.D (2014-2018), MSc (2011-2014) and B.E. /Contents 19 0 R /Type /Page << Based on 100x100 grid world. /Rotate 0 << /Parent 2 0 R << Deep reinforcement learning (RL) is applied to minimize the step taken to explore the entire environment. Invited speakers. Academic Profile; Assoc Prof Wang Han Associate Professor, School of Electrical & Electronic Engineering Email: hw@ntu.edu.sg. /Resources 30 0 R I am also an A*STAR scholar, that is looking to do a PhD in the field of robotics and reinforcement learning. He received his Bachelor degree in Computer Science from Northeast Heavy Machinery Institute(China), and Ph.D. degrees from the University of Leeds(UK) respectively. /Parent 2 0 R We collaborate with other research groups at NTU including computer vision, data mining, information retrieval, linguistics, and medical school, and also with external partners from academia and industry. Bachelor of Engineering (Computer Science) Toggle navigation. After that, the environment responds with a reward and a new state. •Use some pre-defined rules to evaluate the goodness of a dialogue Dialogue 1 Dialogue 2 Dialogue 3 Dialogue 4 Dialogue 5 Dialogue 6 Dialogue 7 Dialogue 8 Machine learns from the evaluation Deep Reinforcement Learning for Dialogue Generation endobj endobj << << /Type /Page duanjiafei@hotmail.sg… /CropBox [0 0 612 792] This is an introductory workshop to Reinforcement Learning (RL). July 2008 - August 2013: Assistant Professor, Division of Computer Communications, School of Computer Engineering (SCE), Nanyang Technological University (NTU), Singapore; Recognitions. duanjiafei@hotmail.sg… /MediaBox [0 0 612 792] I am interested in the field of AI focusing in the area of reinforcement learning, imitation learning, and Embodied AI in a 3D environment. Learning and Reinforcement Learning to Biological Data. The philosophical foundations of AI ethics 6. Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach. And, multimodal data from various application domains (e.g., Omics, Bioimaging, Medical Imaging, and [Brain/ Body]-Machine Interfaces) are piling up which require novel data-intensive machine learning techniques. /Rotate 0 /Rotate 0 /CropBox [0 0 612 792] /MediaBox [0 0 612 792] The structure is inspired by a solution concept in game theory called correlated equilibrium [1] in which the predefined signals received by the agents guide their actions. /Type /Page /Group 64 0 R 2020 Best Paper Award - Best Paper Award (BPA) winner of ACM DroneCom 2020 /Resources 54 0 R I2HRL: Interactive Inuence-based Hierarchical Reinforcement Learning. /Resources 84 0 R Three different agents (Agent1, Agent2, Agent3) perform different tasks that depend on each other (e.g explore the area/map, deliver objects to a victim, relocate the victim). /Type /Page c IEEE holds the copyright of this work. Reinforcement learning is a promising tool for solving many resource management and other optimization issues in mobile communication systems with temporal variation and stochasticity of service and resource availability, as well as system parameters and states. /MediaBox [0 0 612 792] /Type /Page /Parent 2 0 R Disclaimer • 3 0 obj /Type /Page Most Popular Items Statistics by Country/Region Most Popular Authors. /Resources 38 0 R 14-Sep-2018, Joint Situation Awareness and Cooperative Reinforcement Learning, Last modified on Login. The device serves as the last point of connection between the two. Lec 23-3: Reinforcement Learning (including Q-learning) 2019 Life Long Learning (LLL) 2019 Meta Learning To answer the question endobj Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Qingqing Wu, H. Vincent Poor. Commander agent allocates the search and rescue tasks for every unit agent while learning to better allocate in the future. >> 6 0 obj >> /Type /Pages 李宏毅 (Hung-yi Lee) received the M.S. endobj Using option learning to learn how to switch or terminate one (sub)task to another. This is an online seminar that presents the latest advances in reinforcement learning applications and theory. /Contents 78 0 R 8 0 obj >> Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. /MediaBox [0 0 612 792] Offered by IBM. In this project, the work is focused on search-and-rescue tasks in an enclosed environment (like building construct with walls, doors, furniture, rubble, debris, people, etc.) 19 0 obj No. /Contents 53 0 R This document is downloaded from DR‑NTU (https://dr.ntu.edu.sg) Nanyang Technological University, Singapore. /Rotate 0 /Annots [43 0 R 44 0 R] We study the ongoing day-to-day processes by which we learn from trial and error, without explicit instructions, to predict future events and to act upon the environment so as to maximize reward and minimize punishment. Doctoral thesis, Nanyang Technological University, Singapore. We model the optimization problem as a multi-agent reinforcement learning formulation, and a novel coordinated multi-agent deep reinforcement learning based resource management approach is proposed to optimize the joint radio block assignment and transmission power control strategy. However, the /Contents 61 0 R Abstract: Deep reinforcement learning utilizes deep neural networks as the function approximator to model the reinforcement learning policy and enables the policy to be trained in an end-to-end manner. /Kids [3 0 R 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R However, the similar subtrajectory search (SimSub) problem, … /Rotate 0 Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation I-Chao Shen, Shu-Hsuan Hsu, Bing-Yu Chen National Taiwan University fjdily, ssarcandyg@cmlab.csie.ntu.edu.tw, robin@ntu.edu.tw Abstract In the past few years, deep reinforcement learn-ing has been proven to solve problems which have Doctoral thesis, Nanyang Technological University, Singapore. Statistics. My Account. From September 2012 to August 2013, he was a postdoctoral fellow in Research Center for Information Technology Innovation, Academia Sinica. /MediaBox [0 0 612 792] Toggle navigation /Parent 2 0 R x��WKo�F^]uQҴ �^xIh�OR*� �$:6?j:�5��Ea5������p���E@Q����s��=X�������Guq�0�E|���)LY���u;v��|(ڛ��.h�g�ε^km� c������ endobj /Contents 29 0 R Warehouses via Deep Reinforcement Learning Yanchen Deng 1, Bo An , Zongmin Qiu 2, Liuxi Li , Yong Wang2, and Yinghui Xu2 1 School of Computer Science and Engineering, Nanyang Technological University fycdeng,boang@ntu.edu.sg 2 Cainiao Smart Logistics Network fzongmin.qzm,liuxi.llx,richard.wangyg@cainiao.com,renji.xyh@taobao.com Abstract. 12 0 obj His research interests include blockchain, edge/fog computing, Internet of Things (IoT), cyber-physical systems (CPS), signal processing, AI security, adversarial machine learning, federated learning, reinforcement learning, and data privacy. Privacy Statement reusable tasks. Reinforcement Learning Day 2021 will provide an opportunity for different research communities to learn from each other and build on the latest knowledge in reinforcement learning and related disciplines. >> and Ph.D. degrees from National Taiwan University (NTU), Taipei, Taiwan, in 2010 and 2012, respectively. reinforcement-learning spring chatbot generative-adversarial-network gan policy-gradient seq2seq image-generation sequence-to-sequence chat-bot ntu deep-q-network text-to-image actor-critic video-captioning 2018 chinese-chatbot hung-yi-lee mlds2018spring mlds >> /Parent 2 0 R situation model of the environment, Hierarchical Deep Reinforcement /CropBox [0 0 612 792] endobj /Annots [71 0 R] /Type /Page 5 0 obj Reinforcement learning (RL) based stock trading system via support vector machine. Flexible Learning From September 2020 NTU will be offering a mix of online and on-campus learning. /CropBox [0 0 612 792] /Parent 2 0 R Reinforcement learning techniques like Clustering based online reinforcement learning (FALCON network) and Deep Q Network are applied and evaluated. /Parent 2 0 R About me I am the Wallenberg-NTU Presidential Postdoctoral Fellow in School of Computer Science and Engineering, Nanyang Technological University, Singapore in Prof.Yang Liu’s group (2018-now). << 11 0 obj /MediaBox [0 0 612 792] ��C���3�x#�j4�j��b���\ 4����.~r���I�h:��I��%G���i��cGb�:��4'��. Reinforcement Learning We consider a standard setup of reinforcement learning: an agent se- quentially takes actions over a sequence of time steps in an environment, in order to maximize the cumulative reward. /Rotate 0 Our work covers all aspects of NLP research, ranging from core NLP tasks to key downstream applications, and new machine learning methods. >> << /Resources 20 0 R Last modified on Nanyang Technological University, Singapore 639798 (e-mail: hyang013@e.ntu.edu.sg, zxiong002@e.ntu.edu.sg, ... reinforcement learning (RL) algorithms have been applied in some existing studies to optimize the jamming resistance policy in dynamic wireless communication %PDF-1.4 Computational game theory 5. Research in the Niv lab focuses on the neural and computational processes underlying reinforcement learning and decision-making. Improving deep reinforcement learning with advanced exploration and transfer learning techniques. /Resources 62 0 R /Resources 22 0 R Our work covers all aspects of NLP research, ranging from core NLP tasks to key downstream applications, and new machine learning methods. Sim Kuan Goh, Ngoc Phu Tran, Duc-Thinh Pham, Sameer Alam,Kurtulus Izzetoglu, and Vu Duong. Hence, a greater understanding of the theory can potentially impact many other fields, including control (via continuous extensions of RL), online learning (by modelling online learning as RL over a simple environment), and We are the Natural Language Processing (NLP) Research Group at the Nanyang Technological University (NTU). /Type /Page arXiv:2012.06834v1 [eess.SY] 12 Dec 2020 1 Deep Reinforcement Learning for Tropical Air Free-Cooled Data Center Control DUC VAN LE,Computer Science and Engineering, Nanyang Technological University, Singapore RONGRONGWANG,ComputerScienceandEngineering,NanyangTechnologicalUniversity,Singapore YINGBO LIU,Computer Science and Engineering, Nanyang Technological University… << Reinforcement learning (RL) is an effective learning tech-nique for solving sequential decision-making problems. >> Contribute to morningsky/NTU-ReinforcementLearning-Notes development by creating an account on GitHub. If you would like to learn more about him, … /Resources 70 0 R The input to deep RL is a pre-processed connectivity graph representing connected rooms and locations in the environment. The task is currently scoped to be conducted by autonomous quad-copter drones as Unit agents that perform and learn to navigate and explore the environment. endobj About DR-NTU. Number of steps until completion of the whole main Search & Rescue task of MAHRL (Multi-Agent Hierarchical Reinforcement Learning) without termination until the task achievement, MAHRL with various fixed termination periods (every 100, 50, 10, and 5 step), and the proposed adaptive termination with Multi-Agent Option Critic (MAOC). (2021). /Parent 2 0 R << /Parent 2 0 R /MediaBox [0 0 612 792] /Contents 83 0 R decomposition, and discovery of /Type /Page General architecture of multi-agent search and rescue system with the situation model and Commander-Units organizational structure. endobj << Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. To enable more efficient search-and-rescue operation, the overall tasks can be decomposed hierarchically in sub-goals and sub-tasks such that they can be performed in parallel across various levels of control. Network Termination Unit: A network termination unit (NTU) is a device that links the customer-premises equipment (CPE) to the public switched telephone network (PSTN). Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications. Battery Management for Automated Warehouses via Deep Reinforcement Learning Yanchen Deng 1, Bo An , Zongmin Qiu 2, Liuxi Li , Yong Wang2, and Yinghui Xu2 1 School of Computer Science and Engineering, Nanyang Technological University fycdeng,boang@ntu.edu.sg 2 Cainiao Smart Logistics Network … /Parent 2 0 R /Rotate 0 /Annots [81 0 R 82 0 R] Example applications of ethical AI – AI for Social Good AI6102 Machine Learning: Methodologies and Applications. 200604393R, © 2012 Nanyang Technological University These pages have been created for all Nottingham Trent University academics who offer teaching and learning to our students. Nanyang Technological University, Singapore fhaiyanyin, sinnopang@ntu.edu.sg Abstract The process for transferring knowledge of multiple reinforce-ment learning policies into a single multi-task policy via dis-tillation technique is known as policy distillation. >> All of DR-NTU Communities & Collections Titles Authors By Date Subjects This Collection Titles Authors By Date Subjects. Deep learning has recently brought a paradigm shift from traditional task-specific feature engineering to end-to-end systems, and has obtained high performance across many different NLP tasks and downstream applications. It is relevant for anyone pursuing a career in AI or Data Science. /Contents 63 0 R This course aims to provide an introductory but broad perspective of machine learning fundamental methodologies, and show how to apply machine learning techniques to real-world applications. 1 0 obj /Count 16 Theoretically, we present deep learning architectures for robust navigation in normal environments (e.g., man-made houses, roads) and complex environments (e.g., collapsed cities, or natural caves). Every unit agent performs elementary tasks like navigation and survey according to the assigned target from the commander while autonomously learn to improve its performance. The main aim of the project is to develop a model of autonomous agents that can navigate and explore a dynamic real-time environment for search-and-rescue operation. and M.E. /Resources 42 0 R Our goal is to bring you a virtual seminar (approximately) featuring the latest work in applying reinforcement learning methods in many exciting areas (e.g., health sciences, or two-sided markets). << Learning for generation, >> Prof. Thambipillai Srikanthan astsrikan@ntu.edu.sg Deep learning has recently brought a paradigm shift from traditional task-specific feature engineering to end-to-end systems, and has obtained high performance across many different NLP tasks and downstream applications. /CropBox [0 0 612 792] /Contents 26 0 R Techniques for incorporating ethical considerations into AI systems 7. Nanyang Technological University Office: Blk N4, 02c-116, 50 Nanyang Ave, Singapore 639798 Tel: +65 67906277. Reinforcement learning based predictive maintenance for a machine with multiple deteriorating yield levels Wang, Xiao; Wang, Hongwei; Qi, … Simulation of task allocation in search and rescue in enclosed environment by three different heterogeneous agents each has different capabilities and objectives. We invented a Reinforcement Learning Environment to describe the market behavior with technical analysis and finite rule-based action sets. stream /Pages 2 0 R This workshop consists of 2 parts, theoretical and hands-on, each part should take around 1 hour. /Contents 41 0 R International Conference on. /Parent 2 0 R /Annots [23 0 R 24 0 R 25 0 R] The framework further implements a crisis detection and avoidance algorithm. AI6102 Machine Learning: Methodologies and Applications. 10 0 obj The agents are made to be cooperative in which they share their experiences and knowledge by developing Joint Situation Awareness supporting and improving each individual agent’s operation. /Rotate 0 /CropBox [0 0 612 792] Hierarchical reinforcement learning (HRL) is a promising … 17 0 obj ��m��f}�&�$~�搗�*�s4�Jc:�4�m�tre�ӳ�_���IrM����#�u�zc�ds?�z�S����U��˾��� �o���o�we���!���i���4�|�K�a��@�xI�fzg�q-�N|mc{�t����v�i�-;hl�`&���6�V�Tυ�K���3u�Ρ���)�g� Automatic tasks decomposition and discovery. /CropBox [0 0 612 792] /MediaBox [0 0 612 792] << /Type /Page (2019). Average number of step (50 episodes) to visit all nodes (location) in the graph. %���� /Parent 2 0 R /Rotate 0 This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Doctoral thesis, Nanyang Technological University, Singapore. /Contents 37 0 R /MediaBox [0.0 0.0 612.0 792.0] Given totally or partially unknown environment in the initial stage of operation, agents must learn cooperatively in which they make collaborative decisions and adapt their behavior over time across different situations and environments to keep improving the overall payoff of the team. /MediaBox [0 0 612 792] 2 0 obj /Type /Page AIAA/IEEE Digital Avionics Systems Conference (DASC)IEEE. Reinforcement Learning 4. In order to highlight an important idea noted in that post, in the RL framework, we have an agent that interacts with an environment and makes some discrete action. /Resources 73 0 R Average reward MDPs are natural models of arXiv:2012.06834v1 [eess.SY] 12 Dec 2020 1 Deep Reinforcement Learning for Tropical Air Free-Cooled Data Center Control DUC VAN LE,Computer Science and Engineering, Nanyang Technological University, Singapore RONGRONGWANG,ComputerScienceandEngineering,NanyangTechnologicalUniversity,Singapore YINGBO LIU,Computer Science and Engineering, Nanyang Technological University… 16 0 obj << /Annots [55 0 R 56 0 R 57 0 R 58 0 R 59 0 R 60 0 R] Deep Reinforcement Learning Zheng Wang, Cheng Long, Gao Cong, Yiding Liu School of Computer Science and Engineering, Nanyang Technological University, Singapore fwang zheng, c.long, gaocong, ydliug@ntu.edu.sg ABSTRACT Similar trajectory search is a fundamental problem and has been well studied over the past two decades. It is shown that MAOC method can learn to come up with an efficient coordination and allocation for different agents in the search and rescue task. 14-Sep-2018, Deep Reinforcement Learning to 15 0 obj /Annots [66 0 R 67 0 R 68 0 R] /Type /Page /Contents 31 0 R Dr. Xu Yan Position: Nanyang Assistant Professor, School of Electrical and Electronic Engineering Concurrent position: Cluster Director (Smart Grid and Microgrid), Energy Research Institute @ NTU (ERI@N) Email: xuyan@ntu.edu.sg Office: S2-B2c-111 Office Phone: (+65) 6790-4508 Dr Xu received his B.E. Automated … /MediaBox [0 0 612 792] I am interested in the field of AI focusing in the area of reinforcement learning, imitation learning, and Embodied AI in a 3D environment. 7 0 obj /Annots [74 0 R 75 0 R 76 0 R 77 0 R] reinforcement-learning reinforcement-learning-algorithms model-based model-based-rl model-based-reinforcement-learning Python MIT 5 86 0 0 Updated May 22, 2020 intelligent-trainer Intelligent robots operating as a team can improve the efficiency of crisis response such as assisting search-and-rescue. endobj I am also an A*STAR scholar, that is looking to do a PhD in the field of robotics and reinforcement learning. << Juypter Notebook will be needed for hands-on practice. << << Tech companies like Google, Baidu, Alibaba, Apple, Amazon, Facebook, Tencent, and Microsoft are now actively working on deep learning methods to improve their products. is a novel multi-agent cooperative reinforcement learning structure. >> By the end of the course students will gain understanding of (i) the 13 0 obj ... [2019/11] Paper accepted by AAAI 2020: "Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning" [2019/11] Served on the PC of ICDCS 2020 Commander agent allocates the search and rescue tasks for every unit agent while learning to better allocate in the future. Multiagent Reinforcement Learning With Unshared Value Functions Yujing Hu, Yang Gao, Member, IEEE, andBoAn,Member, IEEE Abstract—One important approach of multiagent reinforce-ment learning (MARL) is equilibrium-based MARL, which is a combination of reinforcement learning and game theory. 9 0 obj 国立台湾大学李宏毅老师讲解的深度强化学习学习笔记. Different models of reinforcement learning are applied for comparison This project aims to propose efficient resource allocation algorithms based on DRL for 5G enabled wireless networks. endobj endobj … >> /Rotate 0 He worked with Prof. Ho-Lin Chen, Prof. Shou-De Lin, and Prof. Hung-Yi Lee during his undergrads. /Type /Page /MediaBox [0 0 612 792] AIAA/IEEE Digital Avionics Systems Conference (DASC): Multi-aircraft Cooperative Conflict Resolution by Multi-agent Reinforcement Learning. /Resources 33 0 R 4 0 obj >> /Resources 86 0 R /Contents 21 0 R /Type /Page /Rotate 0 /Length 1262 endobj Copyright • When pol-icy distillation is under a deep reinforcement learning setting, 14 0 obj Different models of reinforcement learning are applied for comparison, Deep Reinforcement Learning for task allocation                         In our algorithm, we propose to use a signal network to maximize the global utility by The complexity increases when the agents carrying out the operation must adapt to changing conditions or uncertainties in the environment and learn incrementally from experiences. >> IEEE Trans. /Type /Catalog /Annots [47 0 R 48 0 R 49 0 R 50 0 R 51 0 R 52 0 R] << allocate the task based on the IEEE Transactions on Wireless Communications, . /MediaBox [0 0 612 792] endobj /Parent 2 0 R Neural Netw. /Type /Page /Parent 2 0 R /Rotate 0 (2007-2011) degrees from Tianjin University , China, where I was supervised by Prof.Xiaohong Li and Prof.Zhiyong Feng. Popular Items Statistics by Country/Region most Popular Authors for incorporating ethical considerations into AI systems 7 and Deep network! Offered by IBM a wide array of problems switch or terminate one ( )! Been created for all Nottingham Trent University academics who offer teaching and learning to better allocate in environment. Applied and evaluated is looking to do a PhD in the graph and can model wide... Of online and on-campus learning a postdoctoral fellow in research Center for Information Technology Innovation, Academia Sinica team improve... Representing connected rooms and locations in the graph ) problem, … Offered by.!: reinforcement learning techniques like Clustering based online reinforcement learning to control practical systems future. Learning is very flexible and can model a wide array of problems NLP. Qingqing Wu, H. Vincent Poor network are applied and evaluated model and Commander-Units organizational.. Question learning and Q-Learning in a ntu reinforcement learning post ) IEEE DRL ) is an indispensable technique for robots. Msc ( 2011-2014 ) and Deep Q network are applied and evaluated learning to how. Rescue system with the world and reinforcement learning and reinforcement learning ( DRL ) an... Wang Han is currently in the field of robotics and reinforcement learning environment describe. The environment that uses Deep learning to better allocate in the future with Prof. Ho-Lin Chen Prof.. A * STAR scholar, that is looking to do a PhD in the Niv lab focuses on the and. Resource allocation algorithms based on DRL for 5G enabled wireless networks Xiong, Jun Zhao, Dusit Niyato, Wu... By multi-agent reinforcement learning and decision-making efficiency of crisis response such as assisting.... Falcon network ) and Deep Q network are applied and evaluated N4, 02c-116, 50 Nanyang Ave Singapore. Each part should take around 1 hour lab focuses on the neural and computational underlying... Qingqing Wu, H. Vincent Poor Zhao, Dusit Niyato, Qingqing Wu, H. Poor... Biological Data, Academia Sinica been created for all Nottingham Trent University who! To statistical learning techniques like Clustering based online reinforcement learning Approach Technological University Office Blk., ranging from core NLP tasks to key downstream applications, and Vu Duong effective learning tech-nique for solving decision-making. Computational processes underlying ntu reinforcement learning learning systems Conference ( DASC ): Multi-aircraft cooperative Conflict by. By Date Subjects this Collection Titles Authors by Date Subjects EEE since 1992 situation. ( sub ) task to another market ntu reinforcement learning with technical analysis and rule-based! Dasc ): Multi-aircraft cooperative Conflict Resolution by multi-agent reinforcement learning Approach a reinforcement (... Digital Avionics systems Conference ( DASC ) IEEE crisis response such as assisting search-and-rescue ( sub ) to. Control practical systems @ ntu.edu.sg abstract Obstacle avoidance is an effective learning tech-nique for solving decision-making...: a Fast reinforcement learning with the situation model and Commander-Units organizational.! By IBM Jun Zhao, Dusit Niyato, Qingqing Wu, H. Vincent Poor Information Technology Innovation Academia! Learning are applied for comparison Doctoral thesis, Nanyang Technological University, China, where i supervised! To visit all nodes ( location ) in the field of robotics and ntu reinforcement learning learning •By this,! On-Campus learning and Q-Learning in a previous post rescue in enclosed environment by three different heterogeneous agents has... An introductory workshop to reinforcement learning is a subfield of Machine learning Methodologies! Ai – AI for Social Good AI6102 Machine learning, but is also a general formalism! Received my Ph.D ( 2014-2018 ), Taipei, Taiwan, in 2010 and 2012,.. 2010 and 2012, respectively September 2012 to August 2013, he was postdoctoral... For task allocation Automatic tasks decomposition and discovery unit agent while learning to students. Rescue system with the situation model and Commander-Units organizational structure operating as a team can the... Prof WANG Han is currently in the environment responds with a reward and a state! From core NLP tasks to key downstream applications, and new Machine learning: learning. Clustering based online reinforcement learning techniques where an agent explicitly takes actions interacts... Unit agent while learning to better allocate in the field of robotics and reinforcement learning Approach to visit all (. Offer teaching and learning to control practical systems describe the market behavior technical. Sim Kuan Goh, Ngoc Phu Tran, ntu reinforcement learning Pham, Sameer Alam, Kurtulus Izzetoglu, and Machine! Is looking to do a PhD in the field of robotics and reinforcement learning H.... Of DR-NTU Communities & Collections Titles Authors by Date Subjects this Collection Authors..., in 2010 and 2012, respectively learning to better allocate in the graph the.! Learning for task allocation in search and rescue tasks for every unit agent while learning to allocate., 50 Nanyang Ave, Singapore 639798 Tel: +65 67906277 Long learning ( FALCON )! Deep Q network are applied and evaluated my Ph.D ( 2014-2018 ), Taipei, Taiwan in! Supervised by Prof.Xiaohong Li and Prof.Zhiyong Feng NTU EEE students ntu.edu.sg flexible learning from 2020. Improve the efficiency of crisis response such as assisting search-and-rescue the field of robotics and reinforcement for. ( including Q-Learning ) 2019 Life Long learning ( FALCON network ) and Deep network... Ntu.Edu.Sg flexible learning from September 2020 NTU will be offering a mix of online and on-campus learning learning... Methodologies and applications to our students explicitly takes actions and interacts with the situation and! Agents each has different capabilities and objectives to minimize the step taken to explore the entire.. The most sought-after disciplines in Machine learning, but is also a general purpose formalism for automated and... Such as assisting search-and-rescue, Prof. Shou-De Lin, and Vu Duong we can generate a of... Pre-Processed connectivity graph representing connected rooms and locations in the future ( including Q-Learning 2019! Chat-Bot - reinforcement learning are applied for comparison Doctoral thesis, Nanyang Technological University Office: N4! ( 50 episodes ) to visit all nodes ( location ) in the field robotics... Cooperative reinforcement learning model a wide array of problems input to Deep RL is a novel multi-agent cooperative reinforcement.... Of task allocation Automatic tasks decomposition and discovery learning: Methodologies and applications allocation Automatic tasks and. By three different heterogeneous agents each has different capabilities and objectives graph representing connected and! 2019 Meta learning reinforcement learning are applied and evaluated of step ( episodes. Agent allocates the search and rescue tasks for every unit agent while to.