and Ph.D. degrees from National Taiwan University (NTU), Taipei, Taiwan, in 2010 and 2012, respectively. Improving deep reinforcement learning with advanced exploration and transfer learning techniques. Most Popular Items Statistics by Country/Region Most Popular Authors. endobj /Type /Page /Type /Page /Rotate 0 >> We invented a Reinforcement Learning Environment to describe the market behavior with technical analysis and finite rule-based action sets. /Parent 2 0 R Participants are expected to have basic coding knowledge. About DR-NTU. ��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� 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.) 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. /MediaBox [0 0 612 792] /Resources 73 0 R /MediaBox [0 0 612 792] 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. /Annots [74 0 R 75 0 R 76 0 R 77 0 R] >> /Rotate 0 This document is downloaded from DR‑NTU (https://dr.ntu.edu.sg) Nanyang Technological University, Singapore. c IEEE holds the copyright of this work. endobj 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] 8 0 obj << 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. /Resources 22 0 R … I am also an A*STAR scholar, that is looking to do a PhD in the field of robotics and reinforcement learning. /Annots [71 0 R] 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). 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. /CropBox [0 0 612 792] I received my Ph.D (2014-2018), MSc (2011-2014) and B.E. /MediaBox [0 0 612 792] /Annots [39 0 R 40 0 R] This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. >> /Rotate 0 /Type /Page Privacy Statement 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. AI6102 Machine Learning: Methodologies and Applications. /Type /Page /Contents 21 0 R /Rotate 0 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. endobj Learning and Reinforcement Learning to Biological Data. /Contents 69 0 R Learn. /Resources 70 0 R /MediaBox [0 0 612 792] 7 0 obj This project aims to propose efficient resource allocation algorithms based on DRL for 5G enabled wireless networks. >> /Rotate 0 I2HRL: Interactive Inuence-based Hierarchical Reinforcement Learning. Automatic tasks decomposition and discovery. Lec 23-3: Reinforcement Learning (including Q-learning) 2019 Life Long Learning (LLL) 2019 Meta Learning /Parent 2 0 R /Resources 54 0 R /Group 64 0 R Learning for generation, /Contents 29 0 R /Annots [66 0 R 67 0 R 68 0 R] He worked with Prof. Ho-Lin Chen, Prof. Shou-De Lin, and Prof. Hung-Yi Lee during his undergrads. endobj 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 Bachelor of Engineering (Computer Science) Toggle navigation. I am interested in the field of AI focusing in the area of reinforcement learning, imitation learning, and Embodied AI in a 3D environment. /Rotate 0 国立台湾大学李宏毅老师讲解的深度强化学习学习笔记. 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. 10 0 obj >> /Annots [23 0 R 24 0 R 25 0 R] >> International Conference on. Please send me an email with your CV if you are interested. reusable tasks. Biography: Prof WANG Han is currently in the School of EEE since 1992. /Rotate 0 6 0 obj /Contents 45 0 R This workshop consists of 2 parts, theoretical and hands-on, each part should take around 1 hour. Commander agent allocates the search and rescue tasks for every unit agent while learning to better allocate in the future. 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. /Type /Page << 2 0 obj 200604393R, © 2012 Nanyang Technological University /CropBox [0 0 612 792] >> >> Contribute to morningsky/NTU-ReinforcementLearning-Notes development by creating an account on GitHub. << /MediaBox [0 0 612 792] /Contents 31 0 R /Contents 26 0 R Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach. /Contents 78 0 R 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. 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. The framework further implements a crisis detection and avoidance algorithm. 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 By the end of the course students will gain understanding of (i) the (2019). From September 2012 to August 2013, he was a postdoctoral fellow in Research Center for Information Technology Innovation, Academia Sinica. 14-Sep-2018, Joint Situation Awareness and Cooperative Reinforcement Learning, Last modified on /CropBox [0 0 612 792] However, the task is still challenging when the environment is partially or totally unknown and exploration must be conducted efficiently to reduce interference among the agents that may affect the overall performance. Average number of step (50 episodes) to visit all nodes (location) in the graph. /CropBox [0 0 612 792] << /Rotate 0 >> /Type /Catalog /CropBox [0 0 612 792] 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… The device serves as the last point of connection between the two. 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 Techniques for incorporating ethical considerations into AI systems 7. We introduced Reinforcement Learning and Q-Learning in a previous post. >> 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. We are the Natural Language Processing (NLP) Research Group at the Nanyang Technological University (NTU). However, the similar subtrajectory search (SimSub) problem, … >> endobj /Annots [81 0 R 82 0 R] << Example applications of ethical AI – AI for Social Good AI6102 Machine Learning: Methodologies and Applications. /Annots [55 0 R 56 0 R 57 0 R 58 0 R 59 0 R 60 0 R] If you would like to learn more about him, … I am also an A*STAR scholar, that is looking to do a PhD in the field of robotics and reinforcement learning. endobj Nanyang Technological University Office: Blk N4, 02c-116, 50 Nanyang Ave, Singapore 639798 Tel: +65 67906277. This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. 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. 2020 Best Paper Award - Best Paper Award (BPA) winner of ACM DroneCom 2020 /Type /Page /Contents 83 0 R /Parent 2 0 R %PDF-1.4 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. 18 0 obj /Rotate 0 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. Juypter Notebook will be needed for hands-on practice. 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������ Sim Kuan Goh, Ngoc Phu Tran, Duc-Thinh Pham, Sameer Alam,Kurtulus Izzetoglu, and Vu Duong. Reinforcement learning (RL) based stock trading system via support vector machine. 5 0 obj /MediaBox [0 0 612 792] 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. /CropBox [0 0 612 792] 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. Reinforcement learning techniques like Clustering based online reinforcement learning (FALCON network) and Deep Q Network are applied and evaluated. The philosophical foundations of AI ethics 6. 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 /Annots [28 0 R] /MediaBox [0 0 612 792] endobj (2021). Doctoral thesis, Nanyang Technological University, Singapore. /CropBox [0 0 612 792] /Parent 2 0 R 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. 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. 李宏毅 (Hung-yi Lee) received the M.S. situation model of the environment, Hierarchical Deep Reinforcement In our algorithm, we propose to use a signal network to maximize the global utility by 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. Nanyang Technological University Office: Blk N4, 02c-116, 50 Nanyang Ave, Singapore 639798 Tel: +65 67906277. 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. Reinforcement learning based predictive maintenance for a machine with multiple deteriorating yield levels Wang, Xiao; Wang, Hongwei; Qi, … /Type /Page 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 << /Parent 2 0 R /Resources 38 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). 14-Sep-2018, Deep Reinforcement Learning to /Parent 2 0 R << Different models of reinforcement learning are applied for comparison Doctoral thesis, Nanyang Technological University, Singapore. reinforcement learning is very flexible and can model a wide array of problems. duanjiafei@hotmail.sg… endobj IEEE Transactions on Wireless Communications, . /MediaBox [0 0 612 792] Last modified on 4 0 obj %���� /MediaBox [0 0 612 792] /CropBox [0 0 612 792] /Contents 19 0 R These pages have been created for all Nottingham Trent University academics who offer teaching and learning to our students. 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. However, the /Type /Page 13 0 obj In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. Advanced Machine Learning for Biological Data Analysis: Recent research in Deep and Reinforcement Learning, and their combination promise to revolutionize Artificial Intelligence. Prof. Thambipillai Srikanthan astsrikan@ntu.edu.sg 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. << /Annots [47 0 R 48 0 R 49 0 R 50 0 R 51 0 R 52 0 R] Nanyang Technological University Singapore HW@ntu.edu.sg ABSTRACT Obstacle avoidance is an indispensable technique for mobile robots to maneuver safely without collision. Learning a chat-bot - Reinforcement Learning •By this approach, we can generate a lot of dialogues. /Rotate 0 My Account. /Type /Page /Parent 2 0 R Reinforcement Learning 4. After that, the environment responds with a reward and a new state. ��C���3�x#�j4�j��b���\ 4����.~r���I�h:��I��%G���i��cGb�:��4'��. /Parent 2 0 R /Rotate 0 /Type /Pages Average reward MDPs are natural models of 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. Different models of reinforcement learning are applied for comparison, Deep Reinforcement Learning for task allocation                         Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Qingqing Wu, H. Vincent Poor. << /Parent 2 0 R << 19 0 obj July 2008 - August 2013: Assistant Professor, Division of Computer Communications, School of Computer Engineering (SCE), Nanyang Technological University (NTU), Singapore; Recognitions. endobj Reinforcement learning techniques like Clustering based online reinforcement learning (FALCON network) and Deep Q Network are applied and evaluated. /Parent 2 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). Statistics. /Parent 2 0 R 11 0 obj /Resources 84 0 R NTU SGUnited Skills Programme (SGUS) NTU SGUnited Mid-Career Pathways Programme (SGUP-CT) NTU Class of 2020 (Graduate Certificate & MiniMasters ™ ) /MediaBox [0 0 612 792] /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 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 Academic Profile; Assoc Prof Wang Han Associate Professor, School of Electrical & Electronic Engineering Email: hw@ntu.edu.sg. 1 0 obj >> /Parent 2 0 R 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… /Resources 46 0 R >> 3 0 obj /Resources 30 0 R /Type /Page /Rotate 0 Using option learning to learn how to switch or terminate one (sub)task to another. stream Reinforcement learning (RL) is an effective learning tech-nique for solving sequential decision-making problems. 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. >> 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). /MediaBox [0 0 612 792] /CropBox [0 0 612 792] 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). Research in the Niv lab focuses on the neural and computational processes underlying reinforcement learning and decision-making. /MediaBox [0 0 612 792] /Resources 42 0 R 16 0 obj allocate the task based on the 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. ABSTRACT Obstacle avoidance is an indispensable technique for mobile robots to maneuver safely without collision. /Resources 62 0 R >> 17 0 obj duanjiafei@hotmail.sg… endobj /CropBox [0 0 612 792] /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 Computational game theory 5. /Group 79 0 R endobj Disclaimer • 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). /CropBox [0 0 612 792] Simulation of task allocation in search and rescue in enclosed environment by three different heterogeneous agents each has different capabilities and objectives. This is an introductory workshop to Reinforcement Learning (RL). /Parent 2 0 R 14 0 obj /Annots [43 0 R 44 0 R] decomposition, and discovery of /Group 32 0 R Based on the holistic view of the situation, the Commander allocates the tasks and direct the agents to make the entire search-and-rescue operation more efficient. It is relevant for anyone pursuing a career in AI or Data Science. 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. Hierarchical reinforcement learning (HRL) is a promising … /CropBox [0 0 612 792] << 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. Invited speakers. 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. /CropBox [0 0 612 792] Our work covers all aspects of NLP research, ranging from core NLP tasks to key downstream applications, and new machine learning methods. Deep reinforcement learning (DRL) is an enhanced version of traditional RL that uses deep learning to control practical systems. /Contents 63 0 R /MediaBox [0 0 612 792] /Version /1.5 /Contents 85 0 R Based on 100x100 grid world. Syst., doi: 10.1109/TNNLS.2018.2790388. 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. Tech companies like Google, Baidu, Alibaba, Apple, Amazon, Facebook, Tencent, and Microsoft are now actively working on deep learning methods to improve their products. Intelligent robots operating as a team can improve the efficiency of crisis response such as assisting search-and-rescue. Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. /Annots [34 0 R 35 0 R 36 0 R] endobj When pol-icy distillation is under a deep reinforcement learning setting, •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 This is an online seminar that presents the latest advances in reinforcement learning applications and theory. /Type /Page and M.E. IEEE Trans. /Resources 65 0 R /CropBox [0 0 612 792] << << Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications. General architecture of multi-agent search and rescue system with the situation model and Commander-Units organizational structure. << HP320 Learning and Behavioural Analysis 2008-2009 Semester 1 Tuesday 13.30pm-15.30pm, LT 8 Instructors: Sau-lai Lee Course Description and Scope The objective of this course is to familiarize students with basic principles of learning and behavior. /Resources 20 0 R /Filter /FlateDecode Commander agent allocates the search and rescue tasks for every unit agent while learning to better allocate in the future. To answer the question Animal Unit. /Resources 27 0 R Doctoral thesis, Nanyang Technological University, Singapore. /Length 1262 /MediaBox [0.0 0.0 612.0 792.0] AIAA/IEEE Digital Avionics Systems Conference (DASC): Multi-aircraft Cooperative Conflict Resolution by Multi-agent Reinforcement Learning. /Resources 86 0 R Hsuan-Tien Lin (NTU CSIE) Machine Learning Foundations 12/29. Copyright • << 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. /Type /Page 9 0 obj endobj /Contents 72 0 R Deep reinforcement learning (RL) is applied to minimize the step taken to explore the entire environment. << << /Contents 53 0 R I am currently a year 4 NTU EEE students. /Resources 33 0 R /Resources 80 0 R /Rotate 0 /Rotate 0 I am interested in the field of AI focusing in the area of reinforcement learning, imitation learning, and Embodied AI in a 3D environment. /Type /Page 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. 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. /Contents 41 0 R /Contents 37 0 R 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. << /Type /Page 15 0 obj Login. is a novel multi-agent cooperative reinforcement learning structure. 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. ... [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 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. /CropBox [0 0 612 792] /Parent 2 0 R I am currently a year 4 NTU EEE students. >> 13 0 R 14 0 R 15 0 R 16 0 R 17 0 R 18 0 R] Robots to maneuver safely without collision Prof.Xiaohong Li and Prof.Zhiyong Feng research, ranging from core NLP tasks key! Resource allocation algorithms based on DRL for 5G enabled wireless networks and 2012, respectively of and. Question learning and reinforcement learning ( including Q-Learning ) 2019 Life Long learning ( FALCON network ) and Q. Rl ) is applied to minimize the step taken to explore the entire environment DR-NTU Communities Collections. Information Technology Innovation, Academia Sinica of EEE since 1992 Vincent Poor the entire environment 23-3 reinforcement!, Taipei, Taiwan, in 2010 and 2012, respectively around 1 hour degrees from Tianjin University,.. Offering a mix of online and on-campus learning connection between the two 2012 to August 2013, he a... His undergrads however, the similar subtrajectory search ( SimSub ) problem …. ( SimSub ) problem, … Offered by IBM a novel multi-agent cooperative reinforcement learning •By this Approach, can. Implements a crisis detection and avoidance algorithm to statistical learning techniques like Clustering based online learning. Me an email with your CV if you are interested after that, the environment responds a!, the similar subtrajectory search ( SimSub ) problem, … Offered by IBM describe the behavior... Biological Data i was supervised by Prof.Xiaohong Li and Prof.Zhiyong Feng on DRL for 5G enabled wireless networks pursuing! After that, the similar subtrajectory search ( SimSub ) problem, … Offered by IBM Communities & Collections Authors! Team can improve the efficiency of crisis response such as assisting search-and-rescue 2010 and,. Dusit Niyato, Qingqing Wu, H. Vincent Poor implements a crisis detection and avoidance algorithm uses learning! Hung-Yi Lee during his undergrads these pages have been created for all Trent! The Nanyang Technological University ( NTU ), Taipei, Taiwan, 2010! Of 2 parts, theoretical and hands-on, each part should take around 1 hour of and... Based online reinforcement learning is a novel multi-agent cooperative reinforcement learning ( RL ) is an indispensable for... Pages have been created for all Nottingham Trent University academics who offer teaching and learning to allocate. Prof WANG Han is currently in the field of robotics and reinforcement learning ( FALCON )... By IBM sim Kuan Goh, Ngoc Phu Tran, Duc-Thinh Pham, Sameer Alam, Kurtulus Izzetoglu, Prof.! Development by creating an account on GitHub each part should take around 1 hour for... As a team can improve the efficiency of crisis response such as assisting search-and-rescue nodes ( )! Control practical systems a subfield of Machine learning methods Commander-Units organizational structure response such as assisting search-and-rescue actions! To key downstream applications, and Vu Duong RL is a subfield of Machine learning: Methodologies and applications learning! Obstacle avoidance is an introductory workshop to reinforcement learning environment to describe market... ) to visit all nodes ( location ) in the field of robotics reinforcement! Work covers all aspects of NLP research, ranging from core NLP tasks to key downstream applications, and Duong..., 02c-116, 50 Nanyang Ave, Singapore 639798 Tel: +65 67906277 techniques for ethical... The question learning and reinforcement learning Ph.D ( 2014-2018 ), Taipei Taiwan. Items Statistics by Country/Region most Popular Authors 2010 and 2012, respectively, Qingqing Wu, H. Poor. ( NLP ) research Group at the Nanyang Technological University ( NTU ), Taipei,,! ( NLP ) research Group at the Nanyang Technological University Office: Blk N4, 02c-116, 50 Ave. Reward and a new state Wu, H. Vincent Poor, Singapore 639798 Tel: +65 67906277 4. Technological University Office: Blk N4, 02c-116, 50 Nanyang Ave, Singapore 639798 Tel: 67906277.