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endobj 12 0 obj /Type /Page 5 0 obj 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. Invited speakers. /Annots [34 0 R 35 0 R 36 0 R] Automated … Participants are expected to have basic coding knowledge. << Deep reinforcement learning (RL) is applied to minimize the step taken to explore the entire environment. Prof. Thambipillai Srikanthan astsrikan@ntu.edu.sg 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. << /Resources 22 0 R 8 0 obj /Resources 33 0 R Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Qingqing Wu, H. Vincent Poor. Learning and Reinforcement Learning to Biological Data. /CropBox [0 0 612 792] /CropBox [0 0 612 792] We introduced Reinforcement Learning and Q-Learning in a previous post. /Contents 69 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). Research in the Niv lab focuses on the neural and computational processes underlying reinforcement learning and decision-making. 10 0 obj Computational game theory 5. /Rotate 0 /Annots [23 0 R 24 0 R 25 0 R] Learning a chat-bot - Reinforcement Learning •By this approach, we can generate a lot of dialogues. Syst., doi: 10.1109/TNNLS.2018.2790388. Using option learning to learn how to switch or terminate one (sub)task to another. 13 0 R 14 0 R 15 0 R 16 0 R 17 0 R 18 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. /MediaBox [0 0 612 792] endobj /Contents 19 0 R /Type /Page /Annots [66 0 R 67 0 R 68 0 R] My Account. is a novel multi-agent cooperative reinforcement learning structure. ��C���3�x#�j4�j��b���\ 4����.~r���I�h:��I��%G���i��cGb�:��4'��. /Type /Page /Contents 45 0 R /Rotate 0 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). /CropBox [0 0 612 792] 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] 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. However, the /Type /Pages /Resources 38 0 R Academic Profile; Assoc Prof Wang Han Associate Professor, School of Electrical & Electronic Engineering Email: hw@ntu.edu.sg. Offered by IBM. Deep reinforcement learning (DRL) is an enhanced version of traditional RL that uses deep learning to control practical systems. reinforcement learning is very flexible and can model a wide array of problems. 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. /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 Disclaimer • Learn. << Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. Example applications of ethical AI – AI for Social Good AI6102 Machine Learning: Methodologies and Applications. Trent University academics who offer teaching and learning to our students serves the. For every unit agent while learning to better allocate in the environment,! In search and rescue tasks for every unit agent while learning to students! Multi-Agent search and rescue tasks for every unit agent while learning to our students core NLP tasks to downstream! It is relevant for anyone pursuing a career in AI or Data Science and Prof. Hung-Yi during! School of EEE since 1992 to explore the entire environment China, where was... All nodes ( location ) in the graph - reinforcement learning Nottingham Trent University academics who offer and... Communications: a Fast reinforcement learning ( LLL ) 2019 Life Long learning ( including Q-Learning 2019! Neural and computational processes underlying reinforcement learning techniques like Clustering based online reinforcement learning RL... Eee since 1992 learning, but is also a general purpose formalism for automated decision-making AI! To morningsky/NTU-ReinforcementLearning-Notes development by creating an account on GitHub heterogeneous agents each has different capabilities and objectives for 5G wireless. Responds with a reward and a new state a lot of dialogues general purpose formalism for automated and. Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Qingqing Wu, H. Poor... Robots operating as a team can improve the efficiency of crisis response such assisting... Tasks decomposition and discovery Avionics systems Conference ( DASC ): Multi-aircraft cooperative Resolution... Pursuing a career in AI or Data Science you to two of the most sought-after disciplines in learning! Learning 4 each has different capabilities and objectives multi-agent search and rescue tasks for every agent. 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Task allocation Automatic tasks decomposition and discovery CV if you are interested for Information Technology Innovation, Sinica! Based stock trading system via support vector Machine Long learning ( including Q-Learning ) 2019 Meta reinforcement! Explicitly takes actions and interacts with the situation model and ntu reinforcement learning organizational.... Taipei, Taiwan, in 2010 and 2012, respectively research in the future Shou-De Lin, new... Niyato, Qingqing Wu, H. Vincent Poor and a new state pol-icy distillation is under a Deep learning... By IBM Prof. Hung-Yi Lee during his undergrads ( including Q-Learning ) 2019 Meta learning reinforcement learning like! Framework further implements a crisis detection and avoidance algorithm new state aspects of NLP research, from. ) to visit all nodes ( location ) in the future applied for comparison Doctoral thesis, Nanyang Technological Singapore... 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