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28th Int symposium on Micro-NanoMechatronics and Human Science (MHS) - From Micro and Nano Scale Systems to Robotics and Mechatronics Systems / symposium on Understanding Brain Plasticity on Body Representations to Promote Their adaptive Functions

作者： Sugimoto, Masashi Kurashige, Kentarou Kagawa Coll Natl Inst Technol 551 Kohda Mitoya Kagawa 7691192 Japan Muroran Inst Technol 27-1 Mizumoto Muroran Hokkaido 0508585 Japan

ISBN: (纸本)9781538633151

Over the years, many studies have been conducted with the objective of facilitating the working of robots in dynamic environments. Various robots have been developed to assist humans in workspaces, such as a house or factory. In general, robots are required to work effectively and safely in a dynamic environment to achieve their tasks. However, it is not easy to make a robot behave like a human in dynamic environments. When they are working in a certain environment, humans select an appropriate course of action through subconsciously predicting all the changes in the environment and their next state. For achievement these problems, recent years, various machine learning methods have been suggested. In reinforcement learning, it attracts attention as the technique that often use in actual robot. However, reinforcement learning has some problems. In one of the problems, robot does not copes with changing purpose in reinforcement learning. reinforcement learning has been demanded to achieve various purpose, because what request to robot is diversifying and to achieve various purpose in robot have been wanted, as mentioned above. Therefore, It is important to solve this problem.

关键词： Robots Task analysis learning (artificial intelligence) Atmosphere Electronic mail Production facilities learning systems

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Autonomous Robot Navigation System with learning Based on Deep Q-Network and Topological Maps

Autonomous Robot Navigation System with Learning Based on De...

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ieee/SICE International symposium on System Integration (SII)

作者： Kato, Yuki Kamiyama, Koji Morioka, Kazuyuki Meiji Univ Grad Sch Adv Math Sci Network Design Program Tokyo Japan

ISBN: (纸本)9781538622636

This paper proposes a autonomous mobile robot navigation system integrating local navigation based on deep reinforcement learning and global navigation based on topological maps. Especially, the proposed system aims to achieve a robot navigation that is adaptive with human traffic in environments crowded with many people. In order to obtain abilities for adapting dynamic obstacles in such environments, a simulator imitating real worlds is required for learning. Then a simulator suitable for learning of robot navigation in crowded environments is developed at first. This simulator is specialized in simulation of two-dimensional shape, and it could achieve learning in shorter time than existing simulators. Next, a learning system based on DDQN was designed for local navigation with avoiding pedestrians. Some simulations and experiments were performed to present effectiveness of the proposed navigation system. Simulation results show that the system learned abilities to go to local destinations with avoiding dynamic obstacles Also, the system was applied to an actual robot system and navigation in a real world was demonstrated.

关键词： Navigation Robot kinematics Mobile robots Machine learning Path planning learning systems

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Cache-Enabled adaptive Bit Rate Streaming via Deep Self-Transfer reinforcement learning 10

Cache-Enabled Adaptive Bit Rate Streaming via Deep Self-Tran...

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10th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Zhang, Zhengming Zheng, Yaru Li, Chunguo Huang, Yongming Yang, Luxi Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781538661192

Caching and rate allocation are two promising approaches to support video streaming over wireless networks. However, existing rate allocation designs do not fully exploit the advantages of the two approaches. This paper investigates the problem of cache-enabled video rate allocation. We establish a mathematical model for this problem, and point out that it is difficult to solve it with traditional dynamic programming. Then we propose a deep reinforcement learning approach to solve it. Firstly, we model the problem as a Markov decision problem. Then we present a deep Q-learning algorithm with a special knowledge transfer process to find out an effective allocation policy. Finally, numerical results are given to demonstrate that the proposed solution can effectively maintain high-quality of service. We also investigate the impact of critical parameters on the performance of our algorithm.

关键词： critical parameters performance algorithm

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ADP-based adaptive Optimal Tracking of Strict-feedback Nonlinear Systems

ADP-based Adaptive Optimal Tracking of Strict-feedback Nonli...

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ieee symposium Series on Computational Intelligence (ieee SSCI)

作者： Gao, Weinan Jiang, Zhong-Ping Georgia Southern Univ Coll Engn & Informat Technol Dept Elect Engn Statesboro GA 30460 USA NYU Dept Elect & Comp Engn Tandon Sch Engn Brooklyn NY 11201 USA

ISBN: (纸本)9781538627266

This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. adaptive dynamic programming (ADP) and nonlinear output regulation theories are employed to compute an adaptive near-optimal tracker without a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP.

关键词： Mathematical model adaptive systems Convergence Adaptation models Nonlinear systems learning systems Stability analysis

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Mobile Robot Control Based on Hybrid Neuro-Fuzzy Value Gradient reinforcement learning

Mobile Robot Control Based on Hybrid Neuro-Fuzzy Value Gradi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Al-Dabooni, Seaar Wunsch, Donald Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65401 USA

ISBN: (纸本)9781509061822

This paper uses value gradient learning (VGL) to track a reference trajectory under uncertainties, by computing the optimal left and right torque values for a nonholonomic mobile robot. VGL is a high-performance algorithm in adaptive dynamic programming (ADP). Here, it is used as a critic function after fitting a first-order Sugeno fuzzy neural network (FNN) structure to critic and actor networks. Moreover, this work handles the impacts of unmodeled bounded disturbances with various friction values. The simulation is introduced to compare two approaches. The first uses an actor network that confirms the ability of the mobile robot dynamic model to follow a desired trajectory. This approach demonstrates a significant enhancement of the robot's capability to absorb unstructured disturbance signals and friction effects. The second type of results use a critic-optimal-control approach, calculating the optimal control signal for the affine dynamic model of the robot. This completely removes the actor network to exploit reduced computational complexity with faster responses. The simulation is introduced to compare both cases.

关键词： Nonholonomic dynamic mobile robot fuzzy neural network adaptive dynamic programming

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A Comparison of reinforcement learning Techniques for Fuzzy Cloud Auto-Scaling 17

A Comparison of Reinforcement Learning Techniques for Fuzzy ...

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17th ieee/ACM International symposium on Cluster, Cloud and Grid Computing (CCGRID)

作者： Arabnejad, Hamid Pahl, Claus Jamshidi, Pooyan Estrada, Giovani Dublin City Univ IC4 Dublin Ireland Free Univ Bozen Bolzano Bolzano Italy Imperial Coll London London England Intel Leixlip Ireland

ISBN: (纸本)9781509066117

A goal of cloud service management is to design self-adaptable auto-scaler to react to workload fluctuations and changing the resources assigned. The key problem is how and when to add/remove resources in order to meet agreed service-level agreements. Reducing application cost and guaranteeing service-level agreements (SLAs) are two critical factors of dynamic controller design. In this paper, we compare two dynamic learning strategies based on a fuzzy logic system, which learns and modifies fuzzy scaling rules at runtime. A self-adaptive fuzzy logic controller is combined with two reinforcement learning (RL) approaches: (i) Fuzzy SARSA learning (FSL) and (ii) Fuzzy Q-learning (FQL). As an off-policy approach, Q-learning learns independent of the policy currently followed, whereas SARSA as an on-policy always incorporates the actual agent's behavior and leads to faster learning. Both approaches are implemented and compared in their advantages and disadvantages, here in the OpenStack cloud platform. We demonstrate that both auto-scaling approaches can handle various load traffic situations, sudden and periodic, and delivering resources on demand while reducing operating costs and preventing SLA violations. The experimental results demonstrate that FSL and FQL have acceptable performance in terms of adjusted number of virtual machine targeted to optimize SLA compliance and response time.

关键词： Cloud Computing Orchestration Controller Fuzzy Logic Q-learning SARSA OpenStack

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Analytical Greedy Control and Q-learning for Optimal Power Management of Plug-in Hybrid Electric Vehicles

Analytical Greedy Control and Q-Learning for Optimal Power M...

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ieee symposium Series on Computational Intelligence (ieee SSCI)

作者： Liu, Chang Murphey, Yi Lu Univ Michigan Dearborn Dept Elect & Comp Engn Dearborn MI 48128 USA

ISBN: (纸本)9781538627266

In this paper, we present two solutions for achieving the optimal control of PHEVs on short trips. We prove, mathematically, that a greedy control policy is optimal for those short trips where the battery State-of-Charge (SoC) will not drop below its minimum threshold level. A closed-form greedy control solution is derived from the PHEV powertrain model. Furthermore, we provide a Q-learning based approach which has the capability of in-vehicle learning and is model-free. Our algorithm, combining the Neuro-dynamic programming (NDP) with estimated future trip information, can robustly converge to the optimal policy on both fixed and randomly selected drive cycles.

关键词： Plug-in Hybrid Electric Vehicles (PHEVs) Power Management Energy Optimization reinforcement learning Q-learning

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Energy and Lifetime Optimizations for Dark Silicon Manycore Microprocessor Considering Both Hard and Soft Errors

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ieee TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2017年第9期25卷 2561-2574页

作者： Kim, Taeyoung Sun, Zeyu Chen, Hai-Bao Wang, Hai Tan, Sheldon X-D Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA Shanghai Jiao Tong Univ Dept Micro Nanoelect Engn Shanghai 200240 Peoples R China Univ Elect Sci Technol China Sch Microelect & Solid State Elect Chengdu 610051 Sichuan Peoples R China

In this paper, we propose a new energy and lifetime optimization techniques for emerging dark silicon manycore microprocessors considering both hard long-term reliability effects (hard errors) and transient soft errors, which have been studied less in the past. We consider a recently proposed physics-based electromigration (EM) reliability model to predict the EM-induced reliability. We employ both dynamic voltage and frequency scaling (DVFS) and dark silicon core state using ON/OFF switching action as the two control knobs. We show that on-chip power consumption has different (even contradicting) impacts on soft and hard reliability effects. This paper also shows that soft error should be mitigated by other techniques if aggressive low power and high long-term reliability are pursued. We focus on two optimization techniques for improving lifetime and reducing energy. To optimize EM-induced lifetime, we first apply the adaptive Q-learning-based method, which is suitable for dynamic runtime operation as it can provide cost-effective yet good solutions. The second lifetime optimization approach is the mixed-integer linear programming (MILP) method, which typically yields better solutions but at higher computational costs. To optimize the energy of a dark silicon chip subject to the both hard and soft reliability effects, power budgets, and performance limits, the Q-learning method has been applied as well. A large class of multithreaded applications is used as our benchmarks to validate and compare the proposed dynamic reliability management methods. Experimental results on a 64-core dark silicon chip show that the proposed DRM algorithm can effectively manage and optimize the lifetime of a dark silicon microprocessor under the given power budget and performance limit. Also, the proposed energy optimization can effectively manage and optimize energy consumption subject to both hard and soft-error rates, power budget, and performance limits as constraints. We also show

关键词： Dark Silicon electromigration integrated circuit reliability mircoprocessors optimization Q-learning reinforcement learning reliability semiconductor device reliability

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8th International symposium on Parallel Architectures, Algorithms, and programming, PAAP 2017

8th International Symposium on Parallel Architectures, Algor...

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8th International symposium on Parallel Architectures, Algorithms, and programming, PAAP 2017

ISBN: (纸本)9789811064418

The proceedings contain 57 papers. The special focus in this conference is on Parallel Architectures, Algorithms, and programming . The topics include: Ford motor side-view recognition system based on wavelet entropy and back propagation neural network and levenberg-marquardt algorithm;intrusion detection based on self-adaptive differential evolution extreme learning machine with gaussian kernel;prediction for passenger flow at the airport based on different models;election based pose estimation of moving objects;a novel topology reconfiguration backtracking algorithm for 2d REmesh networks-on-chip;user behaviour authentication model based on stochastic petri net in cloud environment;performance prediction of spark based on the multiple linear regression analysis;exploration of heuristic-based feature selection on classification problems;anti-similarity group shilling attacks;the study of the seabed side-scan acoustic images recognition using bp neural network;node localization of wireless sensor network based on secondary correction error;optimizations of the whole function vectorization based on SIMD characteristics;a stacked denoising autoencoders based collaborative approach for recommender system;research on adaptive canny algorithm based on dual-domain filtering;a dynamic individual recommendation method based on reinforcement learning;research on the pre-distribution model based on seesaw model;an efficient filtration method based on variable-length seeds for sequence alignment;an optimized fusion method for double-wearable-wireless-band platform on cloud-health application;research on concept drift detection for decision tree algorithm in the stream of big data;review of various strategies for gateway discovery mechanisms for integrating internet-MANET.

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS Special Section on Deep reinforcement learning and adaptive dynamic programming

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ieee Transactions on Neural Networks and learning Systems 2017年第4期28卷 1016-1016页

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