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An extension of weighted strategy sharing in cooperative Q-learning for specialized agents

An extension of weighted strategy sharing in cooperative Q-l...

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International Conference on Neural Information Processing

作者： S.M. Eshgh M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rides for unseen situations. These benefits will be gained if the learning agents know the area of expertise and the expertness values of each other. In this paper, some Q-learning agents with different skills and expertness levels cooperate in learning. The agents use some criteria to judge others information and knowledge. Four expertness criterion, certainty and entropy measures are used to assign degrees of importance to others' Q-Tables. Effects of measuring these values based on their whole Q-Table, a portion of Q-Tables that reflects their proficiencies, and the states in Q-Tables on the learning quality are studied. Simple strategy sharing and two different weighted strategy-sharing methods are used to combine the acquired knowledge from different agents.

关键词： intelligent robots intelligent agent Process control Control systems intelligent control Laboratories intelligent systems Physics Mathematics Entropy

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Data fusion for compliant motion tasks based on human skills

Data fusion for compliant motion tasks based on human skills

引用

IEEE International Workshop on intelligent Robots and systems (IROS)

作者： R. Cortesao R. Koeppe U. Nunes G. Hirzinger Electrical and Computer Engineering Department Institute of Systems and Robotics (ISR) University of Coimbra Coimbra Portugal Institute of Robotics and Mechatronics German Aerospace Center Germany

The paper discusses new developments of the data fusion paradigm due to Cortesao and Koeppe (1999, 2000). A bank of Kalman filters is analyzed in the fusion process. Experiments for a robotic compliant motion task (peg-in-hole) emerged from human skills are reported. Stereo vision and pose sense are fused to execute the task. Feedforward artificial neural networks (ANNs) are trained to transfer human skills to robotic manipulators.

关键词： Humans Sensor phenomena and characterization Stochastic resonance Robot sensing systems State estimation Information filtering Information filters Aerospace engineering Mechatronics Stereo vision

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Entire region filling in indoor environments using neural networks

Entire region filling in indoor environments using neural ne...

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World Congress on intelligent Control and Automation (WCICA)

作者： Chaomin Luo S.X. Yang M. Meng Advanced Robotics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph Guelph Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Entire region filling is a special type of robot path planning strategy that requires the mobile robot to cover every part of the whole workspace, which has many applications such as cleaning robots, vacuum cleaners, painter robots, land mine detectors, lawn mowers, and window cleaners. In this paper, a novel biologically inspired neural network approach is proposed for entire region filling with obstacle avoidance of a mobile cleaning robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally efficient. It can deal with an unstructured environment with irregular obstacles. The effectiveness of the proposed model is demonstrated by simulation results.

关键词： Filling Indoor environments Neural networks Mobile robots Equations Cleaning Landmine detection Neurons Computational modeling Path planning

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Certainty and expertness-based credit assignment for cooperative Q-Learning agents with an AND-type task

Certainty and expertness-based credit assignment for coopera...

引用

International Conference on Neural Information Processing

作者： A. Harati M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence & AI and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

In multiagent reinforcement learning, inter-agent credit assignment is a fundamental problem, since a single scalar reinforcement signal is the only reliable feedback that teams of learning agents receive. This problem is more critical in groups of independent learners with a joint task. In this research, it is assumed that a critic agent receives the environment feedback and assigns a proper credit to each agent using some measures. Three of such measures for a team of cooperative agents with a parallel and AND-type task are introduced. These measures somehow compare the agents' knowledge. One of these criteria, called normal expertness, is a non-relative measure while two other ones (certainty and relative normal expertness) are relative measure. It is experimentally shown that relative measures work better as they contain more information for the critic agent.

关键词： Feedback intelligent agent intelligent robots Process control Control systems intelligent control Artificial intelligence Robot kinematics Physics computing Reliability engineering

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Visual servoing of an omni-directional mobile robot for alignment with parking lot lines

Visual servoing of an omni-directional mobile robot for alig...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： M. Berkemeier M. Davidson V. Bahl Yangquan Chen Lili Ma Center for Self-Organizing and Intelligent Systems Department of Electrical & Computer Engineering Utah State University Logan UT USA

ODIS is an omni-directional mobile robot designed to autonomously or semi-autonomously inspect automobiles in a parking lot. Periodically, its position and orientation references need to be reset. This paper considers visual servoing to parking lot lines as one possible approach. Analysis and simulations demonstrate that a surprisingly simple proportional controller in the image coordinates can accomplish position and orientation alignment with parking lot lines. Unlike previous work, no image Jacobian matrix is necessary. Knowledge of the camera focal length is not required, but the camera and vehicle axes are assumed to be aligned, and the vehicle is assumed to rotate about the camera frame's y-axis.

关键词： Visual servoing Mobile robots Robot kinematics Jacobian matrices Robot vision systems Cameras Sensor arrays Remotely operated vehicles Robot sensing systems intelligent robots

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Spatial integration for a nonlinear path tracking control law

Spatial integration for a nonlinear path tracking control la...

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American Control Conference (ACC)

作者： M. Davidson V. Bahl K.L. Moore Department of Electrical and Computer Engineering Center for Self-organizing and Intelligent Systems Utah State University Logan UT USA

A nonlinear spatial path tracking control law, called the /spl epsi/-controller (C/sub /spl epsi//), was developed for autonomous ground vehicles (AGVs) such as the USU T-series and ODIS (omni-directional inspection system) wheeled mobile robots (WMRs). It is essentially a SISO regulator operating on the normal spatial deviation, /spl epsi/, of the robot from the desired path. As our performance expectation of C/sub /spl epsi// is entirely spatial a logical choice of the regulator should avoid any reference to time. We present a spatial integrator PI (SI-PI) regulator for C/sub /spl epsi// which is devoid of any time references while its structure includes some of the robot dynamics. It is also shown that the control action taken by this regulator represents the work done in moving the robot through an error vector field. The regulator gains are designed using a validated nonlinear Simulink/spl trade//StateFlow model. The experimental results presented show the effectiveness of the proposed SI-PI regulation scheme in the path tracking control of the ODIS WMR.

关键词： Regulators Robot kinematics Mobile robots Ear Orbital robotics Trajectory intelligent systems Automotive engineering Nonlinear control systems Control systems

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Harnessing the nonrepetitiveness in iterative learning control

Harnessing the nonrepetitiveness in iterative learning contr...

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IEEE Conference on Decision and Control

作者： YangQuan Chen K.L. Moore Center for Self-organizing and Intelligent Systems (CSOIS) Department of Electrical and Computer Engineering Utah State University Logan UT USA

ISBN: (纸本)0780375165

In iterative learning control (ILC), it is usually assumed that the disturbances, uncertainties and the desired trajectories are invariant with respect to the iteration number or iteration-independent. In practice, this may not be true. How to accommodate the iteration-dependent disturbances, uncertainties and the desired trajectories is practically important for any successful application of ILC. In practice, it is observed that the baseline performance of ILC is limited mainly by the nonrepetitiveness factors. In this paper, by the proposed two methods, it is shown that one can harness or make use of the nonrepetitiveness in ILC to reduce the baseline errors. When the pattern of the nonrepetitiveness is known, an internal model principle (IMP) in the iteration domain can be applied. When the pattern of the nonrepetitiveness is unknown in advance, a disturbance observer in the iteration domain is proposed. It is noted that to harness the nonrepetitiveness in ILC, usually, the ILC updating law has to be high-order in the iteration direction. To facilitate our discussion, a supervector notion is adopted in a fairly general setting. Simulation examples are provided to illustrate the fact that nonrepetitiveness in ILC, if properly handled, can be harnessed to achieve a better performance previously not achievable.

关键词： Convergence Control systems intelligent systems Educational institutions Uncertainty Adaptive control Error correction Uniform resource locators Feedback control Sampling methods

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An optimal design of PD-type iterative learning control with monotonic convergence

An optimal design of PD-type iterative learning control with...

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IEEE International Symposium on intelligent Control (ISIC)

作者： YangQuan Chen K.L. Moore Department of Electrical and Computer Engineering College of Engineering Center for Self-organizing and Intelligent Systems (CSOIS) Utah State University Logan UT USA

Iterative learning control (ILC) is a technique to make use of the repetitiveness of the tasks a system is commanded to execute in a fixed finite time interval. In this paper, we assume that a measured finite impulse response series of the plant to be controlled is available. We present an optimal design procedure for the commonly used PD-type ILC updating law. The monotonic convergence in a suitable norm topology other than the exponentially weighted sup-norm is emphasized. For practical reasons, an averaged difference formula for a numerical derivative estimate is preferred over the conventional one step backward difference method for smoothing out the high frequency noise. From the analysis, we show a trade-off between noise suppression and the rate of monotonic convergence of the ILC process.

关键词： Optimal control Convergence Design methodology Control systems Three-term control Design engineering Frequency estimation Proportional control Manipulators Adaptive control

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PI-type iterative learning control revisited

PI-type iterative learning control revisited

引用

American Control Conference (ACC)

作者： YangQuan Chen K.L. Moore Department of Electrical and Computer Engineering Center for Self-organizing and Intelligent Systems (CSOIS) College of Engineering Utah State University Logan UT USA

Iterative learning control (ILC) is a value-added block to enhance the feedback control performance by utilizing the fact that the system is operated repeatedly for the same task. In terms of how to use the tracking error signal of previous iteration to form the control signal of current iteration, ILC updating schemes can be classified as P-type, D-type, PI-type and PID type etc. So far, no one answered this question: what's the use of the error integral in ILC updating law? In this paper, for discrete-time linear time invariant systems, we show that the error integral in ILC updating scheme is helpful in achieving a monotonic convergence in a suitable norm topology other than the exponentially weighted sup-norm. Optimal design of PI-type ILC scheme is presented. Two extreme cases are considered to show that PI-type ILC can be better than P-type ILC in terms of convergence speed. Simulation results are presented to illustrate how to optimal design the PI-type ILC. We also show that when the number of time instants in an iteration is large, I-component in ILC updating law is of little use.

关键词： Control systems Convergence Feedback control Pi control Proportional control intelligent systems Educational institutions Time invariant systems Topology intelligent vehicles

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Cooperative Q-learning with heterogeneity in actions

Cooperative Q-learning with heterogeneity in actions

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IEEE International Conference on systems, Man and Cybernetics

作者： S.M. Reza MirFattah M.N. Ahmadabadi Institute for Studies on Theoretical Physics and Mathematics School of Intelligent Systems Tehran Iran Robotics and AI Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Cooperation in learning improves the speed of convergence and the quality of learning. Special care is needed when heterogeneous agents cooperate in learning. It is discussed that, cooperation in learning may cause the learning process to diverge if heterogeneity is not handled properly. In this paper, it is assumed that two heterogeneous Q-learning agents cooperate to learn. The heterogeneity is assumed in their action order (and not in their action set). A Q-learning-based method is introduced for the agents to learn the mapping among their actions. It is shown that, the agents are able to learn this mapping while cooperating in learning. Some simulation results are reported to show the effectiveness of the proposed method.

关键词： intelligent agent intelligent robots Humans Telecommunication control Artificial intelligence Laboratories intelligent systems Physics Mathematics Convergence

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