Multi Agent Systems And Applications Iv

Author: Michal Pechoucek
Publisher: Springer Science & Business Media
ISBN: 9783540290469
Size: 79.78 MB
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4th International Central and Eastern European Conference on Multi-Agent
Systems, CEEMAS 2005, Budapest, Hungary, September 15-17, 2005,
Proceedings Michal Pechoucek, Paolo Petta, Laszlo Zsolt Varga. Stochastic
Reactive Production Scheduling by Multi-agent Based Asynchronous
Approximate Dynamic Programming Bal ́azs Csan ́ad Cs ́aji1 and L ́aszl ́o
Monostori1,2 1 Computer and Automation Research Institute, Hungarian
Academy of Sciences 2 Faculty of ...

Multi Agent Systems And Applications

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A closed-loop scheduling technique is presented that on-line controls the
production process. To achieve this, the scheduling problem is reformulated as a
special Markov Decision Process. A near-optimal control policy of the resulted
MDP is calculated in a homogeneous multi-agent system. Each agent applies a
trial-based approximate dynamic programming method. Different cooperation
techniques to distribute the value function computation among the agents are
described.

Innovations In Multi Agent Systems And Application 1

Author: Dipti Srinivasan
Publisher: Springer
ISBN: 3642144357
Size: 79.63 MB
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MIT Press, Cambridge (1999) Peng, J., Williams, R.J.: Incremental multi-step Q-
learning. Machine Learning 22(1–3), 283–290 (1996) Peters, J., Schaal, S.:
Natural actor-critic. Neurocomputing 71(7–9), 1180–1190 (2008) Potter, M.A.,
Jong, K.A.D.: A cooperative coevolutionary approach to function optimization. In:
Davidor, Y., M ̈anner, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp.
249–257. Springer, Heidelberg (1994) Powell, W.B.: Approximate Dynamic
Programming: ...

Agent And Multi Agent Systems Technologies And Applications

Author: James O'Shea
Publisher: Springer Science & Business Media
ISBN: 3642219993
Size: 21.19 MB
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Sutton, R.S., Barto, A.G.: Reinforcement learning - an introduction. MIT Press,
Cambridge (1998) 4. Watkins, C.J.C.H.: Learning from delayed rewards. Ph.D.
thesis. University of Cambridge, England (1989) 5. Watkins, C.J.C.H., Dayan, P.:
Technical note: Q-learning. Machine Learning 8, 279–292 (1992) 6. Cassandras,
C.G., Lafortune, S.: Introduction to discrete event system. Springer, New York (
2008) 7. Werbos, P.J.: Approximate dynamic programming for real-time control
and neural ...

Intelligent Robotics And Applications

Author: YongAn Huang
Publisher: Springer
ISBN: 3319652893
Size: 33.26 MB
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Control 57(5), 1291–1297 (2012) Xie, D., Xu, S., Zhang, B., Li, Y., Chu, Y.:
Cosensus for multi-agent systems with distributed adaptive control and an event-
triggered communication strategy. IET Control Theor. Appl. 10(13), 1547–1555 (
2016) ... Theory, Facts, and Formulas. Princeton University Press, Princeton (
2009) Approximate Dynamic Programming for Relay Deployment in Multi-robot
System Song Event-Triggered Consensus of Neutrally Stable Linear Multi-agent
Systems 647.

Adaptive Dynamic Programming For Control

Author: Huaguang Zhang
Publisher: Springer Science & Business Media
ISBN: 144714757X
Size: 68.28 MB
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Luo YH, Zhang HG, Cao N, Chen B (2009) Near-optimal stabilization for a class
of nonlinear systems with control constraint based on single network greedy
iterative DHP algorithm. ... Neural Netw 19(10):1648–1660 Rantzer A (2005) On
approximate dynamic programming in switching systems. ... Springer, Berlin, pp
540–555 Yang H, Jiang B, Cocquempot V, Zhang HG (2011) Stabilization of
switched nonlinear systems with all unstable modes: application to multi-agent
systems.

Principles Of Practice In Multi Agent Systems

Author: Jung-Jin Yang
Publisher: Springer
ISBN: 3642111610
Size: 24.49 MB
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For example, we should apply RASP-MSE to TD learning and multiagent
learning, which may not follow the assumption of random walk. Also, we need to
utilize higher-order derivatives ... Adaptive stepsizes for recursive estimation with
applications in approximate dynamic programming. Machine learning 65(1), 167
–198 (2006) 4. ... In: The Sixth International Joint Conference on Autonomous
Agents and Multiagent Systems (May 2007) 7. Schoknecht, R., Riedmiller, M.:
Speeding-up ...

Multiagent System Technologies

Author: Jan Ole Berndt
Publisher: Springer
ISBN: 3319647989
Size: 53.48 MB
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In: Approximate Dynamic Programming and Reinforcement Learning, ADPRL
2007 (2007) Hoffman, M., Jasra, A.: Trans-dimensional MCMC for Bayesian
policy learning. Neural Inf. Process. Syst. 20, 1–8 (2008) Buşoniu, L., Babuška, R.
, Schutter, B.: Multi-agent reinforcement learning: an overview. In: Srinivasan, D.,
Jain, L.C. (eds.) Innovations in Multi-Agent Systems and Applications - 1. Studies
in Computational Intelligence, vol. 310, pp. 183–221. Springer, Berlin,
Heidelberg ...

Value Based Planning For Teams Of Agents In Stochastic Partially Observable Environments

Author: Frans Oliehoek
Publisher: Amsterdam University Press
ISBN: 9056296108
Size: 74.39 MB
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Multi-Agent Programming: Languages, Platforms and Applications. Springer,
2005. A. Boularias and B. Chaib-draa. Exact dynamic programming for
decentralized POMDPs with lossless policy compression. In Proc. of the
International Conference on Automated Planning and Scheduling, 2008. ... An
approximate dynamic programming approach to decentralized control of
stochastic systems. In Proc. of the 2004 Allerton Conference on Communication,
Control, and Computing, 2004.

Reinforcement Learning And Approximate Dynamic Programming For Feedback Control

Author: Frank L. Lewis
Publisher: John Wiley & Sons
ISBN: 1118453972
Size: 67.78 MB
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In practice, thereare two general typesof multiagent systems: systems which use
distributed controlto maximizesome kind ofglobal performance of the systemas a
whole, systems truly intendedto balance the goals of different humans, with
different goals. For distributed ... Insome applications, like electric power [10], it
has become fashionable to assign a complex decision problemto a large number
ofindependent agents, commonly agents trained by reinforcement learning. It is
common ...