Book Chapter10.1007/978-3-540-68996-6_14
Parallel Markov Decision Processes
L. Enrique Sucar
- 01 Jan 2007
- pp 295-309
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About: The article was published on 01 Jan 2007. The article focuses on the topics: Markov renewal process & Partially observable Markov decision process.
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Citations
Supplier behavior modeling and winner determination using parallel MDP
TL;DR: A states-space model is proposed to capture the uncertainty involved in long-term supplier behavior and gives better buyer utility, selects best suppliers and fetches better quality product through the efficiency of the MDP-based selection process.
15
Executing concurrent actions with multiple Markov decision processes
Elva Corona-Xelhuantzi,Eduardo F. Morales,Enrique Sucar +2 more
- 15 May 2009
TL;DR: This work proposes a framework based on a functional decomposition of the problem into several sub-problems, each represented as a subMDP, and defines two kinds of conflicts, resource and behavior conflicts, and proposes solutions for both.
Conflict Resolution in Multiagent Systems: Balancing Optimality and Learning Speed
Aaron Rocha-Rocha,Enrique Munoz de Cote,Saul E. Pomares Hernandez,Enrique Sucar Succar +3 more
- 27 Oct 2012
TL;DR: The proposed framework guarantees an optimal solution to the original problem, at the cost of a low learning speed, but can be tuned to balance learning speed and optimality.
4
•Posted Content
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation.
Abhimanyu Dubey,Alex Pentland +1 more
TL;DR: In this paper, the problem of cooperative multi-agent RL with function approximation is discussed, where a group of agents communicate with each other to jointly solve an episodic MDP, and it is shown that via careful message-passing and cooperative value iteration, it is possible to achieve near-optimal no-regret learning with a fixed constant communication budget.
4
References
•Book
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Martin L. Puterman
- 15 Apr 1994
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
12.3K
Markov Decision Processes
P. Whittle,M. L. Puterman +1 more
TL;DR: Markov Decision Processes covers recent research advances in such areas as countable state space models with average reward criterion, constrained models, and models with risk sensitive optimality criteria, and explores several topics that have received little or no attention in other books.
•Book
A robust layered control system for a mobile robot
Rodney A. Brooks
- 01 Jun 1991
TL;DR: A new architecture for controlling mobile robots is described, building a robust and flexible robot control system that has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms.
7.9K
A robust layered control system for a mobile robot
Rodney A. Brooks
- 01 Mar 1986
TL;DR: In this paper, a new architecture for controlling mobile robots is described, which is made up of asynchronous modules that communicate over low-bandwidth channels, each module is an instance of a fairly simple computational machine.
•Book
Introduction to Reinforcement Learning
Richard S. Sutton,Andrew G. Barto +1 more
- 01 Mar 1998
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.
7.7K