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1 edition of Computational comparison of value iteration algorithms for discounted Markov decision processes found in the catalog.

Computational comparison of value iteration algorithms for discounted Markov decision processes

L. C. Thomas

Computational comparison of value iteration algorithms for discounted Markov decision processes

by L. C. Thomas

  • 147 Want to read
  • 18 Currently reading

Published by Naval Postgraduate School in Monterey, Calif .
Written in English

    Subjects:
  • Markov processes,
  • Algorithms,
  • Decision making,
  • Iterative methods (Mathematics)

  • About the Edition

    This note describes the results of a computational comparison of value iteration algorithms suggested for solving finite state discounted Markov decision processes. Such a process visits a set of states S = (1,2,...M). In Section two we describe the schemes examined and the various bounds that can be used for stopping them. Section three concentrates on one scheme that did well in the comparison - ordinary value iteration - and looks at various methods for eliminating non-optimal actions both permanently and temporarily.

    Edition Notes

    Other titlesNPS-55-82-034.
    Statementby L.C. Thomas, R. Hartley, A.C. Lavercombe
    ContributionsHartley, R., Lavercombe, A. C., Naval Postgraduate School (U.S.)
    The Physical Object
    Pagination10 p. ;
    Number of Pages10
    ID Numbers
    Open LibraryOL25522610M
    OCLC/WorldCa83370132

    Numerical studies show that the computational savings can be significant especially when the discount factor approaches one and the transition probability matrix becomes dense, in which the standard value iteration algorithm and its variants suffer from slow by: We give an introduction to in nite-horizon Markov decision processes (MDPs) with nite sets of states and actions. We focus primarily on discounted MDPs for which we present Shapley’s () value iteration algorithm and Howard’s () policy iter-ation algorithm. We also give a short introduction to discounted turn-based stochastic.

    Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes Aaron Sidford Stanford University [email protected] Mengdi Wang Princeton University [email protected] Xian Wu Stanford University [email protected] Yinyu Ye Stanford University [email protected] Abstract In this paper we provide faster. Here's what I understand: discount factor represents the preference of short-term solutions over long-term solutions. For example, if I could earn $1 today, I'd value it more than $1 which I could earn tomorrow, and much more than $1 which I could earn on Jan 1, , because random factor change situation more and more as time passes.

      Value Iteration is an iterative algorithm that computes values of states indexed by a, as in, which can also be thought of the best value of state assuming the game ends in time-steps. This is not the actual policy itself, but these values are used to determine the optimal policy 2.   Introduction DecisionTheory Intelligence Agents Simple Decisions Complex Decisions Value Iteration Policy Iteration Partially Observable MDP Dopamine-based learning MarkovDecision Process (MDP) A sequential decision problem for a fully observable, stochastic environment with a markovian transition model and additive rewards is called a markov.


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Computational comparison of value iteration algorithms for discounted Markov decision processes by L. C. Thomas Download PDF EPUB FB2

Volume 2, Number 2 OPERATIONS RESEARCH LETTERS June COMPUTATIONAL COMPARISON OF VALUE ITERATION ALGORITHMS FOR DISCOUNTED MARKOV DECISION PROCESSES L.C. THOMAS * and R. HARLEY Department of Decision Theory, University of Manchester, Manchester, United Kingdom A.C.

LAVERCOMBE Department of Mathematics, Bristol Cited by:   This note describes the results of a computational comparison of value iteration algorithms suggested for solving finite state discounted Markov decision processes.

Such a process visits a set of states S = (1,2, M). In Section two we describe the schemes examined and the various bounds that can be used for stopping :   This note describes the results of a computational comparison of value iteration algorithms suggested for solving finite state discounted Markov decision processes.

Such a process visits a set of states S = (1,2, M). In Section two we describe the schemes examined and the various bounds that can be used for stopping them. This note describes the results of a computational comparison of value iteration algorithms suggested for solving finite state discounted Markov decision processes.

Such a process visits a set of states S = (1,2, M). In Section two we describe the schemes examined and the various bounds that can be used for stopping : L. Thomas, R. Hartley and A.C. Lavercombe.

Markov decision process (MDP) is a model for represent-ing decision theoretic planning problems. Value iteration and policy iteration [Howard, ]are two fundamentaldy-namic programming algorithms for solving MDPs. How-ever, these two algorithms are sometimes inefficient. They spend too much time backing up states, often redundantly.

Abstract: In this paper we provide faster algorithms for approximately solving discounted Markov Decision Processes in multiple parameter regimes. Given a discounted Markov Decision Process (DMDP) with $|S|$ states, $|A|$ actions, discount factor $\gamma\in(0,1)$, and rewards in the range $[-M, M]$, we show how to compute an $\epsilon$-optimal policy, with probability Cited by: 3.

CSE Artificial Intelligence Markov Decision Processes (MDPs) Markov Decision Processes. An MDP is defined by:. A set of states s ∈ S. A set of actions a ∈ A. A transition function T(s,a,s’) Comparison.

In value iteration:. Every pass (or “backup”) updates both utilities (explicitly, basedFile Size: 1MB. $ Now we know how to act for infinite horizon with discounted rewards. $ Run value iteration till convergence.

$ This produces V*, which in turn tells us how to act, namely following: $ Note: the infinite horizon optimal policy is stationary, i.e., the optimal action at a state s is the same action at all times.

(Efficient to store!)File Size: 2MB. • Markov decision processes: examples, continued. • Value iteration: finite horizon, infinite horizon.

• Contractions 2 Markov decision processes (MDPs) Examples, continued Tetris Recall the popular computer game Tetris. In Tetris, pieces descend vertically one by one to stack on a game board, clearing when a row is fully Size: 97KB.

Markov Decision Theory In practice, decision are often made without a precise knowledge of their impact on future behaviour of systems under consideration.

The eld of Markov Decision Theory has developed a versatile appraoch to study and optimise the behaviour of random processes by taking appropriate actions that in uence future Size: KB.

What value-iteration does is its starts by giving a Utility of to the goal state and 0 to all the other states. Then on the first iteration this of utility gets distributed back 1-step from the goal, so all states that can get to the goal state in 1 step (all 4 squares right next to it) will get some utility.

European Journal of Operational Research 67 () North-Holland Theory and Methodology Serial and parallel value iteration algorithms for discounted Markov decision processes T.W. Archibald and K.I.M. McKinnon Department of Mathematics, University of Edinburgh, Edinburgh, UK L.C.

Thomas Department of Business Studies, University of Edinburgh, Edinburgh, UK Cited by: 7. In this paper we consider computational aspects of decision-theoretic planning modeled by Markov decision processes (MDPs).

Commonly used algorithms, such as value iteration. Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes." ―Journal of the American Statistical AssociationCited by: Computational Complexity Estimates for Value and Policy Iteration Algorithms for Total-Cost and Average-Cost Markov Decision Processes Je erson Huang Department of Applied Mathematics and Statistics Stony Brook University AP for Lunch Seminar IBM T.

Watson Research Center J Joint work with Eugene A. FeinbergFile Size: KB. Value Iteration and Its Variants. Policy Iteration. Modified Policy Iteration. Spans, Bounds, Stopping Criteria, and Relative Value Iteration.

Action Elimination Procedures. Convergence of Policies, Turnpikes and Planning Horizons. Linear Programming. Countable‐State Models.

The Optimality of. of the discounted Markov decision problem. By leveraging the value-policy duality and binary-tree data structures, the algorithm adaptively samples state-action-state transitions and makes exponentiated primal-dual updates.

We show that it nds an -optimal policy using nearly-linear run time in the worst case. When the Markov decision process is. A Markov decision process is a discrete time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.

MDPs are useful for studying optimization problems solved via dynamic programming and reinforcement learning. MDPs were known at least as early as the s; a core body of research on Markov decision processes resulted from Ronald Howard's book.

an extension of decision theory, but focused on making long-term plans of action. We’ll start by laying out the basic framework, then look at Markov chains, which are a simple case.

Then we’ll explore what it means to have an optimal plan for an MDP, and look at an algorithm, called value iteration, for finding optimal plans. We’ll finish File Size: KB. This text introduces the intuitions and concepts behind Markov decision processes and two classes of algorithms for computing optimal behaviors: reinforcement learning and dynamic programming.

First the formal framework of Markov decision process is defined, accompanied by the definition of value functions and by:. Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter was written by a leading expert in the re spective area.

The papers cover major research areas and methodologies, and discuss open questions and future research directions. The papers can be read independently, with the basic .Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.

Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable.We present two criteria for selecting the adaptive relaxation factor being used in speeding-up the value iteration algorithm for undiscounted Markov decision processes.

The criteria are: 1.