Value iteration example problems

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Lecture 3: Solving Equations Using Fixed Point Iterations Instructor: Professor Amos Ron Scribes: Yunpeng Li, Mark Cowlishaw, Nathanael Fillmore Our problem, to recall, is solving equations in one variable. The same complexity will come up and problems we really care about. The proposed method is an elegant combination of variational iteration and the homotopy perturbation methods and is mainly due to Ghorbani (2007). What would happen if we chose an initial x-value of x=0? tted value iteration (FVI) for solving large, or innite state-space Markovian decision problems (MDP) with a generative model. The values may or may not be used in the statement being executed. Policy iteration cuts through the search space, which is key when the optimal policy is not straightforward, in this case literally. This is the first question of assignment 5. The list of algorithms that have been implemented includes backwards induction, linear programming, policy iteration, q-learning and value iteration along with several variations. Assume The exact solution for the reinforcement learning (RL) and planning problems with large state space is difficult or impossible to obtain, so one usually has to aim for approximate solutions. Condition in for loop is evaluated on each iteration, if the condition is true then the statements inside for loop body gets iteration method. Here is the simplest while loop for our fixed point iteration. Open the data file Hh. These are the problems that are often taken as the starting point for adaptive dynamic programming. Markov Decision Process (MDP) Toolbox for Python. problems. Manual calculation of a number's square root is a common use and a well-known example. time for value iteration and . In C++ we have three types of basic loops: for, while and do-while. g (con-tractive structure) − Finite-state SSP with “nearly” contractive structure − Bellman’s equation has unique solution, value and policy iteration work • Difficult problems (w/ additional structure Iteration is the repetition of a process in order to generate a (possibly unbounded) sequence of outcomes. - To provide regular, predictable dev cadence to produce an increment of value - To give the ART members a process tool to keep the train on the tracks - To create a continuous flow of work to support the delivery pipeline - To allow the team to perform some final backlog refinement for upcoming iteration planning Solving Iteration Problems - An Example. 9 But returning to our example, how can we find the optimal policy like the one described in the above illustration? A classic algorithm exists for that kind of problems called Value Iteration. " Note: If we stop this algorithm at a finite value of n, we expect yn(t) to be a very good approximate solution to the differential equation. At each time t: (a) If t2T i, processor iperforms a policy improvement iteration, where the local policy is set to one attaining the corresponding minimum in the value iteration (1. Numerical Solution of Difficult ODE Boundary Value Problems Description Examples Description This page describes some strategies and suggestions for the use of the dsolve/numeric bvp solver for difficult problems. Parametric Iteration Method . Value iteration networks are an approximation of the value iteration (VI) algorithm implemented with convolutional neural networks to make VI fully differentiable. Select the Advanced tab, click the Open model button, and open the model file Hh. 410 / 16. This modification reduces the number of iteration. If you roll a 1 or a 2 you get that value in $ but if you roll a 3 you loose all your money and the game ends (finite horizon problem) CSE 473: Artificial Intelligence Markov Decision Processes (MDPs) Luke Zettlemoyer Many slides over the course adapted from Dan Klein, Stuart Russell or Andrew Moore Iteration Method Let the given equation be f(x) = 0 and the value of x to be determined. The variables used are: The algorithm that will be developed in here for the solution of certain boundary value problems is based on a kind of Picard iteration as just described. 6. e. 413 Principles of Autonomy and Decision Making. If x 0 = 3, for example, you would substitute 3 into the original equation where it says x n. Figure 1: The graphs of y=x (black) and y=\cos x (blue) intersect. Value Iteration is a combination of one (or more than one) sweep of policy evaluation and then perform another sweep of policy improvement. 1 Setting Up Iterative Calculations in Excel. It is assumed that the n eigenvalues have the dominance property . of Electrical Engineering and Computer Sciences, UC Berkeley Abstract We introduce the value iteration network (VIN): a fully differentiable neural net-work with a ‘planning module’ embedded within. For the general multichain case, we obtain necessary and sufficient conditions which guarantee that the maximal total expected reward for a planning horizon of n epochs minus n times the long run average expected reward has a finite limit as n → ∞ for each initial state and each final reward vector. in ’t panhuis CASA Center for Analysis, Scientific Computing and Applications Department of Mathematics and Computer Science 9-November-2005 Outline Introduction Schur Decomposition The QR Iteration Methods for Symmetric matrices Conclusion Iterative Techniques For Solving Eigenvalue Problems P. Value-Determination Function (1) 2 ways to realize the function VALUE-DETERMINATION. . Value iteration is not optimal, because the value iteration solutions that have been devised for deterministic games require exponentially many iterations even when applied to the special case of graphs. 24 Sep 2010 Bias-Variance Trade-off in Control Problems The λ-Policy Iteration algorithm tion of the value function, that is, they need samples. 27: In Example 9. The Bisection Method will keep cut the interval in halves until the resulting interval is extremely small. M. Here, for example, is an algorithm for evaluating n m where m is a non-negative integer: Start off with store locations acc initially set to 1 and count initially set to 0. Example 2. A criterion function in risk minimizing stopping problems is G τ (x,r)=P x (Y τ ⩽r)=E x [I (Y τ ⩽r)], x∈S, τ∈Γ, where r is a real number and is called the threshold value and I A denotes the indicator function of A. Linear and nonlinear problems are solved to outline the basic ideas of Put simply, you can use Solver to determine the maximum or minimum value of one cell by changing other cells. Notions of pivoting and splitting are deliberated on to make the method more robust. This makes this method of iteration an extremely powerful tool for solving differential equations! For a concrete example, I’ll show you how to solve problem #3 from section 2−8. You can, for example, move them to a different iteration or return them to the backlog. Example 9. Some notation has also been altered from the previous edition to reflect mor e common usage. The new x-value (x n+1) will be equal to the root of the tangent to the function at the current x-value (x n). - Lighting was also the subject of a computer simulation study, with five iterations undertaken before the final configuration of rooflight was determined. Excel can use iteration to calculate the solutions to simultaneous equations which refer to one another in a circular way. Writeafunctioncalledvalue_iteration() thatwillacceptanumpyarrayrep-resentingtheinitialvectorV 0,adiscountfactor 2(0;1),thenumberofstatestosplitw into N, theamountofinitialcake W max, autilityfunction u(x), thetoleranceamount ", andthe maximum number of iterations max_iter. Our point-based policy iteration (PBPI) algorithm aims to combine some of   23 Mar 2017 The following problems appeared as a project in the edX course ColumbiaX: CSMM. This is illustrated by the example in Figure 4. In this methods the value of unknown immediately reduces the number of iterations, the calculated value replace the earlier value only at the end of the iteration. , for all x2X i, t+1 i (x) 2argmin "Value Iteration Networks (VIN)" (Tamar et al. S elect Structural Equation Modeling from the Statistics - Advanced Linear/Nonlinear Models menu to display the Structural Equation Modeling Startup Panel. Although Q-learning is guaranteed to converge to an optimal state-action value function (or Q-function) when state- We applied the variational iteration method and the homotopy perturbation method to solve Sturm-Liouville eigenvalue and boundary value problems. cmd. Find the yield to maturity on the bond. H. Results on Fitted Value Iteration Outline Outline 1 Fitted Value Iteration Markovian Decision Problems Fitted Value Iteration Counterexamples Positive Results 2 Results Regression Finite-time Bounds Outline of the Proof Single-sample Variant How to Use the Result? 3 Illustration 4 Conclusions Csaba Szepesvari´ Results on Fitted Value Iteration • First iteration (k=1 x=7) – test: while-loop example • Q: What is the value of p when the loop terminates? Practice problems 1. 1st way: use modified Value Iteration with: Often needs a lot if iterations to converge (because policy starts more or less random). neural networks, adaptive regression trees, kernel machines, locally weighted learning). in ’t panhuis CASA Center for Analysis, Scientific Computing and Applications Department of Mathematics and Computer Science 9-November-2005 The following team level artifacts help describe the business and technical value delivered by the teams during each iteration and PI: Story – Is the vehicle that carries Customer requirements through the Value Stream into implementation. We notice that A1 is stronger than A2 since when A1 holds, A2 also holds for any distribution ˆ (with the same con-stant C). Use the power method to find the dominant eigenvalue and eigenvector for the matrix . Value iteration first initializes the value function arbitrarily, for example all zero. Few examples are solved to demonstrate the applicability of the method. Fixed Point Iteration Method : In this method, we flrst rewrite the equation (1) in the form x = g(x) (2) in such a way that any solution of the equation (2), which is a flxed point of g, is a solution of equation (1). We will now show an example of value iteration proceeding on a problem for a horizon length of 3. EXAMPLE 4 Illustrate the Picard iteration scheme for the initial value problem Solution For the problem at hand, , and Equation (4) becomes If we now use Equation (5) with we get Substitute for in Horizon-based Value Iteration Peng Zang Arya Irani Charles Isbell ABSTRACT We present a horizon-based value iteration algorithm called Re-verse Value Iteration (RVI). 2 Policy Iteration The value iterations of Section 10. Then on the first iteration this 100 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. For example, suppose we want to sum the integers from 0 to some value n: Package ‘MDPtoolbox’ March 3, 2017 Type Package Title Markov Decision Processes Toolbox Version 4. What value-iteration does is its starts by giving a Utility of 100 to the goal state and 0 to all the other states. ” Why study fixed-point iteration? 3 1. For example, the term “null space” has been substituted to less c ommon term “kernel. It is worth noting the implementation detail that if 1 is negative, for example, it may appear Another use of iteration in mathematics is in iterative methods which are used to produce approximate numerical solutions to certain mathematical problems. Value iteration converges. The object of my dissertation is to present the numerical solution of two-point boundary value problems. In particular, note that Value Iteration doesn't wait for the Value function to be fully estimates, but only a single synchronous sweep of Bellman update is carried out. 2 Feb 2018 A finite Markov Decision Process (MDP) is a tuple where: ○ Dynamic Programming (DP) solutions to the RL problem. 3. In the "factorial" example the iterative implementation is likely to be slightly faster in practice than the recursive one. It follows that convergence can be slow if 2 is almost as large as 1, and in fact, the power method fails to converge if j 2j= j 1j, but 2 6= 1 (for example, if they have opposite signs). Then, the values are updated iteratively using an operator called the Bellman backup (Line 7 of Algorithm 1) to create successively better approximations for each state per iteration. • Policy- a function that describes how to select actions in. by a simple yet powerful algorithm named value iteration (Bellman, 1957). Empirical results on a variety of do-mains, both synthetic and real, show RVI often yields speedups of several orders of magnitude. EXAMPLE 2 Applying the Gauss-Seidel Method Use the Gauss-Seidel iteration method to approximate the solution to the system of equations given in Example 1. This program is same as the one in Example 1. Many concepts in data models, such as lists, are forms During an iteration any flux is treated as inactive, and the corresponding degree of freedom is also marked inactive. An iteration formula might look like the following: x n+1 = 2 + 1 x n. Syntax of for loop MODIFIED CHEBYSHEV-PICARD ITERATION METHODS FOR SOLUTION OF INITIAL VALUE AND BOUNDARY VALUE PROBLEMS A Dissertation by XIAOLI BAI Submitted to the Office of Graduate Studies of One page accumulates more page rank at each iteration monopolizing the score. ! Dynamic programming / Value iteration ! Discrete state spaces (DONE!) ! Discretization of continuous state spaces ! Linear systems ! LQR ! Extensions to nonlinear settings: ! Local linearization ! Differential dynamic programming ! Optimal Control through Nonlinear Optimization ! Open-loop ! Policy iteration is usually slower than value iteration for a large number of possible states. •Initialize all values to the immediate rewards. (Efficient to store!) Value Iteration Convergence Theorem. reinforcement-learning / DP / Value Iteration Solution. We apply our approach to adaptive occupancy mapping and demonstrate our method on problems of up to 103 grid cells, solving example problems within 1% of the optimal policy. The Gauss-Seidel method is the modification of the gauss-iteration method. net /1969. In particular, we change during the value iteration process (6) by using an iteration of the form (5), but with h k(n) replaced by an approximation, the current value iterate hk+1(n). Modular Value Iteration Through Regional Decomposition Linus Gisslen, Mark Ring, Matthew Luciw, and Jurgen Schmidhuber IDSIA Manno-Lugano, 6928, Switzerland {linus,mark,matthew,juergen}@idsia. The iteration process with these nodes will not converge irrespective of how long the process is run. It was successfully applied to various linear and nonlinear problems Mohyud-Din (2009), Noor (2007, 2008). R(s) = -  For many medium-sized problems, we can use the techniques from this lecture to A more formal definition will follow, but at a high level, an MDP is defined by:  Value Iteration and Policy Iteration. Newton's method is an example of an iterative method. Solving MDPs. w:Power method is an eigenvalue algorithm which can be used to find the w:eigenvalue with the largest absolute value but in some exceptional cases, it may not numerically converge to the dominant eigenvalue and the dominant eigenvector. You are usually given a starting value, which is called x 0. Available electronically from http: / /hdl. find the power root of each side, leaving x on its own on the left. Excel continues the process until a criteria or limit is reached. problem involves policy iteration; a value iteration approach for this problem involves a relative value iteration algorithm for solving average reward SMDPs via  Markov Decision Processes (MDP) are a widely used model including both RP 2014: Reachability Problems pp 125-137 | Cite as First we introduce an interval iteration algorithm, for which the stopping criterion is straightforward. The iteration is when a loop repeatedly executes until the controlling condition becomes false. com Abstract. It runs into problems in several places. 1 work by iteratively updating cost-to-go values on the state space. Let's say we've got a Markov Decision Process, and a policy π. Value Iteration Networks Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel Dept. Modified Chebyshev-Picard Iteration Methods for Solution of Initial Value and Boundary Value Problems. It is well known that in many instances Picard iteration performs poorly in actual practice, even for relatively simple differential equations. 2. That's it for this video. For example, you'll definitely encounter the q learning which is the value iteration algorithm applied to the q function instead of v function. Lesser; CS683, F10 $ Run value iteration till convergence. edu is a platform for academics to share research papers. Cyrill Stachniss the optimal policy. They require iteration across all of the states. 100 examples: The network was trained by processing 12 iterations of the complete training set. The exception, as Isaac Newton discovered, is that interest computation requires iteration and may result in several solutions. Cycles In figure 3, nodes 1 and 2 form an infinite loop or cycle. we do not Policy iteration comments •Each step of policy iteration is guaranteed to strictly improve the policy at some state when improvement is possible • Converge to optimal policy •Gives exact value of optimal policy Policy Iteration Example 0. We saw in the gridworld example that at around k = 10, we were already in a position to find the optimal policy. These equations solve problems that involve compound interest. It has been shown that seventh-order boundary value problem can be transformed into a system of integral equations, which can be solved by variational iteration technique. The Bisection Method will cut the interval into 2 halves and check which half interval contains a root of the function. Bertsekas2 Abstract We propose a new value iteration method for the classical average cost Markovian Decision problem, under the assumption that all stationary policies are unichain and furthermore there A Preliminary Example. 4. The default value of is 10 –5; you can redefine this parameter. In the Excel 2016 Bible, John Walkenbach provides a typical example of such situation: problems involving square numbers or square roots. In this example, we have a small gridworld. The value function for optimal policy can be solved through a  We present a horizon-based value iteration algorithm called Re- verse Value learning, and control problems,there has been a tremendous amount of recent  Abstract The problem of solving large Markov decision processes accurately and Value iteration is a dynamic programming algorithm (Bellman 1957) for. 2. Analyzing fixed-point problem can help us find good root-finding methods A Fixed-Point Problem Determine the fixed points of the function = 2−2. method: 1. ipynb. The default, 'factorization', takes a slower but more accurate step than 'cg'. In this tutorial we will learn how to use “for loop” in C++. After all instructions are typed in, we press the “enter” key to execute the sequence. 10. It is also observed that the variational iteration method can be easily applied to the initial and boundary value problems. • States: (x,y)- coordinate in  18 Apr 1996 iteration (PI) [Howard, 1960]), are compared on a class of problems from the mo- in case of the policy iteration algorithm introduced in [Russel  We introduce an algorithm named Topolog- ical Value Iteration (TVI) that can circumvent the problem of unnecessary backups by detecting the structure of MDPs  variant of modified policy iteration of order six to a value iteration algorithm with The policy iteration algorithm for solving this problem can be written as follows:. A method for solving complex problems. When the problem is complex and can be expressed in more simplified form as recursive case then its iterative counter part. By breaking Value function stores and reuses solutions Dynamic programming assumes full knowledge of the MDP. A pathological example planning problems based on inverse reinforcement learning (IRL) using deep convolutional networks and value iteration networks as important internal structures. The MDP toolbox provides classes and functions for the resolution of descrete-time Markov Decision Processes. handle. sta. Approximate Policy Iteration (API) and Approximate Value Iteration (AVI) are two classes of iterative algorithms Learn via example how Gauss-Seidel method of solving simultaneous linear equations works. MDPs are useful for studying optimization problems solved via dynamic  management. ❑ Model for animals, people. This way, the policy extracted from value iteration will not get stuck in an infinite loop. With one exception, each kind of problem can be solved immediately, using a well-defined equation. There is no repeat of the two The value of () is negative, which means that is less than . PIM is an approximation method for solving linear and nonlinear problems and at beginning it was proposed for solving nonlinear fractional differential equations [6], by modifying He’s variational iteration method [7]. Keywords: Singular perturbation, two- point, boundary value problems; Variational Iteration method . Feinberg Value and policy iteration algorithms apply • Somewhat complicated problems − Infinite state, discounted, bounded. Examples of iteration in a sentence, how to use it. Example. The optimal plan can alternatively be obtained by iteratively searching in the space of plans. An iteration is a repetition of an operation. g. Moreover, the small-est nonnegative ratio of and is 5, so is the departing variable. Then consider the following algorithm. 14 Jul 2014 The solution of this problem is a policy function that indicates the action to . In your script, calculate field will compute the values of group for ALL the row (or the selected rows if you used a layer) at each iteration. J. The value of acc is now n m Chapter 3: Programming in Mathematica Programming in Mathematica A program (code) is a sequence of instructions to solve some problem. S. The algorithm is simple and guaranteed to converge by the CMT. GU∗ Abstract. In some cases, we do not know the initial conditions for derivatives of a certain order. methods less a⁄ected by ⁄atness max dominant eigenvalue. . 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. Value iteration is almost a pessimal algorithm, in the sense that it never leverages any advantage a sparse transition matrix (and/or sparse reward function) may offer: it always iterates over and updates every (s,a) pair, even if such a backup does not (or cannot) change the value function. If you don't know the total number of iterations, a while-loop is often the best choice. This leads to a method called policy iteration ; the term policy is synonymous with plan. For example, when you are displaying number from 1 to 100 you may want set the value of a variable to 1 and display it 100 times, increasing its value by 1 on each loop iteration. The bounds derived on the The Sherman Morrison Iteration Joseph T. This example showed how to implement iterators for a collection class, but we can implement iteration abstractions for other problems. Modified policy iteration. Given V. A. /reinforcement-learning/ blob/master/DP/Policy%20Evaluation%20Solution. We could define an iterator object that iterates over all primes! riving performance bounds in policy iteration algorithms. , During an iteration any flux is treated as inactive, and the corresponding degree of freedom is also marked inactive. 1 Updating variables A common pattern in assignment statements is an assignment statement that updates a variable - where the new value of the variable depends on the old. In this example, we haven't used the initialization and iterator statement. 1). This functionality is known as recursion. • Policy Iteration • Reinforcement Learning Andrey Markov (1856‐1922) Asynchronous Value Iteration States may be backed up in any order •Instead of systematically, iteration by iteration Theorem: •As long as every state is backed up infinitely often… • Asynchronous value iteration converges to optimal Value Iteration Networks Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel Dept. ca. 2 Value Function Iteration The idea behind value function iteration is essentially to apply the contraction mapping theorem (CMT) on a computer. Heckendorn Department of Computer Science Colorado State University Fort Collins, CO 80523 heckendo@cs. For some problems, an intermediate bandwidth reduces the number of PCG iterations. 0. is the time-averaged value of the largest flux in the model during the current step. To fully understand the intuition of dynamic programming, we begin with sim-ple models that are deterministic. Design an algorithm to As the name suggests, it is a process that is repeated until an answer is achieved or stopped. the most time-consuming stepin the value function iteration • Howard’s improvement reduces the number of times we update the policy function relative to the number of times we update the value function • Idea: on some iterations, we simply use the current approximation to the policy function to update the value function, i. yes Iteration #2 . In this week's assessment, you will have a chance to implement policy iteration on a slightly more realistic example. To compute the dominant value and its associated eigenvector for the n×n matrix A. The sequence will approach some end point or end value. 3), i. A classical problem in matrix computations is the efficient and reliable approximation of a given matrix by a matrix of lower rank. I am learning about MDP's and value iteration in self-study and I hope someone can improve my understanding. ○ For DP, this is a full backup, since we don't sample next states  18 Sep 2018 DP is a collection of algorithms that can solve a problem where we have the . Future AGIs will need to solve large reinforcement-learning problems involving complex reward functions having multiple reward sources. Just to get a feel for the method in action, let's work a preliminary example completely by hand. Example 1. i. A while loop executes a block of code an unknown number of times. Horizon-based Value Iteration Peng Zang Arya Irani Charles Isbell ABSTRACT We present a horizon-based value iteration algorithm called Re-verse Value Iteration (RVI). Recursion versus Iteration. , 2017) embed a differentiable planning module (i. Chapter 1 Introduction Before we start with the subject of this notes we want to show how one actually arrives at large eigenvalue problems in practice. The gamma value of 0 is the best when unit testing the code, as for MDPs, it is always difficult to test the behavior of an agent when number of horizons increases. While count is not equal to m, multiply acc by n and increase count by 1. At the end of an iteration, you should find all work items that remain active or have not been closed for that iteration and take appropriate action. So once an initial value is chosen, the iteration is given by Notice that the operations involved in the iteration are additions and multiplications which are things that calculators can do! You might try to show that the iteration will compute square roots. Markov decision processes exemplify sequential problems, which iteration algorithm starts with a random initial policy, instead of a fixed one, which. Solution The first computation is identical to that given in Example 1. For example, if the starting value (the argument passed to sequence) is 3, the resulting sequence is 3, 10, 5, 16, 8, 4, 2, 1. In asynchronous value iteration, the +10 reward state can be chosen first. x = x+1 This means "get the current value of x, add one, and then update x with the new value. 1 Introduction Q-learning is a foundational algorithm in reinforcement learning (RL) [34, 26]. ▫. The fact that variational iteration technique solves nonlinear problems without using Adomian polynomials can be considered as a clear advantage of this method over the decomposition method. 25. These type of problems are called boundary-value problems. But how to compare it to these approaches which one is better? Well, this is a question without a clear answer. We can’t apply value/policy iteration when we do not have a model of an environment for instance. It also contains a simple simulator for evaluating the quality of the computed policy. For example, you can assign a task to an iteration but not close or complete it during that iteration. •Update values based on the best next-state. discrete-time Markov Decision Processes: finite horizon, value iteration, policy mdp_bellman_operator. 1. Consider the problem of a 3 sided dice having numbers 1, 2, 3. Then step one is again performed once and so on. numerically, finding a value for the solution at x = 1, and using steps of size h = 0. This is how your calculator does it internally. Value Function Iteration as a Solution Method for the Ramsey Model Abstract Value function iteration is one of the standard tools for the solution of the Ramsey model. Value Iteration. Common problems Value Iteration control problems where T has the form (1. TolPCG: Termination tolerance on the PCG iteration, a positive scalar. We should know the definition for dominant eigenvalue and eigenvector before learning some exceptional examples. In this paper, we modify VIM for second order initial value problems by transforming the I was going through Sutton and Barto's "Reinforcement Learning- An Introduction". In this paper, the author used He’s variational iteration method for solving singularly perturbed two-point boundary value problems. In math, iterations often involve taking the output of a function and plugging it back in - repeating a process using each output as the input for the following iteration. See Trust-Region-Reflective Least Squares. Siddiqi and Ghazala [5-10] presented the solutions of eight, tenth and The exact solution for the reinforcement learning (RL) and planning problems with large state space is difficult or impossible to obtain, so one usually has to aim for approximate solutions. So the policy will continue to stay in those states that have high reward. What this algorithm does is calculate the long-term benefit that can be achieved by currently being in some state, by asking the question “what is the Iteration Method Let the given equation be f(x) = 0 and the value of x to be determined. It takes as input a POMDP model (coded in C++) and produce a policy file. A Markov decision process (MDP) is a discrete time stochastic control process. rearrange the given equation to make the highest power of x the subject . For each processor i, there are two disjoint subsets of times T i and T i. We apply our approach to adaptive occupancy mapping and demonstrate our method on problems of up to 10 3 grid cells, solving example problems within 1 % of the optimal policy. the LHS x becomes x n+1 2. required for the value iteration algorithm (lines 11a, 13b, Table 2). Approximate Policy Iteration (API) and Approximate Value Iteration (AVI) are two classes of iterative algorithms SOLUTION METHODS FOR EIGENVALUE PROBLEMS IN STRUCTURAL MECHANICS KLAUS-JURGEN BATHE* AND EDWARD L. 5 contributors. Thus, we can apply another iteration of the simplex method to further im-prove our solution as follows. ○ Generalised Policy Iteration. For example, suppose we wanted to do some computation on all the prime numbers. Dr Quasi-Newton Methods are an efficient way to optimize functions when either computation or iteration is costly. 1 decreases by roughly this factor from iteration to iteration. $\endgroup$ – ljeabmreosn Dec 6 '18 at 16:59 Planning: Policy Evaluation, Policy Iteration, Value Iteration 05 June 2016 on tutorials. current value held in A1 should be incremented by 1 and that this new value should in turn be increased by 1 again. ▫ Algorithm: ▫ Start with for all s. When to choose recursion against iteration; 1. As a preliminary work on the topic, the simplest algorithm of PIA(1,1) is employed in the calculations. W. Troubleshooting Convergence Problems As with any nonlinear estimation routine, there is no guarantee that the estimation will be successful for a given model and data. closed form solutions by using only one or two iterations. The root is then approximately equal to any value in the final (very small) interval. LSPI is a model-free, off-policy method which can use efficiently (and reuse in each iteration) sample experiences collected in any manner. Introduction November, 1993 LIDS- P 2212 Research Supported By: NSF CCR-9103804 Generic Rank-One Corrections for Value Iteration in Markovian Decision Problems Iteration Examples By YourDictionary Iteration is defined as the act or process of repeating. In value iteration, you start at the end and then work backwards re ning an We will now show an example of value iteration proceeding on a problem for a horizon length of 3. Equations don't have to become very complicated before symbolic solution methods give out. Fixed point: A point, say, s is called a fixed point if it satisfies the equation x = g(x). 3 Fitted value iteration (FVI), both in the model-based [4] and model-free [5, 15, 16, 17] settings, has become a method of choice for various applied batch reinforcement learning problems. If it is odd, the value is replaced by 3n+1. Value iteration example: Grid World. ualberta. iteration method and a particular case of this method called Newton’s method. In the sequel, we will assume that the problem has a continuous state space S= Rn, but that the action space Ais small and discrete. It arises in a wide variety of practical applications in physics, chemistry, biosciences, engineering, etc. This paper considers undiscounted Markov Decision Problems. Numerical Methods: Fixed Point Iteration. has a z-value of In Example 1 the improved solution is not yet optimal since the bottom row still has a negative entry. We find that value function iteration with cubic spline interpolation between grid In C, "sequence statements" are imperatives. 3 Date 2017-03-02 Author Iadine Chades, Guillaume Chapron, Marie-Josee Cros, Frederick Garcia, Regis Sabbadin value-iteration and Q-learning that attempt to reduce delusional bias. The reason why such problems may have multiple solutions is because, as explained at Math Planet, any positive real number has 2 square roots. One of these square roots is positive and the other is negative. The teams use stories to deliver value within an iteration, and the Product Owner has content authority Variational iteration method for solving two-point boundary value problems Junfeng Lu∗ College of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310006, PR China Received 15April 2006; received in revised form 22 May 2006 Abstract Variational iteration method is introduced to solve two-point boundary value problems. The main advantage of these methods is the flexibility to give approximate and exact solutions to both linear and nonlinear problems without linearization or discretization. The primary difference between recursion and iteration is that is a recursion is a process, always applied to a function. The elegant coupling gives rise to the modified versions of VIM which is very efficient in solving nonlinear problems of diversified nature. Cite This There is simple rule that you can use to tell if you should use a while-loop. The result is unpredictability and delays in value delivery. Gauss Seidel Iteration Method Explained on Casio fx-991ES and fx Problems - Duration: 15:48. , best action is not changing • convergence to values associated with fixed policy much faster Normal Value Iteration V. Anderson Department of Computer Science Colorado State University Fort Collins, CO 80523 anderson@cs value iteration for these problems. Direct/Fixed Point Iteration. The use of the Rayleigh quotient is demonstrated in Example 3. Examples. The following examples illustrate the Picard iteration scheme, but in most practical cases the computations soon become too burdensome to continue. Fixed point Iteration: The transcendental equation f(x) = 0 can be converted algebraically into the form x = g(x) and then using the iterative scheme with the recursive relation Value Iteration for POMDPs After all that… The good news Value iteration is an exact method for determining the value function of POMDPs The optimal action can be read from the value function for any belief state The bad news Time complexity of solving POMDP value iteration is exponential in: Actions and observations 6 Chapter 1. ❑ Shortest path problems. A1 concerns the immediate transition probabili-ties (an example for which A1 holds is the optimal replace-ment problem described below) whereas A2 expresses some horizon problems of optimal control to a terminal set of states. However, it is known that depending on the function approximation scheme used, fitted value iteration can and does diverge in some settings. 11, 2011 HG 1. Mathematica Subroutine (Power Method). Yield to maturity is the rate which discounts the bond's future cash flows (coupons and par value) such that their present value equals the bond's market price. The "selection" is the "if then else" statement, and the iteration is satisfied by a number of statements, such as the "while," " do," and the "for," while the case-type statement is satisfied by the "switch" statement. Monte Carlo Value Iteration (MCVI) for POMDP ----- MCVI is a C++ implementation of the MCVI algorithm [1]. The remaining problem is to find an efficient method for computing starting. Note that, a priori, we do not 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. FITTED VALUE FUNCTION ITERATION WITH PROBABILITY ONE CONTRACTIONS JENO P¨ AL AND JOHN STACHURSKI´ ABSTRACT. For example, iteration can include repetition of a sequence of operations in order to get ever closer to a desired result. Pieter Abbeel Shortest path problems. Numerical Methods for the Root Finding Problem Oct. This lecture intro-duces two key concepts: the value function and value function iterations. A second example of the kind of problems considered in this dissertation is the  2 Dec 2017 We'll discuss the Q-Learning algorithm for teaching a machine to play a Specifically, we use a Markov Decision Process (MDP) to define our game. In Mathematica, we input each instruction and press the “return” key. We are given a function f, and would like to find at least one solution to the equation f(x) = 0. Iteration is used, for example, to solve equations and optimization problems - see Goal Seek and Solver in Microsoft Excel for further Using Python to Solve Computational Physics Problems. VINs can learn to plan, and are Thus the variational iteration method is suitable for finding the approximation of the solution without discretization of the problem. This example will provide some of the useful insights, making the connection between the figures and the concepts that are needed to explain the general problem. In this section, we study the process of iteration using repeated substitution. This algorithm uses the fact that the Bellman operator $ T $ is a contraction mapping with fixed point $ v^* $. As a countermeasure, the IP iteration offers a ‘guard band’ (or small buffer) to prevent unfinished work from the current PI from carrying over to the next PI. Unlike most previous results, our theoretical guarantees apply to a large class of regressors (e. Perform value iteration until kV k+1 V kk<"or k>max_iter. On the other hand, fixed point iteration is a method of computing fixed point of iterated function, it is a well- II. tion (LSPI), learns the state-action value function which allows for action selection without a model and for incremental policy improvement within a policy-iteration framework. Allowing for non-concave Choosing the gamma value=0 would mean that you are going for a greedy policy where for the learning agent, what happens in the future does not matter at all. Abbeel steps through the execution of value iteration. ▫ For i=1, … , H. Chapter 4 Existence and uniqueness of solutions for nonlinear ODEs In this chapter we consider the existence and uniqueness of solutions for the initial value problem for general nonlinear ODEs. For loop in Java with example. ▫ In an MDP, we want an optimal policy π*: S → A In deterministic single-agent search problems, want an Example Optimal Policies. 0 Do one iteration of policy iteration on the MDP below. 1 1. Graphs are also plotted for the numerical examples. Watson Research Center July 29, 2015 Joint work with Eugene A. Value iteration methods in risk minimizing stopping problems for finite and infinite horizon cases, find optimal stopping times and give a value iteration method. G. [Initially, the window is the whole array. Solution. If the limit in (1. 2 Nov 2015 developed to solve infinite horizon undiscounted optimal control problems. Topological Value Iteration Algorithm for Markov Decision Processes Peng Dai and Judy Goldsmith Computer Science Dept. Without this section you will not be able to do any of the differential equations work that is in this chapter. Then we  This package implements the discrete value iteration algorithm in Julia for solving Markov decision processes (MDPs). 1 A Case Study on the Root-Finding Problem: Kepler’s Law of Planetary Motion The root-finding problem is one of the most important computational problems. To find the root of the equation first we have to write equation like below x = pi(x) - The **Value Iteration** button starts a timer that presses the two buttons in turns. Computer Science Technical Report A Multigrid Form of Value Iteration Applied to a Markov Decision Problem Robert B. A backward value iteration solution will be presented that follows naturally from the method given in Section 10. Iteration Problems Because it deals w/financial statements, circular references abound and are necessary. We therefore use as the left endpoint of our new interval and keep 2 as the right endpoint. 1) for large n is maximal gain. 2 Fitted value iteration We now describe the fitted value iteration algorithm for approximating the value function of a continuous state MDP. For this reason the VFI Toolkit should be robust to the ’ugly’ problems for which value function iteration is actually used. •Repeat until convergence (values don’t change). To solve the equation on a calculator with an ANS, type 2 =, then type to The Adomian Decomposition Method [1, 4], the Differential Transform Method [15], the Variational Iteration Method, the successive iteration, the splines [5, 6], the Homotopy Perturbation Method [7], the Homotopy Analysis Method etc Recently Vasile Marinca et al. Section 5-3 : Review : Eigenvalues & Eigenvectors. 1. The mixed value and policy iteration method of this paper evolved from the enhanced policy iter-ation algorithmic framework proposed and analyzed in our earlier works for nite-state and control problems [10, 57] and for abstract DP problems [9] under discounted and undiscounted total cost cri- VALUE ITERATION METHOD 745 In this paper we propose algorithms based on the -SSP, which are more e cient than the algorithms mentioned above. 4 Recall that in value iteration, we would like to perform the update The agile approach can help project teams quickly adapt to changing stakeholder requirements and volatile project conditions. In order to get the desired result, try making each state (excluding the end state) have a non-positive reward. The suggested algorithm is SUBSPACE ITERATION RANDOMIZATION AND SINGULAR VALUE PROBLEMS M. For example, if you would like to solve the two simultaneous equations: y = x + 3y 3 2x 2 = 3y + 2 you would need to convert them to the form: y = f(x) x = g(y) i. Instead, we know initial and nal values for the unknown derivatives of some order. RVI does this by ordering backups by Problems usually involve finding the root of an equation when only an approximate value is given for where the curve crosses an axis. Value iteration is not typically considered a viable algorithm for solving large-scale MDPs because it converges too slowly. It is to be mentioned that, presently, the literature on the numerical solutions of seventh order boundary value problems and associated eigen value problems is not available We apply the variational iteration method using He's polynomials (VIMHP) for solving the fifth-order boundary value problems. Problems were left unchanged from the earlier edition, but the Notes and ref-erences sections ending each chapter were systematically updated. •The value of s’may depend on the value of s. RVI does this by ordering backups by Furthermore, by carefully selecting features to approximate the value function, we can compute value iteration backups in closed form. In Figure 2, the dangling node 3 is a rank sink. The Sherman Morrison iteration method is shown to be e ective when dealing with At each iteration, the program outputs the value of n and then checks whether it is even or odd. 1 /ETD-TAMU-2010-08-8240. , Statistics, and CS, University of Wisconsin-Stout, Menomonie, WI 54751, USA 2Department of Mathematics, Clayton State University, Morrow, GA 30260, USA Abstract-In this paper, we introduce a new parameter Value Iteration •The value of state sdepends on the value of other states s’. This paper outlines a detailed study of the coupling of He's polynomials with correction functional of variational iteration method (VIM) for solving various initial and boundary value problems. This example will provide some of the useful insights, making the connection between the figures and the concepts that are needed to explain the general problem. Such an algorithm may be Solution of Eleventh Order Boundary Value Problems Using Variational Iteration Technique 506 degree splines, respectively. Value and Policy Iteration 1For infinite horizon problems, we need to replace our basic computational tool, the DP algorithm, which we used to compute the optimal cost and policy for finite horizon problems. Earned value management (EVM) provides project managers with an effective tool for tracking progress against the project's schedule and budget. For example, you can change the amount of your projected advertising budget and see the effect on your projected profit amount. C# For Loop: Iteration 1 C# For Loop: Iteration 2 C# For Loop: Iteration 3 C# For Loop: Iteration 4 C# For Loop: Iteration 5. Therefore the GROUP values for all rows are overwritten by has the value of the last "code2012". The recently developed perturbation iteration method is applied to boundary layer type singular problems for the first time. Numerical Methods! for! Elliptic Equations-I! Iteration as Time Integration! Example! Which is exactly the Jacobi iteration! Boundary Value Problems! You would usually use iteration when you cannot solve the equation any other way. Under very general assumptions, we establish the uniqueness of solution of Bellman’s equation, and we provide convergence results for value and policy iteration. Slagel (ABSTRACT) The Sherman Morrison iteration method is developed to solve regularized least squares problems. Iteration, Induction, and Recursion The power of computers comes from their ability to execute the same task, or different versions of the same task, repeatedly. # Generates a random MDP problem. In modified policy iteration (van Nunen 1976; Puterman & Shin 1978), step one is performed once, and then step two is repeated several times. If you get nothing out of this quick review of linear algebra you must get this section. Termi-nation is controlled by a logical expression, which evaluates to true or false. Sometimes easier to analyze 2. Doctoral dissertation, Texas A&M University. Company D's 10-year bond with par value of $1,000 and semiannual coupon of 8% is currently trading at $950. In computing, the theme of iteration is met in a number of guises. Applying the Method problems pervade macroeconomics: any model in which agents face repeated decision problems tends to have a recursive formulation. -convergence of value function Or simply stop after k iterations of iterative policy evaluation? Binary Search Idea: Have a window or range of values we are currently considering. ▫ Calculate utility of   Problem: – Given: • Transition probability function: • Reward function: – Determine: • Optimal . Examples Value Iteration. Quote: If you don't know ahead of time exactly how many times you'll want the loop to iterate, use a while-loop (Code Complete). Repeat the process from Iteration #1 to do Iteration #2: First of all, let me define both terms. 102x Machine (1) First use policy iteration method to… We propose a new approximate value iteration method, namely near-value continuous-state optimal stopping problems under partial observation, which in  Preliminaries: Problem Definition Value Function- Measure of goodness of being in a belief state. between value before iteration and the value until iteration is "small enough", we call it convergence is already done well by Dynare using perturbation methods and value function iteration would be an inappropriate solution method for such problems. The truncated singular value decomposition (SVD) is known to provide the best such approximation for any given fixed rank. Note that you could use "calculate field" only once to update the value of group based on the value of field. The iteration is applied to the set of instructions which we want to get repeatedly executed. That is, using as the initial approximation, you obtain the following new value for The for Loop and Practice Problems CS 107 Stephen Majercik Use To repeat execution of a statement (possibly a compound statement) once for each value of a specified range of values. •Notice on each iteration re-computing what the best action – convergence to optimal values •Contrast with the value iteration done in value determination where policy is kept fixed. WILSONt University of California, Berkeley, California, U. The bottom-left diagram shows the value function for the equiprobable random policy, and the bottom-right diagram shows a greedy policy for this value function. 3 Quality profiles: Average cost to the goal vs. Examples of boundary value problems are consideredto compare the performance of the proposed method with that of the existing methods along with exact solution. The user should define the problem  Apply value iteration, policy iteration and Q learning to develop policies for agents to Two 'Maze Solving Problem' with different state size (8X8 and 15X15) to Although the rewards for each algorithm varies, the optimal policy in value  which is very clear about the grid world problem. You start by making an initial guess for the value function at each capital point (an initial guess of zero at each point for example). Prof. Simply put, planning everyone to full capacity, does not allow people to flex when problems inevitably occur. He (1999, 2000, 2006) developed the variational iteration method for solving linear, nonlinear, initial and boundary value problems. We can iteratively approximate the value using dynamic programming. This is an example of optimal substructure and shows how the value of the problem starting from t is related to the value of the problem starting from s. In Chapter 4, where they discuss Dynamic Programming techniques for solving basic RL problems, they discuss the Value Iteration algorithm and to demonstrate it they use the Gambler's Problem which is described as Many algorithms are expressed using iteration. Up until now, there haven't been problems with formulas iterating enough to get to the right answers, but suddenly it can't seem to go through enough iterations to finish (even if I've set the max # pretty high). EXAMPLE 3 Approximating a Dominant Eigenvalue Use the result of Example 2 to approximate the dominant eigenvalue of the matrix Solution After the sixth iteration of the power method in Example 2, we had obtained. POMDP Value Iteration Example. This is like a while loop in which all of the loop-control information (initialization- Value Function Iteration versus But especially for more complex problems, VFI is more likely to Example to show Euler eq. A New Iterative Method for Solving Initial Value Problems Mingshen Wu 1 and Weihu Hong 2 1Department of Math. Iteration is the process of carrying out a certain activity ( set of statements) again and again whereas Recursion is a process where a function which calls itself again and again until a certain condition i Outline Introduction Schur Decomposition The QR Iteration Methods for Symmetric matrices Conclusion Iterative Techniques For Solving Eigenvalue Problems P. Value iteration for discounted cost. colostate. 3) exists, then a generalization of Odoni [10] shows that any policy achieving the maxima in (1. The variable i is initialized above the for loop and its value is incremented inside the body of loop. 2 Discount = 0. Say you were asked to solve the initial value problem: y′ = x + 2y y(0) = 0. We propose a Policy iteration often converges in surprisingly few iterations. How good is this policy? Well, one way to figure it out is by simply iterating the policy over a value function for each state. The present value iteration ADP algorithm permits an arbitrary  problems, it is often infeasible to maintain a high density of point coverage over the . x = 3 From deterministic to stochastic planning problems Value iteration example 16. A VALUE ITERATION METHOD FOR THE 1 AVERAGE COST DYNAMIC PROGRAMMING PROBLEM by Dimitri P. Problems: ! This tree is usually infinite (why?) ! Same states appear over and over (why?) ! We would search once per state (why?) ! Idea: Value iteration ! Compute optimal values for all states all at once using successive approximations ! Will be a bottom-up dynamic program similar in cost to memoization In this paper, a fixed point iteration method is introduced for the numerical solution of second order two point boundary value problems. Value iteration includes: finding optimal value function + one policy extraction. Here's an example of a very simple iteration using the function 5 + x/2: Pick a number, say 0, and put it in for x. Value iteration is a commonly used and em pirically competitive method in solving many. Optimal Stopping under Partial Observation: Near-Value Iteration Enlu Zhou Abstract We propose a new approximate value iteration method, namely near-value iteration (NVI), to solve continuous-state optimal stopping problems under partial observation, which in general cannot be solved analytically and also pose a great challenge to numerical Chapter 5 Iteration 5. This paper studies a value function iteration algorithm that can be applied to almost all stationary dynamic programming problems. So, instead of waiting for the policy evaluation step to converge exactly to the value function v π, we could stop earlier. VINs can learn to plan, and are Academia. We only consider the problem for autonomous ODEs, but note that through (1. SubproblemAlgorithm: Determines how the iteration step is calculated. 10 Mar 2015 For example, if the agent c hooses the action North, i t m o ves North Using value iteration to solve sequential decision problems in games. , the optimal action at a state s is the same action at all times. One cycle of value iteration is faster than one cycle of policy iteration. Value Function Iteration We are going to focus on in nite horizon problems, where V is the unique Another example is p i (s) = cos FIXED POINT ITERATION METHOD. Policy Methods Problems with Value Iteration Value iteration repeats the Bellman updates: Problem 1: It’s slow – O(S 2A) per iteration Problem 2: The “max” at each state rarely changes Problem 3: The policy often converges long before the values a s s, a s,a,s’ s’ [Demo: value iteration (L9D2)] k=12 Noise = 0. First, consider the above example. We choose as the entering variable. At the end of an iteration the largest flux in the model during the current iteration (q max α) is compared with the time-averaged value of the largest flux (q ~ max α). results have shown that implementations of value iteration, a widely used iterative . If it is even, the value of n is divided by two. This is called iteration. The default value of ϵ l α is 10 −5; you can redefine this parameter. By using the Iteration method you can find the roots of the equation. Recall that it is this property that underlies the existence of a ow. However, its performance can be dramatically improved by eliminating redundant or useless backups, and by backing up states Lecture 3: Planning by Dynamic Programming Policy Iteration Extensions to Policy Iteration Modi ed Policy Iteration Does policy evaluation need to converge to v ˇ? Or should we introduce a stopping condition e. Examples In an MDP, we want an optimal policy π*: S x 0:H → A. easily use value iteration and policy extraction to solve our problem. In Java, the function-call mechanism supports the possibility of having a method call itself. ] Look at midpoint in range and compare to soughtValue. q ~ max α is the time-averaged value of the largest flux in the model during the current step. Universityof Kentucky 773 Anderson Tower Lexington, KY 40506-0046 Abstract Value Iteration is an inefficient algorithm for Markovdecision processes (MDPs) because it puts the majority of its effort into backing up the en- Computer Programs Power Method Power Method . Find file Copy path aerinkim added Sutton book's equation 377c875 May 27, 2018. SUMMARY A survey of probably the most efficient solution methods currently in use for the problems K+ = w2M+ and K+ = XK,\lr is presented. Iteration produces 32 lines of output, one from the initial statement and one more each time through the loop. Follow @python_fiddle Browser Version Not Supported Due to Python Fiddle's reliance on advanced JavaScript techniques, older browsers might have problems running it correctly. A reduction from parity objectives to limit-average objectives [20], followed by value iteration for graphs with limit- Problem 1. $ This produces V*, which in turn tells us how to act, namely following: $ Note: the infinite horizon optimal policy is stationary, i. value iteration can be useless. madani @cs. by carefully selecting features to approximate the value func-tion, we can compute value iteration backups in closed form. Abstract. ▫ The problem is to compute the correct Value Iteration Example. approximation such as variational iteration method, fixed point iteration and so on [6], variational iteration method has been used over the years to obtain an approximate solution of some boundary value problems, see [7, 8]. For notational convenience, let the first stage be designated as so that may be replaced by . We compare six different ways of value function iteration with regard to speed and precision. More specifically, given a function g defined on the real numbers with real values and given a point x 0 in the domain of g, the fixed point iteration is If your calculator has an ANS button, use it to keep the value from one iteration to substitute into the next iteration. [10,12,14] introduced OHAM for approximate solution of nonlinear problems of thin This process is called “value iteration”. The Newton-Raphson method does not always work, however. Each repetition of the process is a single iteration, and the outcome of each iteration is then the starting point of the next iteration. Repeat the process still convergence happens. 2 of Sutton&amp;Barto book is a relevant place to look at. edu Charles W. Value Function Iteration¶ Perhaps the most familiar method for solving all manner of dynamic programs is value function iteration. Applied MDP with Value Iteration to optimally choose path for an agent in a Stochastic Environment, in order to maximize its rewards markov-decision-processes value-iteration artificial-intelligence Value iteration is a method of computing the optimal policy and the optimal value of a Markov decision process. Section 6. , value iteration) into CNNs that can learn planners including mapping from observations to cost Value iteration technique discussed in the next section provides a possible solution to this. We have already encountered in chapter 6 the value iteration (VI) algorithm, which is similar to the DP algorithm and computes UNDISCOUNTED VALUE ITERATION IN MARKOV DECISION PROBLEMS (b) for the case N > 1, where the value-iteration method is the only practical way of locating maximal-gain policies. 26, the state one step up and one step to the left of the +10 reward state only had its value updated after three value iterations, in which each iteration involved a sweep through all of the states. If the equations are linear with respect to the parameters, the parameter estimates always converge in one iteration. Markov decision process problems. While their exact methods vary, they all can determine the optimum faster and more efficiently than Newton’s Method when the problems are complex. value iteration example problems

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