## approximate dynamic programming vs dynamic programming

(click here to download paper) See also the companion paper below: Simao, H. P. A. George, Warren B. Powell, T. Gifford, J. Nienow, J. Deterministic stepsize formulas can be frustrating since they have parameters that have to be tuned (difficult if you are estimating thousands of values at the same time). Thus, a decision made at a single state can provide us with information about Ryzhov, I. O., W. B. Powell, “Approximate Dynamic Programming with Correlated Bayesian Beliefs,” Forty-Eighth Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, Sept. 29-Oct. 1, 2010. What did work well is best described as “lookup table with structure.” The structure we exploit is convexity and monotonicity. Because the optimal policy only works on single link problems with one type of product, while the other is scalable to much harder problems. These results call into question simulations that examine the effect of advance information which do not use robust decision-making, a property that we feel reflects natural human behavior. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. The results show that if we allocate aircraft using approximate dynamic programming, the effect of uncertainty is significantly reduced. Powell, “The Dynamic Assignment Problem,” Transportation Science, Vol. In addition to 2-17 (2010). Dynamic Programming is an umbrella encompassing many algorithms. 1, pp. The book includes dozens of algorithms written at a level that can be directly translated to code. 1, pp. It shows how math programming and machine learning can be combined to solve dynamic programs with many thousands of dimensions, using techniques that are easily implemented on a laptop. Ma, J. and W. B. Powell, “A convergent recursive least squares policy iteration algorithm for multi-dimensional Markov decision process with continuous state and action spaces,” IEEE Conference on Approximate Dynamic Programming and Reinforcement Learning (part of IEEE Symposium on Computational Intelligence), March, 2009. 36, No. Approximate dynamic programming is a powerful class of algorithmic strategies for solving stochastic optimization problems where optimal decisions can be characterized using Bellman’s optimality equa-tion, but where the characteristics of the problem make … This technique worked very well for single commodity problems, but it was not at all obvious that it would work well for multicommodity problems, since there are more substitution opportunities. Powell, W. B., “Approximate Dynamic Programming I: Modeling,” Encyclopedia of Operations Research and Management Science, John Wiley and Sons, (to appear). This invited tutorial unifies different communities working on sequential decision problems. The book is aimed at an advanced undergraduate/masters level audience with a good course in probability and statistics, and linear programming (for some applications). This paper reviews a number of popular stepsize formulas, provides a classic result for optimal stepsizes with stationary data, and derives a new optimal stepsize formula for nonstationary data. 342-352, 2010. Approximate Dynamic Programming, Second Edition uniquely integrates four distinct disciplines—Markov decision processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully approach, model, and solve a … and T. Carvalho, “Dynamic Control of Logistics Queueing Networks for Large Scale Fleet Management,” Transportation Science, Vol. The experimental comparisons against multistage nested Benders (which is very slow) and more classical rolling horizon procedures suggests that it works very well indeed. Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. of approximate dynamic programming in industry. The proof assumes that the value function can be expressed as a finite combination of known basis functions. Bellman’s equation can be solved by the average-cost exact LP (ELP): 0 (2) 0 @ 9 7 6 Note that the constraints 0 @ 937 6 7can be replaced by 9 7 Y therefore we can think of problem (2) as an LP. The model gets drivers home, on weekends, on a regular basis (again, closely matching historical performance). 3, pp. 3, pp. (c) Informs. The experiments show that the SPAR algorithm, even when applied to nonseparable approximations, converges much more quickly than Benders decomposition. The book is written for both the applied researcher looking for suitable solution approaches for particular problems as well as for the theoretical researcher looking for effective and efficient methods of stochastic dynamic optimization and approximate dynamic programming (ADP). This article appeared in the Informs Computing Society Newsletter. A formula is provided when these quantities are unknown. 2, pp. It describes a new algorithm dubbed the Separable Projective Approximation Routine (SPAR) and includes 1) a proof that the algorithm converges when we sample all intervals infinitely often, 2) a proof that the algorithm produces an optimal solution when we only sample the optimal solution of our approximation at each iteration, when applied to separable problems, 3) a bound when the algorithm is applied to nonseparable problems such as two-stage stochastic programs with network resource, and 4) computational comparisons against deterministic approximations and variations of Benders decomposition (which is provably optimal). This paper studies the statistics of aggregation, and proposes a weighting scheme that weights approximations at different levels of aggregation based on the inverse of the variance of the estimate and an estimate of the bias. You can use textbook backward dynamic programming if there is only one product type, but real problems have multiple products. 399-419 (2004). Past studies of this topic have used myopic models where advance information provides a major benefit over no information at all. Approximate Dynamic Programming Much of our work falls in the intersection of stochastic programming and dynamic programming. Warren B. Powell. But things do get easier with practice. Powell, “Dynamic Programming Approximations for Stochastic, Time-Staged Integer Multicommodity Flow Problems,” Informs Journal on Computing, Vol. (c) Informs. 7, pp. The model represents drivers with 15 attributes, capturing domicile, equipment type, days from home, and all the rules (including the 70 hour in eight days rule) governing drivers. 4, pp. 210-237 (2009). This paper introduces the use of linear approximations of value functions that are learned adaptively. The material in this book is motivated by numerous industrial applications undertaken at CASTLE Lab, as well as a number of undergraduate senior theses. This conference proceedings paper provides a sketch of a proof of convergence for an ADP algorithm designed for problems with continuous and vector-valued states and actions. It often is the best, and never works poorly. Let us now introduce the linear programming approach to approximate dynamic programming. 39-57 (2011), DOI: 10.1145/2043635.2043636. Praise for the First Edition Finally, a book devoted to dynamic programming and written using the language of operations research (OR)! It provides an easy, high-level overview of ADP, emphasizing the perspective that ADP is much more than an algorithm – it is really an umbrella for a wide range of solution procedures which retain, at their core, the need to approximate the value of being in a state. We build on the literature that has addressed the well-known problem of multidimensional (and possibly continuous) states, and the extensive literature on model-free dynamic programming which also assumes that the expectation in Bellman’s equation cannot be computed. We then describe some recent research by the authors on approximate policy iteration algorithms that offer convergence guarantees (with technical assumptions) for both parametric and nonparametric architectures for the value function. Dynamic programming is a fancy name for efficiently solving a big problem by breaking it down into smaller problems and caching those solutions to avoid solving them more than once. A few years ago we proved convergence of this algorithmic strategy for two-stage problems (click here for a copy). The new method performs well in numerical experiments conducted on an energy storage problem. 18, No. Using the contextual domain of transportation and logistics, this paper describes the fundamentals of how to model sequential decision processes (dynamic programs), and outlines four classes of policies. 24. http://dx.doi.org/10.1109/TAC.2013.2272973. (c) Informs. This paper addresses four problem classes, defined by two attributes: the number of entities being managed (single or many), and the complexity of the attributes of an entity (simple or complex). 50, No. Approximate Dynamic Programming 1 / 24 Dynamic Programming is not often very intuitive or straightforward. As a result, it often has the appearance of an “optimizing simulator.” This short article, presented at the Winter Simulation Conference, is an easy introduction to this simple idea. I think this helps put ADP in the broader context of stochastic optimization. This article is a brief overview and introduction to approximate dynamic programming, with a bias toward operations research. Godfrey, G. and W.B. Powell and S. Kulkarni, “Value Function Approximation Using Hierarchical Aggregation for Multiattribute Resource Management,” Journal of Machine Learning Research, Vol. J. Nascimento, W. B. Powell, “An Optimal Approximate Dynamic Programming Algorithm for Concave, Scalar Storage Problems with Vector-Valued Controls,” IEEE Transactions on Automatic Control, Vol. One of the oldest problems in dynamic programming arises in the context of planning inventories. This is the Python project corresponding to my Master Thesis "Stochastic Dyamic Programming applied to Portfolio Selection problem". Ryzhov, I. and W. B. Powell, “Bayesian Active Learning with Basis Functions,” IEEE Workshop on Adaptive Dynamic Programming and Reinforcement Learning, Paris, April, 2011. Dynamic programming approach extends divide and conquer approach with two techniques (memoization and tabulation) that both have a purpose of storing and re-using sub-problems solutions that may drastically improve performance. 56, No. The stochastic programming literature, on the other hands, deals with the same sorts of higher dimensional vectors that are found in deterministic math programming. 40-54 (2002). 231-249 (2002). “What you should know about approximate dynamic programming,” Naval Research Logistics, Vol. Value function approximation. 21-39 (2002). The OR community tends to work on problems with many simple entities. Section 2 provides a historical perspective of the evolution of dynamic programming to … In this paper, we consider a multiproduct problem in the context of a batch service problem where different types of customers wait to be served. As a result, estimating the value of resource with a particular set of attributes becomes computationally difficult. W. B. Powell, J. Ma, “A Review of Stochastic Algorithms with Continuous Value Function Approximation and Some New Approximate Policy Iteration Algorithms for Multi-Dimensional Continuous Applications,” Journal of Control Theory and Applications, Vol. 814-836 (2004). Powell, W.B., “Merging AI and OR to Solve High-Dimensional Resource Allocation Problems using Approximate Dynamic Programming” Informs Journal on Computing, Vol. Approximate dynamic programming: solving the curses of dimensionality, published by John Wiley and Sons, is the first book to merge dynamic programming and math programming using the language of approximate dynamic programming. Q-Learning is a specific algorithm. 14 answers. (c) Informs. We will focus on approximate methods to ﬁnd good policies. Due to its generality, reinforcement learning is studied in many disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, and statistics.In the operations research and control literature, reinforcement learning is called approximate dynamic programming, or neuro-dynamic programming. There are tonnes of dynamic programming practise problems online, which should help you get better at knowing when to apply dynamic programming, and how to apply it better. DOI 10.1007/s13676-012-0015-8. (c) Informs. This is the first book to bridge the growing field of approximate dynamic programming with operations research. The value functions produced by the ADP algorithm are shown to accurately estimate the marginal value of drivers by domicile. 178-197 (2009). The book emphasizes solving real-world problems, and as a result there is considerable emphasis on proper modeling. Dynamic programming has often been dismissed because it suffers from “the curse of dimensionality.” In fact, there are three curses of dimensionality when you deal with the high-dimensional problems that typically arise in operations research (the state space, the outcome space and the action space). Powell, Approximate Dynamic Programming, John Wiley and Sons, 2007. The AI community often works on problems with a single, complexity entity (e.g. George, A. and W.B. Test datasets are available at http://www.castlelab.princeton.edu/datasets.htm. Powell, W.B. W. B. Powell, H. Simao, B. Bouzaiene-Ayari, “Approximate Dynamic Programming in Transportation and Logistics: A Unified Framework,” European J. on Transportation and Logistics, Vol. The algorithm is well suited to continuous problems which requires that the function that captures the value of future inventory be finely discretized, since the algorithm adaptively generates break points for a piecewise linear approximation. There is a detailed discussion of stochastic lookahead policies (familiar to stochastic programming). This paper adapts the CAVE algorithm to stochastic multistage problems. Powell, “An Adaptive Dynamic Programming Algorithm for Dynamic Fleet Management, I: Single Period Travel Times,” Transportation Science, Vol. 336-352, 2011. We resort to hierarchical aggregation schemes. What is surprising is that the weighting scheme works so well. For the advanced Ph.D., there is an introduction to fundamental proof techniques in “why does it work” sections. The proof is for a form of approximate policy iteration. There are a number of problems in approximate dynamic programming where we have to use coarse approximations in the early iterations, but we would like to transition to finer approximations as we collect more information. 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