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4th IEEE Conference on Control Technology and Applications (IEEE CCTA)

作者： Ragi, Shankarachary Mittelmann, Hans D. South Dakota Sch Mines & Technol Dept Elect Engn Rapid City SD 57701 USA Arizona State Univ Sch Math & Stat Sci Tempe AZ 85281 USA

ISBN: (纸本)9781728171401

In this paper, we develop a Monte-Carlo based heuristic approach to approximate the objective function in long horizon optimal control problems. In this approach, we evolve the system state over multiple trajectories into the future while sampling the noise disturbances at each time-step, and find the weighted average of the costs along all the trajectories. We call these methods random sampling - multipath hypothesis propagation or RS-MHP. These methods (or variants) exist in the literature;however, the literature lacks convergence results for a generic class of nonlinear systems. This paper fills this knowledge gap to a certain extent. We derive convergence results for the cost approximation error from the MHP methods and discuss their convergence (in probability) as the sample size increases. As a case study, we apply RS-MHP to approximate the cost function in a linear quadratic control problem and demonstrate the benefits of our approach against an existing and closely related approximation approach called nominal belief-state optimization.

关键词： Long horizon optimal control cost approximation approximate dynamic programming multi-path hypothesis propagation

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Overcoming Mobility Poverty with Shared Autonomous Vehicles: A Learning-Based Optimization Approach for Rotterdam Zuid 11th

Overcoming Mobility Poverty with Shared Autonomous Vehicles:...

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11th International Conference on Computational Logistics (ICCL)

作者： Beirigo, Breno Schulte, Frederik Negenborn, Rudy R. Delft Univ Technol Delft Netherlands

ISBN: (纸本)9783030597474;9783030597467

Residents of cities' most disadvantaged areas face significant barriers to key life activities, such as employment, education, and healthcare, due to the lack of mobility options. Shared autonomous vehicles (SAVs) create an opportunity to overcome this problem. By learning user demand patterns, SAV providers can improve regional service levels by applying anticipatory relocation strategies that take into consideration when and where requests are more likely to appear. The nature of transportation demand, however, invariably creates learning biases towards servicing cities' most affluent and densely populated areas, where alternative mobility choices already abound. As a result, current disadvantaged regions may end up perpetually underserviced, therefore preventing all city residents from enjoying the benefits of autonomous mobility-on-demand (AMoD) systems equally. In this study, we propose an anticipatory rebalancing policy based on an approximate dynamic programming (ADP) formulation that processes historical demand data to estimate value functions of future system states iteratively. We investigate to which extent manipulating cost settings, in terms of subsidies and penalties, can adjust the demand patterns naturally incorporated into value functions to improve service levels of disadvantaged areas. We show for a case study in the city of Rotterdam, The Netherlands, that the proposed method can harness these cost schemes to better cater to users departing from these disadvantaged areas, substantially outperforming myopic and reactive benchmark policies.

关键词： Mobility poverty Shared autonomous vehicles approximate dynamic programming

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Improving System-of-Systems Agility through dynamic Reconfiguration

Improving System-of-Systems Agility through Dynamic Reconfig...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Fang, Zhemei Liao, Jingjing Zhou, Xiaozhou Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Wuhan Maritime Commun Res Inst Wuhan 430205 Peoples R China

ISBN: (纸本)9781728185262

Most System-of-Systems (SoS) problems face a highly dynamic, volatile, and uncertain environment. How to cope with the unpredictable and volatile changes is one of the key challenges for SoS practitioners. Agility, as an informative feature indicating an SoS's capability of effecting, coping with and exploiting changes, has not received sufficient attention in the context of SoS. This paper proposes a dynamic reconfiguration method that aims to improve the SoS agility from the perspectives of responding quickly to failures and recovering some amount of the SoS capability. This paper employs approximate dynamic programming method to compute the dynamic reconfiguration decisions that allow failed or degraded systems to be removed and the function-capability allocation to be changed quickly. A naval warfare SoS example takes a preliminary step towards demonstrating the effectiveness of the dynamic reconfiguration method, along with flexible architecture, in achieving the SoS agility in terms of responsiveness and resilience.

关键词： system-of-systems agility dynamic reconfiguration hierarchical representation approximate dynamic programming

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Actor-Critic Algorithm for Optimal Synchronization of Kuramoto Oscillator 7

Actor-Critic Algorithm for Optimal Synchronization of Kuramo...

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7th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Vrushabh, D. Shalini, K. Sonam, K. Veermata Jijabai Technol Inst EED Mumbai Maharashtra India

ISBN: (纸本)9781728159539

This paper constructs a reinforcement learning (RL) based algorithm of Actor-Critic (AC) for the optimal synchronism of the Kuramoto oscillator. This is accomplished through the Ott-Antonsen ansatz framework for the dynamics of large interactive unit networks. Besides, this approach reduces the infinite-dimensional dynamics to phase space flow, i.e., low dimensional dynamics for certain systems of globally coupled phase oscillators. The resulting Hamiltonian-Jacobi-Bellman (HJB) expression is extremely difficult to solve in general, therefore this paper introduces the AC method for learning approximate optimal control laws for the Kuramoto oscillator model. RL has been contemplated as one of the efficient methods to solve optimal control of non-linear systems. For a collection of non-homogeneous oscillators, the states are elucidated as phase angles, which is the modification of the model for a coupled Kuramoto oscillator. An admissible initial control policy for the Kuramoto oscillator model is designed and solved using RL giving an approximate solution of the optimal control problem. Finally, local synchronism of the coupled Kuramoto oscillator model is supported through simulations analysis.

关键词： Reinforcement learning Hamilton-Jacobi-Bellman approximate dynamic programming Kuramoto oscillator Mean-field game Order parameter

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Safe policy iteration: a monotonically improving approximate policy iteration approach

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 4348-4430页

作者： Alberto Maria Metelli Matteo Pirotta Daniele Calandriello Marcello Restelli DEIB Politecnico di Milano Milano Italy Facebook AI Research Paris France Istituto Italiano di Tecnologia Genova Italy

This paper presents a study of the policy improvement step that can be usefully exploited by approximate policy-iteration algorithms. When either the policy evaluation step or the policy improvement step returns an approximated result, the sequence of policies produced by policy iteration may not be monotonically increasing, and oscillations may occur. To address this issue, we consider safe policy improvements, i.e., at each iteration, we search for a policy that maximizes a lower bound to the policy improvement w.r.t. the current policy, until no improving policy can be found. We propose three safe policy-iteration schemas that differ in the way the next policy is chosen w.r.t. the estimated greedy policy. Besides being theoretically derived and discussed, the proposed algorithms are empirically evaluated and compared on some chain-walk domains, the prison domain, and on the Blackjack card game.

关键词： reinforcement learning approximate dynamic programming approximate policy iteration policy oscillation policy chattering Markov decision process

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Comparison of Manual and Automated Decision-Making with a Logistics Serious Game 11th

Comparison of Manual and Automated Decision-Making with a Lo...

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11th International Conference on Computational Logistics (ICCL)

作者： Mes, Martijn van Heeswijk, Wouter Univ Twente Dept Ind Engn & Business Informat Syst Enschede Netherlands

ISBN: (纸本)9783030597474;9783030597467

This paper presents a logistics serious game that describes an anticipatory planning problem for the dispatching of trucks, barges, and trains, considering uncertainty in future container arrivals. The problem setting is conceptually easy to grasp, yet difficult to solve optimally. For this problem, we deploy a variety of benchmark algorithms, including two heuristics and two reinforcement learning implementations. We use the serious game to compare the manual performance of human decision makers with those algorithms. Furthermore, the game allows humans to create their own automated planning rules, which can also be compared with the implemented algorithms and manual game play. To illustrate the potential use of the game, we report the results of three gaming sessions: with students, with job seekers, and with logistics professionals. The experimental results show that reinforcement learning typically outperforms the human decision makers, but that the top tier of humans come very close to this algorithmic performance.

关键词： Intermodal transport Serious gaming Reinforcement learning approximate dynamic programming Heuristics

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Data-Driven Control of Unknown Systems: A Linear programming Approach 21st

Data-Driven Control of Unknown Systems: A Linear Programming...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Tanzanakis, Alexandros Lygeros, John Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland

We consider the problem of discounted optimal state-feedback regulation for general unknown deterministic discrete-time systems. It is well known that open-loop instability of systems, non-quadratic cost functions and complex nonlinear dynamics, as well as the on-policy behavior of many reinforcement learning (RL) algorithms, make the design of model-free optimal adaptive controllers a challenging task. We depart from commonly used least-squares and neural network approximation methods in conventional model-free control theory, and propose a novel family of data-driven optimization algorithms based on linear programming, off-policy Q-learning and randomized experience replay. We develop both policy iteration (PI) and value iteration (VI) methods to compute an approximate optimal feedback controller with high precision and without the knowledge of a system model and stage cost function. Simulation studies confirm the effectiveness of the proposed methods. Copyright (C) 2020 The Authors.

关键词： linear programming Q-learning approximate dynamic programming data-driven control

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Decentralized Formation Shape Control of UAV Swarm using dynamic programming 29

Decentralized Formation Shape Control of UAV Swarm using Dyn...

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX

作者： Azam, Md Ali Ragi, Shankarachary South Dakota Sch Mines & Technol Dept Elect Engn Rapid City SD 57701 USA

ISBN: (纸本)9781510636248;9781510636231

Formation control of unmanned aerial vehicles (UAVs) has many applications including target tracking, surveillance, terrain mapping, precision agriculture, etc. Although many centralized control methods (single command center/computer controlling the UAVs) exist, there are no standard decentralized control frameworks in the literature. In this paper, we present a novel UAV swarm formation control approach based on a decision theoretic approach. Specifically, we pose the decentralized swarm motion control problem as a Decentralized Markov Decision Process (Dec-MDP). Here, the objective is to drive the swarm from an initial geographical region to another geographical region where the swarm must lie on a certain geometrical surface (e.g., surface of a sphere). As most decision theoretic formulations suffer from the curse of dimensionality, we adapt an approximate dynamic programming method called nominal belief-state optimization (NBO) to solve the formation control problem approximately. We perform simulation studies in MATLAB to validate the performance of the algorithms.

关键词： Swarm intelligence formation control decentralized Markov decision process approximate dynamic programming

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The multi-vehicle stochastic-dynamic inventory routing problem for bike sharing systems

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Business Research 2020年第1期13卷 69-92页

作者： Brinkmann, Jan Ulmer, Marlin W. Mattfeld, Dirk C. Institut für Wirtschaftsinformatik Technische Universität Braunschweig Mühlenpfordtstraße 23 Braunschweig 38106 Germany

We address the operational management of station-based bike sharing systems (BSSs). In BSSs, users can spontaneously rent and return bikes at any stations in the system. Demand is driven by commuter, shopping, and leisure activities. This demand constitutes a regular pattern of bike usage over the course of the day but also shows a significant short-term uncertainty. Due to the heterogeneity and the uncertainty in demand, stations may run out of bikes or congest during the day. At empty stations, no rental demand can be served. At full stations, no return demand can be served. To avoid unsatisfied demand, providers dynamically relocate bikes between stations in reaction of current shortages or congestion, but also in anticipation of potential future demand. For this real-time decision problem, we present a method that anticipates potential future demands based on historical observations and that coordinates the fleet of vehicles accordingly. We apply our method for two case studies based on real-world data of the BSSs in Minneapolis and San Francisco. We show that our policy outperforms benchmark policies from the literature. Moreover, we analyze how the interplay between anticipation and coordination is essential for the successful operational management of BSSs. Finally, we reveal that the value of coordination and anticipation based on the demand-structure of the BSS under consideration. © 2019, The Author(s).

关键词： approximate dynamic programming Bike sharing dynamic vehicle routing Inventory routing

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Advances in Tactical & Operational Planning for Less-than-Truckload Carriers

Advances in Tactical & Operational Planning for Less-than-Tr...

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作者： Baubaid, Ahmad Ali Georgia Institute of Technology

学位级别：博士

This thesis explores tactical and operational planning problems in the context of the Less-than-Truckload (LTL) industry. LTL carriers transport shipments that occupy a small fraction of trailer capacity, and, thus, rely on the consolidation of freight from multiple shippers to achieve economies of scale. The first part of this thesis focuses on tactical planning operations of LTL carriers. In particular, in Chapter 2, we study the service network design problem confronted by LTL carriers ahead of an operating season. This problem includes determining: (1) the number of services (trailers) to operate between each pair of terminals, and (2) a load plan which specifies the sequence of transfer terminals that freight with a given origin and destination will visit. Traditionally, for every terminal and every ultimate destination, a load plan specifies a unique next terminal. We introduce the p-alt model, which generalizes traditional load plans by allowing decision-makers to specify a desired number of next terminal options for terminal-destination pairs using a vector p. We compare a number of exact and heuristic approaches for solving a two-stage stochastic variant of the p-alt model. Using this model, we show that by explicitly considering demand uncertainty and by merely allowing up to two next terminal options for terminal-destination pairs in the load plans, carriers can generate substantial cost savings; cost savings that are comparable to those yielded by adopting load plans that allow for any next terminal to be a routing option for terminal-destination pairs. Moreover, by using these more flexible load plans, carriers can generate cost savings in the order of 10% over traditional load plan designs obtained by deterministic models. The second part of the thesis shifts to an operational setting relating to how freight is routed through the carrier's service network. As the daily freight quantities handled by a carrier are uncertain, freight routes are dynamica

关键词： Freight transportation Less-than-truckload Load planning approximate dynamic programming Value function approximation

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