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检索条件"任意字段=2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL 2014"

共 247 条记录，以下是131-140 订阅

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Longitudinal Control of Hypersonic Vehicles Based on Direct Heuristic dynamic programming Using ANFIS

Longitudinal Control of Hypersonic Vehicles Based on Direct ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Luo, Xiong Chen, Yi Si, Jennie Liu, Feng USTB Sch Comp & Commun Engn Beijing 100083 Peoples R China Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA

ISBN: (纸本)9781479914845

Since the launch of the scramjet, recent years have witnessed a growing interest in the study of airbreathing hypersonic vehicles. Due to its strong coupling characteristics, high nonlinearity, and uncertain parameters, the control of hypersonic vehicle becomes a great challenge. To deal with those design issues, we propose an adaptive learning control method based on direct heuristic dynamic programming (direct HDP), which is used to track the angle of attack despite the presence of bounded uncertain parameters. Inspired by the adaptive critic designs, direct HDP is one of the adaptive dynamic programming (ADP) methods, which is a modelfree reinforcement learning algorithm using the online learning scheme to solve dynamic control problems in realistic complex environment. In this paper, this direct HDP method is improved by embedding the fuzzy neural network (FNN) in the controller design to enhance its self-learning ability and robustness. Simulation results are provided to demonstrate the effectiveness of our proposed method.

关键词： Hypersonic vehicles

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Beyond exponential utility functions: A variance-adjusted approach for risk-averse reinforcement learning

Beyond exponential utility functions: A variance-adjusted ap...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Abhijit A. Gosavi Sajal K. Das Susan L. Murray Department of Engineering Management and Systems Engineering Missouri University of Science and Technology Rolla MO Department of Computer Science Missouri University of Science and Technology Rolla MO

Utility theory has served as a bedrock for modeling risk in economics. Where risk is involved in decision-making, for solving Markov decision processes (MDPs) via utility theory, the exponential utility (EU) function has been used in the literature as an objective function for capturing risk-averse behavior. The EU function framework uses a so-called risk-averseness coefficient (RAC) that seeks to quantify the risk appetite of the decision-maker. Unfortunately, as we show in this paper, the EU framework suffers from computational deficiencies that prevent it from being useful in practice for solution methods based on reinforcement learning (RL). In particular, the value function becomes very large and typically the computer overflows. We provide a simple example to demonstrate this. Further, we show empirically how a variance-adjusted (VA) approach, which approximates the EU function objective for reasonable values of the RAC, can be used in the RL algorithm. The VA framework in a sense has two objectives: maximize expected returns and minimize variance. We conduct empirical studies on a VA-based RL algorithm on the semi-MDP (SMDP), which is a more general version of the MDP. We conclude with a mathematical proof of the boundedness of the iterates in our algorithm.

关键词： Equations Linear programming Mathematical model Measurement Markov processes learning (artificial intelligence) Computers

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A comparison of approximate dynamic programming techniques on benchmark energy storage problems: Does anything work?

A comparison of approximate dynamic programming techniques o...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Daniel R. Jiang Thuy V. Pham Warren B. Powell Daniel F. Salas Warren R. Scott Department of Electrical & Electronics Enzineering Dehradun India Graphic Era University Dehradun India School of Rlectronics Dehradun India Graphic Era Hill University Bhimtal India

As more renewable, yet volatile, forms of energy like solar and wind are being incorporated into the grid, the problem of finding optimal control policies for energy storage is becoming increasingly important. These sequential decision problems are often modeled as stochastic dynamic programs, but when the state space becomes large, traditional (exact) techniques such as backward induction, policy iteration, or value iteration quickly become computationally intractable. Approximate dynamic programming (ADP) thus becomes a natural solution technique for solving these problems to near-optimality using significantly fewer computational resources. In this paper, we compare the performance of the following: various approximation architectures with approximate policy iteration (API), approximate value iteration (AVI) with structured lookup table, and direct policy search on a benchmarked energy storage problem (i.e., the optimal solution is computable).

关键词： Function approximation Energy storage Benchmark testing Mathematical model Approximation algorithms Equations

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Finite-horizon optimal control design for uncertain linear discrete-time systems

Finite-horizon optimal control design for uncertain linear d...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Qiming Zhao Hao Xu S. Jagannathan Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

In this paper, the finite-horizon optimal adaptive control design for linear discrete-time systems with unknown system dynamics by using adaptive dynamic programming (ADP) is presented. In the presence of full state feedback, the terminal state constraint is incorporated in solving the optimal feedback control via the Bellman equation. The optimal regulation of the uncertain linear system is solved in a forward-in-time and online manner without using value and/or policy iterations. Due to the nature of finite horizon, the stability of the closed-loop system is involved but verified by using Lyapunov theory. The effectiveness of the proposed method is verified by simulation results.

关键词： Optimal control Vectors Equations dynamic programming Mathematical model Linear systems learning (artificial intelligence)

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High-order local dynamic programming

High-order local dynamic programming

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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Yuval Tassa Emanuel Todorov Interdisciplinary Center of Neural Computation Hebrew University Jerusalem Israel Applied Mathematics and Computer Science & Engineering University of Washington Seattle USA

We describe a new local dynamic programming algorithm for solving stochastic continuous Optimal Control problems. We use cubature integration to both propagate the state distribution and perform the Bellman backup. The algorithm can approximate the local policy and cost-to-go with arbitrary function bases. We compare the classic quadratic cost-to-go/linear-feedback controller to a cubic cost-to-go/quadratic policy controller on a 10-dimensional simulated swimming robot, and find that the higher order approximation yields a more general policy with a larger basin of attraction.

关键词： Approximation methods Heuristic algorithms Mathematical model Equations Trajectory dynamic programming Noise

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Bayesian active learning with basis functions

Bayesian active learning with basis functions

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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Ilya O. Ryzhov Warren B. Powell Operations Research and Financial Engineering Princeton University Princeton NJ USA

A common technique for dealing with the curse of dimensionality in approximate dynamic programming is to use a parametric value function approximation, where the value of being in a state is assumed to be a linear combination of basis functions. Even with this simplification, we face the exploration/exploitation dilemma: an inaccurate approximation may lead to poor decisions, making it necessary to sometimes explore actions that appear to be suboptimal. We propose a Bayesian strategy for active learning with basis functions, based on the knowledge gradient concept from the optimal learning literature. The new method performs well in numerical experiments conducted on an energy storage problem.

关键词： Tin Function approximation Bayesian methods Covariance matrix Mathematical model dynamic programming

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N-step optimal time-invariant trajectory tracking control for a class of nonlinear systems

N-step optimal time-invariant trajectory tracking control fo...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Ruizhuo Song Huaguang Zhang School of Information Science and Engineering Northeastern University Shenyang China

In this paper, the time-invariant trajectory tracking control problem under N-step control is solved by finite horizon approximate dynamic programming (ADP) algorithms. At first, we convert the tracking control problem for time-invariant trajectory into a output regulation problem. The cost function guarantees the energy is minimum. Secondly, the regulation control scheme is proposed using finite horizon ADP technique to obtain the N-step control. Then two theorems are used to prove the convergence of the proposed control algorithm. Finally, the simulation is given to demonstrate the effectiveness and feasibility of the control scheme.

关键词： Trajectory Cost function Nonlinear systems Optimal control dynamic programming Convergence Mathematical model

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Optimal control for a class of nonlinear systems with state delay based on adaptive dynamic programming with ε-error bound

Optimal control for a class of nonlinear systems with state ...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Xiaofeng Lin Nuyun Cao Yuzhang Lin School of Electrical Engineering Guangxi University Nanning China Department of Electrical Engineering Tsinghua University Beijing China

In this paper, a finite-horizon ε-optimal control for a class of nonlinear systems with state delay is proposed by adaptive dynamic programming (ADP) algorithm. First of all, the performance index function is defined and the Hamilton-Jacobi-Bellman (HJB) equation is obtained for the problem, the convergence of the iterative algorithm is also presented. Then, ADP algorithm for finite-horizon optimal control is introduced with an ε-error bound so as to get the ε-optimal control, and BP neural network is used to implement ADP algorithm. At last, an example is given to demonstrate the effectiveness of the proposed algorithm.

关键词： Performance analysis Optimal control Delays Heuristic algorithms Nonlinear systems dynamic programming Neural networks

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Feedback controller parameterizations for reinforcement learning

Feedback controller parameterizations for Reinforcement Lear...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： John W. Roberts Ian R. Manchester Russ Tedrake MIT CSAIL Cambridge MA USA

reinforcement learning offers a very general framework for learning controllers, but its effectiveness is closely tied to the controller parameterization used. Especially when learning feedback controllers for weakly stable systems, ineffective parameterizations can result in unstable controllers and poor performance both in terms of learning convergence and in the cost of the resulting policy. In this paper we explore four linear controller parameterizations in the context of REINFORCE, applying them to the control of a reaching task with a linearized flexible manipulator. We find that some natural but naive parameterizations perform very poorly, while the Youla Parameterization (a popular parameterization from the controls literature) offers a number of robustness and performance advantages.

关键词： adaptive control Torque Observers Trajectory Transfer functions Linear systems Kalman filters

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adaptive dynamic programming for optimal control of unknown nonlinear discrete-time systems

Adaptive dynamic programming for optimal control of unknown ...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Derong Liu Ding Wang Dongbin Zhao Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China

An intelligent optimal control scheme for unknown nonlinear discrete-time systems with discount factor in the cost function is proposed in this paper. An iterative adaptive dynamic programming (ADP) algorithm via globalized dual heuristic programming (GDHP) technique is developed to obtain the optimal controller with convergence analysis. Three neural networks are used as parametric structures to facilitate the implementation of the iterative algorithm, which will approximate at each iteration the cost function, the optimal control law, and the unknown nonlinear system, respectively. Two simulation examples are provided to verify the effectiveness of the presented optimal control approach.

关键词： Artificial neural networks Riccati equations Integrated optics Optimal control Neurons

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