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检索条件"任意字段=IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning"

共 307 条记录，以下是51-60 订阅

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An Adaptive dynamic programming Algorithm to Solve Optimal Control of Uncertain Nonlinear Systems

An Adaptive Dynamic Programming Algorithm to Solve Optimal C...

引用

ieee symposium on Adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Cui, Xiaohong Luo, Yanhong Zhang, Huaguang Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9781479945528

In this paper, an approximate optimal control method based on adaptive dynamic programming(ADP) is discussed for completely unknown nonlinear system. An online critic-action-identifier algorithm is developed using neural network systems, where the critic -action networks approximate the optimal value function and optimal control and the other two neural networks approximates the unknown system. Furthermore the adaptive tuning laws are given based on Lyapunov approach, which ensures the uniform ultimate bounded stability of the closed-loop system. Finally, the effectiveness is demonstrated by a simulation example.

关键词： Closed loop systems

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Neural-Network-Based reinforcement learning Controller for Nonlinear Systems with Non-symmetric Dead-zone Inputs

Neural-Network-Based Reinforcement Learning Controller for N...

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ieee symposium on Adaptive dynamic programming and reinforcement learning

作者： Zhang, Xin Zhang, Huaguang Liu, Derong Kim, Yongsu Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

ISBN: (纸本)9781424427611

A novel adaptive-critic-based NN controller using reinforcement learning is developed for a class of nonlinear systems with non-symmetric dead-zone inputs. The adaptive critic NN controller uses two NNs: the critic NN is used to approximate the strategic utility function, and the output of action NN is used to approximate the unknown nonlinear function and to minimize the strategic utility function. The tuning of the NNs is performed online without an explicit offline learning phase. The uniformly ultimate boundedness of the close-loop tracking error is derived by using using the Lyapunov method. Finally, a numerical example is included to show the effectiveness of the theoretical results.

关键词： Nonlinear systems

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Exponential Moving Average Q-learning Algorithm

Exponential Moving Average Q-Learning Algorithm

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4th ieee international symposium on Adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Awheda, Mostafa D. Schwartz, Howard M. Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

ISBN: (纸本)9781467359252

A multi-agent policy iteration learning algorithm is proposed in this work. The Exponential Moving Average (EMA) mechanism is used to update the policy for a Q-learning agent so that it converges to an optimal policy against the policies of the other agents. The proposed EMA Q-learning algorithm is examined on a variety of matrix and stochastic games. Simulation results show that the proposed algorithm converges in a wider variety of situations than state-of-the-art multi-agent reinforcement learning (MARL) algorithms.

关键词： Policies Moving average optimal strategy mucin 1 European Monetary Agreement European Medicines Agency Exponential algorithms Internet learning Agent (ILA) stochastic games Agents

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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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4th ieee international symposium on Adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Zhao, Qiming Xu, Hao Jagannathan, S. Missouri Univ S&T Dept Elect & Comp Engn Rolla MO 65409 USA

ISBN: (纸本)9781467359252

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： Finite-horizon Optimal Control Q-learning Optimal Control Adaptive Estimator Linear System

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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： Jiang, Daniel R. Pham, Thuy V. Powell, Warren B. Salas, Daniel F. Scott, Warren R.

ISBN: (纸本)9781479945528

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： dynamic programming energy storage power engineering computing power system management renewable energy sources table lookup ADP API AVI approximate dynamic programming approximate policy iteration approximate value iteration backward induction dynamic programming techniques energy storage control policy lookup table natural solution technique solar energy stochastic dynamic programs wind energy Approximation algorithms Benchmark testing Energy storage Equations Function approximation Mathematical model Table lookup dynamic programming energy storage Power system management AVI Benchmark testing Power engineering computing function approximation Approximation algorithms Adenosine Diphosphate Automatic data processing Renewable energy renewable energy sources Wind energy Solar Energy

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approximate reinforcement learning: An overview

Approximate reinforcement learning: An overview

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ieee symposium on Adaptive dynamic programming and reinforcement learning

作者： Buşoniu, Lucian Ernst, Damien De Schutter, Bart Babuška, Robert Delft Center for Systems and Control Delft Univ. of Technology Netherlands Research Associate of the FRS-FNRS Systems and Modeling Unit University of Liège Liège Belgium

ISBN: (纸本)9781424498888

reinforcement learning (RL) allows agents to learn how to optimally interact with complex environments. Fueled by recent advances in approximation-based algorithms, RL has obtained impressive successes in robotics, artificial intelligence, control, operations research, etc. However, the scarcity of survey papers about approximate RL makes it difficult for newcomers to grasp this intricate field. With the present overview, we take a step toward alleviating this situation. We review methods for approximate RL, starting from their dynamic programming roots and organizing them into three major classes: approximate value iteration, policy iteration, and policy search. Each class is subdivided into representative categories, highlighting among others offline and online algorithms, policy gradient methods, and simulation-based techniques. We also compare the different categories of methods, and outline possible ways to enhance the reviewed algorithms. © 2011 ieee.

关键词： reinforcement learning

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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 ...

引用

4th ieee international symposium on Adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Lin, Xiaofeng Cao, Nuyun Lin, Yuzhang Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9781467359252

In this paper, a finite-horizon epsilon-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 epsilon-error bound so as to get the epsilon-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： Adaptive dynamic programming state delay epsilon-optimal control finite time nonlinear systems

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approximate optimal control-based neurocontroller with a state observation system for seedlings growth in greenhouse

Approximate optimal control-based neurocontroller with a sta...

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ieee international symposium on approximate dynamic programming and reinforcement learning

作者： Patino, H. D. Pucheta, J. A. Schugurensky, C. Fullana, R. Kuchen, B. Univ Nacl San Juan Inst Automat Fac Ingn Av San Martin 1109 Oeste RA-5400 San Juan Argentina

ISBN: (纸本)9781424407064

In this paper, an approximate optimal control-based neurocontroller for guiding the seedlings growth in greenhouse is presented. The main goal of this approach is to obtain a close-loop operation with a state neurocontroller, whose design is based on approximate optimal control theory. The neurocontroller drives the progress of the crop growth development while minimizing a predefined cost function in terms of operative costs and final state errors under physical constraints on process variables and actuator signals. The aim is to find an approximate optimal control policy to guide the development of tomato seedlings from an initial to a desired state by controlling the greenhouse's microclimate. In this paper we propose an indirect measuring of the seedlings growth state using artificial vision. In order to show the performance and practical feasibility of the proposed approach, an experiment was carried out for the development of tomato seedings.

关键词： Greenhouses

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Free Energy based Policy Gradients

Free Energy based Policy Gradients

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4th ieee international symposium on Adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Theodorou, Evangelos A. Najemnik, Jiri Todorov, Emo Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA Univ Washington Dept Appl Math Seattle WA 98195 USA

ISBN: (纸本)9781467359252

Despite the plethora of reinforcement learning algorithms in machine learning and control, the majority of the work in this area relies on discrete time formulations of stochastic dynamics. In this work we present a new policy gradient algorithm for reinforcement learning in continuous state action spaces and continuous time for free energy-like cost functions. The derivation is based on successive application of Girsanov's theorem and the use of the Radon Nikodym derivative as formulated for Markov diffusion processes. The resulting policy gradient is reward weighted. The use of Radon Nikodym extends analysis and results to more general models of stochasticity in which jump diffusions processes are considered. We apply the resulting algorithm in two simple examples for learning attractor landscapes in rhythmic and discrete movements.

关键词： Free energy learning (artificial intelligence) Diffusion processes Policies Stochasticity Plethora radon derivation Reward learning

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A reinforcement learning algorithm developed to model GenCo strategic bidding behavior in multidimensional and continuous state and action spaces

A reinforcement learning algorithm developed to model GenCo ...

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4th ieee international symposium on Adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Lau, Alfred Yong Fu Srinivasan, Dipti Reindl, Thomas Natl Univ Singapore Dept Elect Comp Engn 4 Engn Dr 3 Singapore 117576 Singapore Natl Univ Singapore Solar Energy Res Inst Singapore 117574 Singapore

ISBN: (纸本)9781467359252

The electricity market have provided a complex economic environment, and consequently have increased the requirement for advancement of learning methods. In the agent-based modeling and simulation framework of this economic system, the generation company's decision-making is modeled using reinforcement learning. Existing learning methods that models the generation company's strategic bidding behavior are not adapted to the non-stationary and non-Markovian environment involving multidimensional and continuous state and action spaces. This paper proposes a reinforcement learning method to overcome these limitations. The proposed method discovers the input space structure through the self-organizing map, exploits learned experience through Roth-Erev reinforcement learning and the explores through the actor critic map. Simulation results from experiments show that the proposed method outperforms Simulated Annealing Q-learning and Variant Roth-Erev reinforcement learning. The proposed method is a step towards more realistic agent learning in Agent-based Computational Economics.

关键词： reinforcement learning strategic bidding behavior agent-based modeling electricity market

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