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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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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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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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adaptive Optimal Control for Nonlinear Discrete-Time Systems

Adaptive Optimal Control for Nonlinear Discrete-Time Systems

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Qin, Chunbin Zhang, Huaguang Luo, Yanhong Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9781467359252

This paper proposes an on-line near-optimal control scheme based on capabilities of neural networks (NNs), in function approximation, to attain the on-line solution of optimal control problem for nonlinear discrete-time systems. First, to solve the Hamilton-Jacobi-Bellman (HJB) equation forward-intime appearing in the optimal control problem, two neural networks are used to approximate the cost function and to compute the optimal control policy, respectively. And then, according to the Bellman's optimality principle and the adaptive technology, the on-line weight updating laws for the critic network and action network are derived, respectively. Further, considering NNs approximative errors, the stability analysis of the closed-loop system is demonstrated by Lyapunov theory. At last, a numerical example is provided to demonstrate the effectiveness of the proposed method.

关键词： adaptive optimal control Hamilton-Jacobi-Bellman equation neural network adaptive dynamic programming

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reinforcement learning and adaptive dynamic programming for Feedback Control

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ieee CIRCUITS AND SYSTEMS MAGAZINE 2009年第3期9卷 32-50页

作者： Lewis, Frank L. Vrabie, Draguna Univ Texas Arlington Automat & Robot Res Inst Arlington TX USA S China Univ Technol Guangzhou Guangdong Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

Living organisms learn by acting on their environment, observing the resulting reward stimulus, and adjusting their actions accordingly to improve the reward. This action-based or reinforcement learning can capture notions of optimal behavior occurring in natural systems. We describe mathematical formulations for reinforcement learning and a practical implementation method known as adaptive dynamic programming. These give us insight into the design of controllers for man-made engineered systems that both learn and exhibit optimal behavior.

关键词： learning Programmable control adaptive control dynamic programming Feedback control Organisms Optimal control Control systems Design engineering Systems engineering and theory

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Finite Horizon Stochastic Optimal Control of Uncertain Linear Networked Control System

Finite Horizon Stochastic Optimal Control of Uncertain Linea...

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Xu, Hao Jagannathan, S. Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

ISBN: (纸本)9781467359252

In this paper, finite horizon stochastic optimal control issue has been studied for linear networked control system (LNCS) in the presence of network imperfections such as network-induced delays and packet losses by using adaptive dynamic programming (ADP) approach. Due to an uncertainty in system dynamics resulting from network imperfections, the stochastic optimal control design uses a novel adaptive estimator (AE) to solve the optimal regulation of uncertain LNCS in a forward-in-time manner in contrast with backward-in-time Riccati equation-based optimal control with known system dynamics. Tuning law for unknown parameters of AE has been derived. Lyapunov theory is used to show that all the signals are uniformly ultimately bounded (UUB) with ultimate bounds being a function of initial values and final time. In addition, the estimated control input converges to optimal control input within finite horizon. Simulation results are included to show the effectiveness of the proposed scheme.

关键词： Networked Control System adaptive dynamics programming and reinforcement learning Finite horizon Stochastic Optimal Control adaptive Estimator

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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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reinforcement learning in the Game of Othello: learning Against a Fixed Opponent and learning from Self-Play

Reinforcement Learning in the Game of Othello: Learning Agai...

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (ADPRL)

作者： van der Ree, Michiel Wiering, Marco Univ Groningen Inst Artificial Intelligence & Cognit Engn Fac Math & Nat Sci NL-9700 AB Groningen Netherlands

ISBN: (纸本)9781467359252

This paper compares three strategies in using reinforcement learning algorithms to let an artificial agent learn to play the game of Othello. The three strategies that are compared are: learning by self-play, learning from playing against a fixed opponent, and learning from playing against a fixed opponent while learning from the opponent's moves as well. These issues are considered for the algorithms Q-learning, Sarsa and TD-learning. These three reinforcement learning algorithms are combined with multi-layer perceptrons and trained and tested against three fixed opponents. It is found that the best strategy of learning differs per algorithm. Q-learning and Sarsa perform best when trained against the fixed opponent they are also tested against, whereas TD-learning performs best when trained through self-play. Surprisingly, Q-learning and Sarsa outperform TD-learning against the stronger fixed opponents, when all methods use their best strategy. learning from the opponent's moves as well leads to worse results compared to learning only from the learning agent's own moves.

关键词： learning Game theory Game animals learning (artificial intelligence) Fixed Hunting

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An Integrated Design for Intensified Direct Heuristic dynamic programming

An Integrated Design for Intensified Direct Heuristic Dynami...

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Luo, Xiong Si, Jennie Zhou, Yuchao Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Arizona State Univ Dept Elect Engn Tempe AZ 85287 USA

ISBN: (纸本)9781467359252

There has been a growing interest in the study of adaptive/approximate dynamic programming (ADP) in recent years. The ADP technique provides a powerful tool to understand and improve the principled technologies of machine intelligence system. As one of the ADP algorithms based on adaptive critic neural networks (NNs), the direct heuristic dynamic programming (direct HDP) has demonstrated some successful applications in solving realistic engineering control problems. In this study, based on a three-network architecture in which the reinforcement signal is approximated by an additional NN, a novel integrated design method for intensified direct HDP is developed. The new design approach is implemented by using multiple PID neural networks (PIDNNs), which effectively takes into account structural knowledge of system states and control that are usually present in a physical system. By using a Lyapunov stability approach, a uniformly ultimately boundedness (UUB) result is proved for our PIDNNs-based intensified direct HDP learning controller. Furthermore, the learning and control performances of the proposed design is tested using the popular cart-pole example to illustrate the key ideas of this paper.

关键词： Direct heuristic dynamic programming neural network PID neural network stability

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