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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101-110 订阅

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Finite-Horizon Optimal Control Design for Uncertain Linear Discrete-time Systems

Finite-Horizon Optimal Control Design for Uncertain Linear D...

引用

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 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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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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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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Bias-Corrected Q-learning to Control Max-Operator Bias in Q-learning

Bias-Corrected Q-Learning to Control Max-Operator Bias in Q-...

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

作者： Lee, Donghun Defourny, Boris Powell, Warren B. Princeton Univ Dept Comp Sci Princeton NJ 08540 USA Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08540 USA

ISBN: (纸本)9781467359252

We identify a class of stochastic control problems with highly random rewards and high discount factor which induce high levels of statistical error in the estimated action-value function. This produces significant levels of max-operator bias in Q-learning, which can induce the algorithm to diverge for millions of iterations. We present a bias-corrected Q-learning algorithm with asymptotically unbiased resistance against the max-operator bias, and show that the algorithm asymptotically converges to the optimal policy, as Q-learning does. We show experimentally that bias-corrected Q-learning performs well in a domain with highly random rewards where Q-learning and other related algorithms suffer from the max-operator bias.

关键词： Bias Bias Bias Statistical Biases bias(electronics) statistical error Reward optimal strategy iterative methods algorithms asymptotic

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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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Scalarized Multi-Objective reinforcement learning: Novel Design Techniques

Scalarized Multi-Objective Reinforcement Learning: Novel Des...

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

作者： Van Moffaert, Kristof Drugan, Madalina M. Nowe, Ann Vrije Univ Brussel Dept Comp Sci B-1050 Brussels Belgium

ISBN: (纸本)9781467359252

In multi-objective problems, it is key to find compromising solutions that balance different objectives. The linear scalarization function is often utilized to translate the multi-objective nature of a problem into a standard, single-objective problem. Generally, it is noted that such as linear combination can only find solutions in convex areas of the Pareto front, therefore making the method inapplicable in situations where the shape of the front is not known beforehand, as is often the case. We propose a non-linear scalarization function, called the Chebyshev scalarization function, as a basis for action selection strategies in multi-objective reinforcement learning. The Chebyshev scalarization method overcomes the flaws of the linear scalarization function as it can (i) discover Pareto optimal solutions regardless of the shape of the front, i. e. convex as well as non-convex, (ii) obtain a better spread amongst the set of Pareto optimal solutions and (iii) is not particularly dependent on the actual weights used.

关键词： design techniques learning (artificial intelligence) optimal solution Convexity flaws learning Linear Combination Solutions

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A Study on the Efficiency of learning a Robot Controller in Various Environments

A Study on the Efficiency of Learning a Robot Controller in ...

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

作者： Soga, Sachiko Kobayashi, Ichiro Ochanomizu Univ Grad Sch Humanities & Sci Bunkyo Ku Tokyo 1128610 Japan

ISBN: (纸本)9781467359252

In the case that a robot controller is trained by means of evolutionary computation, the robot will be able to behave sufficiently in the environment where the robot has been trained. However, if the robot is put in an environment which is more complex than a training environment, it cannot behave sufficiently and is required to be trained again so as it fits to the complex environment. Based on this fact, we build a training environment for a robot controller with the partial components of a more complex environment than the training environment and aim to obtain a controller which makes a robot be able to act in the complex environment by only training the controller at a simpler environment. We clarify a way of building a training environment which functions effectively for training a robot controller and discuss how much training is necessary in the training environment for a robot to be able to behave under a more complex environment.

关键词： robot controller Environments Robot Functional training Controllers

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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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A Combined Hierarchical reinforcement learning Based Approach For Multi-robot Cooperative Target Searching in Complex Unknown Environments

A Combined Hierarchical Reinforcement Learning Based Approac...

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

作者： Cai, Yifan Yang, Simon X. Xu, Xin Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada Natl Univ Def Technol Coll Mechatron & Automat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781467359252

Effective cooperation of multi-robots in unknown environments is essential in many robotic applications, such as environment exploration and target searching. In this paper, a combined hierarchical reinforcement learning approach, together with a designed cooperation strategy, is proposed for the real-time cooperation of multi-robots in completely unknown environments. Unlike other algorithms that need an explicit environment model or select parameters by trial and error, the proposed cooperation method obtains all the required parameters automatically through learning. By integrating segmental options with the traditional MAXQ algorithm, the cooperation hierarchy is built. In new tasks, the designed cooperation method can control the multi-robot system to complete the task effectively. The simulation results demonstrate that the proposed scheme is able to effectively and efficiently lead a team of robots to cooperatively accomplish target searching tasks in completely unknown environments.

关键词： Hierarchical reinforcement learning multi-robot cooperation complex unknown environment target searching

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Optimistic Planning for Continuous-Action Deterministic Systems

Optimistic Planning for Continuous-Action Deterministic Syst...

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

作者： Busoniu, Lucian Daniels, Alexander Munos, Remi Babuska, Robert Univ Lorraine CRAN UMR 7039 Nancy France CNRS CRAN UMR 7039 Nancy France Delft Univ Technol DCSC Delft Netherlands INRIA Lille Nord Europe Team SequeL Lille France

ISBN: (纸本)9781467359252

We consider the class of online planning algorithms for optimal control, which compared to dynamic programming are relatively unaffected by large state dimensionality. We introduce a novel planning algorithm called SOOP that works for deterministic systems with continuous states and actions. SOOP is the first method to explore the true solution space, consisting of infinite sequences of continuous actions, without requiring knowledge about the smoothness of the system. SOOP can be used parameter-free at the cost of more model calls, but we also propose a more practical variant tuned by a parameter a, which balances finer discretization with longer planning horizons. Experiments on three problems show SOOP reliably ranks among the best algorithms, fully dominating competing methods when the problem requires both long horizons and fine discretization.

关键词： Solution space dynamic programming Smoothness deterministic Optimal control continuous action Discretization Horizon Planning

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