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检索条件"任意字段=2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL 2009"

共 232 条记录，以下是81-90 订阅

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adaptive optimal control for nonlinear discrete-time systems

Adaptive optimal control for nonlinear discrete-time systems

引用

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

作者： Chunbin Qin Huaguang Zhang Yanhong Luo School of Information Science and Engineering Northeastern University Shenyang China Basic Experiment Teaching Center Henan University Kaifeng China

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-in-time 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： Artificial neural networks Equations Optimal control Mathematical model dynamic programming Approximation methods Discrete-time systems

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Real-time tracking on adaptive critic design with uniformly ultimately bounded condition

Real-time tracking on adaptive critic design with uniformly ...

引用

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

作者： Zhen Ni Xiao Fang Haibo He Dongbin Zhao Xin Xu Department of Electrical University of Rhode Island Kingston RI USA Institute of Automation Chinese Academy of Sciences Beijing China Institute of Automation National University of Defense Technology Changsha China

In this paper, we proposed a new nonlinear tracking controller based on heuristic dynamic programming (HDP) with the tracking filter. Specifically, we integrate a goal network into the regular HDP design and provide the critic network with detailed internal reward signal to help the value function approximation. The architecture is explicitly explained with the tracking filter, goal network, critic network and action network, respectively. We provide the stability analysis of our proposed controller with Lyapunov approach. It is shown that the filtered tracking errors and the weights estimation errors in neural networks are all uniformly ultimately bounded (UUB) under certain conditions. Finally, we compare our proposed approach with regular HDP approach in virtual reality (VR)/Simulink environment to justify the improved control performance.

关键词： dynamic programming Stability analysis Function approximation Nonlinear systems Vectors Computer architecture Neural networks

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Scalarized multi-objective reinforcement learning: Novel design techniques

Scalarized multi-objective reinforcement learning: Novel des...

引用

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

作者： Kristof Van Moffaert Madalina M. Drugan Ann Nowé Department of Computer Science Vrije Universiteit Brussel Brussels Belgium

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： Pareto optimization learning (artificial intelligence) Chebyshev approximation Benchmark testing Measurement Shape

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Optimistic planning for continuous-action deterministic systems

Optimistic planning for continuous-action deterministic syst...

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

作者： Lucian Buşoniu Alexander Daniels Rémi Munos Robert Babuška Department of Automation Technical University of Cluj-Napoca Romania France DCSC Delft University of Technology the Netherlands Team SequeL INRIA Lille-Nord Europe France

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 α, 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： Planning Upper bound Optimization dynamic programming Measurement Heuristic algorithms Aerospace electronics

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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...

引用

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

作者： Yifan Cai Simon X. Yang Xin Xu The School of Engineering University of Guelph Guelph Ontario Canada The College of Mechatronics and Automation National University of Defense Technology Changsha Hunan Province China

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： Robot kinematics learning (artificial intelligence) Real-time systems Algorithm design and analysis dynamic programming Robot sensing systems

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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...

引用

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

作者： Michiel van der Ree Marco Wiering Faculty of Mathematics and Natural Sciences University of Groningen Institute of Artificial Intelligence and Cognitive Engineering The Netherlands

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： Games Training learning (artificial intelligence) Artificial neural networks Heuristic algorithms Testing

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Delayed insertion and rule effect moderation of domain knowledge for reinforcement learning

Delayed insertion and rule effect moderation of domain knowl...

引用

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

作者： Teck-Hou Teng Ah-Hwee Tan School of Computer Engineering Center for Computational Intelligence School of Computer Engineering Nanyang Technological University

Though not a fundamental pre-requisite to efficient machine learning, insertion of domain knowledge into adaptive virtual agent is nonetheless known to improve learning efficiency and reduce model complexity. Conventionally, domain knowledge is inserted prior to learning. Despite being effective, such approach may not always be feasible. Firstly, the effect of domain knowledge is assumed and can be inaccurate. Also, domain knowledge may not be available prior to learning. In addition, the insertion of domain knowledge can frame learning and hamper the discovery of more effective knowledge. Therefore, this work advances the use of domain knowledge by proposing to delay the insertion and moderate the effect of domain knowledge to reduce the framing effect while still benefiting from the use of domain knowledge. Using a non-trivial pursuit-evasion problem domain, experiments are first conducted to illustrate the impact of domain knowledge with different degrees of truth. The next set of experiments illustrates how delayed insertion of such domain knowledge can impact learning. The final set of experiments is conducted to illustrate how delaying the insertion and moderating the assumed effect of domain knowledge can ensure the robustness and versatility of reinforcement learning.

关键词： Vectors Knowledge engineering learning (artificial intelligence) Adaptation models Educational institutions Computational modeling Neural networks

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

作者： Alfred Yong Fu Lau Dipti Srinivasan Thomas Reindl National University of Singapore Singapore SG Department of Electrical Computer Engineering National University of Singapore Singapore Solar Energy Research Institute of Singapore National University of Singapore Singapore

The electricity market has provided a complex economic environment, and consequently has 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 model 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 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： learning (artificial intelligence) Electricity supply industry Adaptation models Vectors Computational modeling Simulated annealing Schedules

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reinforcement learning to train Ms. Pac-Man using higher-order action-relative inputs

Reinforcement learning to train Ms. Pac-Man using higher-ord...

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

作者： Luuk Bom Ruud Henken Marco Wiering Faculty of Mathematics and Natural Sciences University of Groningen The Netherlands

reinforcement learning algorithms enable an agent to optimize its behavior from interacting with a specific environment. Although some very successful applications of reinforcement learning algorithms have been developed, it is still an open research question how to scale up to large dynamic environments. In this paper we will study the use of reinforcement learning on the popular arcade video game Ms. Pac-Man. In order to let Ms. Pac-Man quickly learn, we designed particular smart feature extraction algorithms that produce higher-order inputs from the game-state. These inputs are then given to a neural network that is trained using Q-learning. We constructed higher-order features which are relative to the action of Ms. Pac-Man. These relative inputs are then given to a single neural network which sequentially propagates the action-relative inputs to obtain the different Q-values of different actions. The experimental results show that this approach allows the use of only 7 input units in the neural network, while still quickly obtaining very good playing behavior. Furthermore, the experiments show that our approach enables Ms. Pac-Man to successfully transfer its learned policy to a different maze on which it was not trained before.

关键词： Games learning (artificial intelligence) Biological neural networks Neurons Heuristic algorithms Training

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Analyzing collective behavior in evolutionary swarm robotic systems based on an ethological approach

Analyzing collective behavior in evolutionary swarm robotic ...

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

作者： Toshiyuki Yasuda Nanami Wada Kazuhiro Ohkura Yoshiyuki Matsumura Graduate School of Engineering Hiroshima University Higashi-Hiroshima JAPAN Faculty of Textile Science and Technology Shinshu University Ueda Nagano JAPAN

Swarm robotic systems are a type of multi-robot systems which generally consist of many homogeneous autonomous robots without any type of global controllers. Swarm robotics aims at designing desired collective behaviors through many interactions with other robots or their environment. Since a robotic swarm is controlled by an emergent way such as a result of self-organization by using robot learning or artificial evolution, no method has been known to grasp the macroscopic collective behavior in a practical sense, according to the best of our knowledge. In this paper, we propose a novel method for analyzing the collective behavior by introducing the concept of behavioral sequence, which stems from ethology. Analysis about behavioral sequence reveals the transition of robot's action from the viewpoint of specialization and helps us to understand the role of subgroups in a robotic swarm. Applying this method, we observe collective behavior in a foraging task of autonomous mobile robots.

关键词： Robot kinematics Robot sensing systems Mobile robots Vectors Resource management dynamic programming

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