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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81-90 订阅

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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 ...

引用

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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The Second Order Temporal Difference Error for Sarsa(λ)

The Second Order Temporal Difference Error for Sarsa(λ)

引用

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

作者： Fu, Qiming Liu, Quan Xiao, Fei Chen, Guixin Soochow Univ Dept Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9781467359252

Traditional reinforcement learning algorithms, such as Q-learning, Q(lambda), Sarsa, and Sarsa(lambda), update the action value function using temporal difference (TD) error, which is computed by the last action value function. From the perspective of the TD error, and with respect to the problems of low efficiency and slow convergence of the traditional Sarsa(lambda) algorithm, this paper defines the nth order TD Error, applies it in the traditional Sarsa(lambda) algorithm, and develops a fast Sarsa(lambda) algorithm based on the 2nd order TD Error. The algorithm adjusts the Q value with the second-order TD Error and broadcasts the TD Error into the whole state-action space, which speeds up the convergence of the algorithm. This paper also analyzes the convergence rate, and under the condition of one-step update, the results show that the number of iteration depends primarily on gamma, epsilon. Finally, using the proposed algorithm on the traditional reinforcement learning problems, the results show that the algorithm has both a faster convergence rate and better convergence performance.

关键词： reinforcement learning Markov Decision Process Second Order TD Error Eligibility Trace Sarsa(lambda) Algorithm

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

作者： Bom, Luuk Henken, Ruud Wiering, Marco Univ Groningen Inst Artificial Intelligence & Cognit Engn Fac Math & Nat Sci NL-9700 AB Groningen Netherlands

ISBN: (纸本)9781467359252

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： learning (artificial intelligence) higher-order learning Neural network MILLISECOND

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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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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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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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Delayed Insertion and Rule Effect Moderation of Domain Knowledge for reinforcement learning

Delayed Insertion and Rule Effect Moderation of Domain Knowl...

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

作者： Teng, Teck-Hou Tan, Ah-Hwee Nanyang Technol Univ Sch Comp Engn Ctr Computat Intelligence Singapore Singapore Nanyang Technol Univ Sch Comp Engn Singapore Singapore

ISBN: (纸本)9781467359252

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： domain knowledge learning (artificial intelligence) final sets learning Insertion Mutation

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A novel approach for constructing basis functions in approximate dynamic programming for feedback control

A novel approach for constructing basis functions in approxi...

引用

2013 4th ieee symposium on adaptive dynamic programming and reinforcement learning, adprl 2013

作者： Wang, Jian Huang, Zhenhua Xu, Xin College of Mechatronics and Automation National University of Defense Tech Changsha 410073 China Xi'An Air Force Military Representative Office Xi'an China

ISBN: (纸本)9781467359252

This paper presents a novel approach for constructing basis functions in approximate dynamic programming (ADP) through the locally linear embedding (LLE) process. It considers the experience (sample) data as a high-dimensional space and the basis functions to be solved as a low-dimensional space. Through mapping the high-dimensional data into a single global coordinate system of lower dimensionality, the solved basis functions in low-dimensional space have the property that nearby experience data in the high dimensional space remain nearby and similarly co-located with respect to one in the low dimensional space. Thus, the obtained basis functions can precisely approximate the real value/action-value function. The simulation results show that the basis functions obtained by LLE can represent the final policy with a higher precision. © 2013 ieee.

关键词： dynamic programming

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adaptive dynamic programming for terminally constrained finite-horizon optimal control problems

Adaptive dynamic programming for terminally constrained fini...

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ieee Annual Conference on Decision and Control

作者： L. Andrews J. R. Klotz R. Kamalapurkar W. E. Dixon Department of Mechanical and Aerospace Engineering University of Florida Gainesville FL USA

ISBN: (纸本)9781467360890

adaptive dynamic programming is applied to control-affine nonlinear systems with uncertain drift dynamics to obtain a near-optimal solution to a finite-horizon optimal control problem with hard terminal constraints. A reinforcement learning-based actor-critic framework is used to approximately solve the Hamilton-Jacobi-Bellman equation, wherein critic and actor neural networks (NN) are used for approximate learning of the optimal value function and control policy, while enforcing the optimality condition resulting from the hard terminal constraint. Concurrent learning-based update laws relax the restrictive persistence of excitation requirement. A Lyapunov-based stability analysis guarantees uniformly ultimately bounded convergence of the enacted control policy to the optimal control policy.

关键词： control strategy dynamic programming Optimal control critic Best value Self tuning

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