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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141-150 订阅

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Supervised adaptive dynamic programming based adaptive cruise control

Supervised adaptive dynamic programming based adaptive cruis...

引用

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

作者： Dongbin Zhao Zhaohui Hu Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China

This paper proposes a supervised adaptive dynamic programming (SADP) algorithm for the full range adaptive cruise control (ACC) system. The full range ACC system considers both the ACC situation in highway system and the stop and go (SG) situation in urban street way system. It can autonomously drive the host vehicle with desired speed and distance to the preceding vehicle in both situations. A traditional adaptive dynamic programming (ADP) algorithm is suited for this problem, but it suffers from the low learning efficiency. We propose the concept of inducing range to construct the supervisor and finally formulate the SADP algorithm, which greatly speeds up the learning efficiency. Several driving scenarios are designed and tested with the trained controller compared to traditional ones by simulation results, showing that trained SADP performs very well in all the scenarios, so that it provides an effective approach for the full range ACC problem.

关键词： Vehicles Acceleration Driver circuits Training Control systems Artificial neural networks Real time systems

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A reinforcement learning approach for sequential mastery testing

A reinforcement learning approach for sequential mastery tes...

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

作者： El-Sayed M. El-Alfy College of Computer Sciences and Engineering King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

This paper explores a novel application for reinforcement learning (RL) techniques to sequential mastery testing. In such systems, the goal is to classify each examined person, using the minimal number of test items, as master or non-master. Using RL, an intelligent agent autonomously learns from interactions to administer more informative and effective variable-length tests. Empirical results are also provided to evaluate the performance of the proposed approach as compared to two common approaches for variable-length testing (Bayesian decision and sequential probability ratio test) as well as to the fixed-length testing.

关键词： Testing Bayesian methods learning Accuracy Decision theory Measurement Adaptation models

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adaptive dynamic programming with balanced weights seeking strategy

Adaptive dynamic programming with balanced weights seeking s...

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

作者： Jian Fu Haibo He Zhen Ni School of Automation Wuhan University of Technology Wuhan Hubei China Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

In this paper we propose to integrate the recursive Levenberg-Marquardt method into the adaptive dynamic programming (ADP) design for improved learning and adaptive control performance. Our key motivation is to consider a balanced weight updating strategy with the consideration of both robustness and convergence during the online learning process. Specifically, a modified recursive Levenberg-Marquardt (LM) method is integrated into both the action network and critic network of the ADP design, and a detailed learning algorithm is proposed to implement this approach. We test the performance of our approach based on the triple link inverted pendulum, a popular benchmark in the community, to demonstrate online learning and control strategy. Experimental results and comparative study under different noise conditions demonstrate the effectiveness of this approach.

关键词： Artificial neural networks Equations Jacobian matrices Convergence Algorithm design and analysis Damping Robustness

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An adaptive-learning framework for semi-cooperative multi-agent coordination

An adaptive-learning framework for semi-cooperative multi-ag...

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

作者： Abdeslem Boukhtouta Jean Berger Warren B. Powell Abraham George Defence Research and Development Canada QUE Canada Department of Operations Research and Financial Engineering Princeton University Princeton NJ USA

Complex problems involving multiple agents exhibit varying degrees of cooperation. The levels of cooperation might reflect both differences in information as well as differences in goals. In this research, we develop a general mathematical model for distributed, semi-cooperative planning and suggest a solution strategy which involves decomposing the system into subproblems, each of which is specified at a certain period in time and controlled by an agent. The agents communicate marginal values of resources to each other, possibly with distortion. We design experiments to demonstrate the benefits of communication between the agents and show that, with communication, the solution quality approaches that of the ideal situation where the entire problem is controlled by a single agent.

关键词： Mathematical model Multiagent systems Function approximation Equations dynamic programming Approximation algorithms

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An approximate dynamic programming based controller for an underactuated 6DoF quadrotor

An approximate Dynamic Programming based controller for an u...

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

作者： Emanuel Stingu Frank L. Lewis Automation & Robotics Research Institute University of Texas Arlington Arlington TX USA

This paper discusses how the principles of adaptive dynamic programming (ADP) can be applied to the control of a quadrotor helicopter platform flying in an uncontrolled environment and subjected to various disturbances and model uncertainties. ADP is based on reinforcement learning using an actor-critic structure. Due to the complexity of the quadrotor system, the learning process has to use as much information as possible about the system and the environment. Various methods to improve the learning speed and efficiency are presented. Neural networks with local activation functions are used as function approximators because the state-space can not be explored efficiently due to its size and the limited time available. The complex dynamics is controlled by a single critic and by multiple actors thus avoiding the curse of dimensionality. After a number of iterations, the overall actor-critic structure stores information (knowledge) about the system dynamics and the optimal controller that can accomplish the explicit or implicit goal specified in the cost function.

关键词： Equations Optimal control Rotors Heuristic algorithms Neurons Artificial neural networks Approximation methods

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Optimistic planning for belief-augmented Markov Decision Processes

Optimistic planning for belief-augmented Markov Decision Pro...

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

作者： Raphael Fonteneau Lucian Buşoniu Rémi Munos Department of Electrical Engineering and Computer Science University of Liège BELGIUM Universite de Lorraine CRAN FRANCE SequeL Team Inria Lille FRANCE

This paper presents the Bayesian Optimistic Planning (BOP) algorithm, a novel model-based Bayesian reinforcement learning approach. BOP extends the planning approach of the Optimistic Planning for Markov Decision Processes (OP-MDP) algorithm [10], [9] to contexts where the transition model of the MDP is initially unknown and progressively learned through interactions within the environment. The knowledge about the unknown MDP is represented with a probability distribution over all possible transition models using Dirichlet distributions, and the BOP algorithm plans in the belief-augmented state space constructed by concatenating the original state vector with the current posterior distribution over transition models. We show that BOP becomes Bayesian optimal when the budget parameter increases to infinity. Preliminary empirical validations show promising performance.

关键词： Bayes methods Planning Context learning (artificial intelligence) Algorithm design and analysis Context modeling dynamic programming

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

作者： Xiong Luo Jennie Si Yuchao Zhou School of Computer and Communication Engineering University of Science and Technology Beijing (USTB) Beijing China Arizona State University Tempe AZ US

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： Neural networks dynamic programming Convergence Lyapunov methods learning (artificial intelligence) Educational institutions Algorithm design and analysis

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adaptive dynamic programming for terminally constrained finite-horizon optimal control problems 53

Adaptive dynamic programming for terminally constrained fini...

引用

53rd ieee Annual Conference on Decision and Control (CDC)

作者： Andrews, L. Klotz, J. R. Kamalapurkar, R. Dixon, W. E. Univ Florida Dept Mech & Aerosp Engn Gainesville FL USA

ISBN: (纸本)9781467360906

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： dynamic programming

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Protecting against evaluation overfitting in empirical reinforcement learning

Protecting against evaluation overfitting in empirical reinf...

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

作者： Shimon Whiteson Brian Tanner Matthew E. Taylor Peter Stone Informatics Institute University of Amsterdam Netherlands Department of Computing Science University of Alberta Canada Department of Computer Science Lafayette College USA Department of Computer Science University of Texas Austin USA

Empirical evaluations play an important role in machine learning. However, the usefulness of any evaluation depends on the empirical methodology employed. Designing good empirical methodologies is difficult in part because agents can overfit test evaluations and thereby obtain misleadingly high scores. We argue that reinforcement learning is particularly vulnerable to environment overfitting and propose as a remedy generalized methodologies, in which evaluations are based on multiple environments sampled from a distribution. In addition, we consider how to summarize performance when scores from different environments may not have commensurate values. Finally, we present proof-of-concept results demonstrating how these methodologies can validate an intuitively useful range-adaptive tile coding method.

关键词： Uncertainty Algorithm design and analysis Tiles learning Tuning Machine learning Supervised learning

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Higher-level application of adaptive dynamic programming/reinforcement learning - a next phase for controls and system identification?

Higher-level application of Adaptive Dynamic Programming/Rei...

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

作者： George G. Lendaris Systems Science Graduate Program Portland State University Portland OR USA

In previous work it was shown that adaptive-Critic-type Approximate dynamic programming could be applied in a “higher-level” way to create autonomous agents capable of using experience to discern context and select optimal, context-dependent control policies. Early experiments with this approach were based on full a priori knowledge of the system being monitored. The experiments reported in this paper, using small neural networks representing families of mappings, were designed to explore what happens when knowledge of the system is less precise. Results of these experiments show that agents trained with this approach perform well when subject to even large amounts of noise or when employing (slightly) imperfect models. The results also suggest that aspects of this method of context discernment are consistent with our intuition about human learning. The insights gained from these explorations can be used to guide further efforts for developing this approach into a general methodology for solving arbitrary identification and control problems.

关键词： Context Artificial neural networks Context modeling Process control System identification Training Humans

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