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

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Closed-Loop Control of Anesthesia and Mean Arterial Pressure Using reinforcement learning

Closed-Loop Control of Anesthesia and Mean Arterial Pressure...

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

作者： Padmanabhan, Regina Meskin, Nader Haddad, Wassim M. Qatar Univ Dept Elect Engn Doha Qatar Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

ISBN: (纸本)9781479945528

General anesthesia is required for patients undergoing surgery as well as for some patients in the intensive care units with acute respiratory distress syndrome. However, most anesthetics affect cardiac and respiratory functions. Hence, it is important to monitor and control the infusion of anesthetics to meet sedation requirements while keeping patient vital parameters within safe limits. The critical task of anesthesia administration also necessitates that drug dosing be optimal, patient specific, and robust. In this paper, the concept of reinforcement learning (RL) is used to develop a closed-loop anesthesia controller using the bispectral index (BIS) as a control variable while concurrently accounting for mean arterial pressure (MAP). In particular, the proposed framework uses these two parameters to control propofol infusion rates to regulate the BIS and MAP within a desired range. Specifically, a weighted combination of the error of the BIS and MAP signals is considered in the proposed RL algorithm. This reduces the computational complexity of the RL algorithm and consequently the controller processing time.

关键词： closed loop systems computational complexity learning (artificial intelligence) medical computing medical control systems surgery BIS MAP RL acute respiratory distress syndrome anesthesia administration anesthetics bispectral index closed-loop control mean arterial pressure patient surgery reinforcement learning Anesthesia Biomedical monitoring Blood pressure Drugs Indexes learning (artificial intelligence) Optimal control Anesthetics complexity classes learning (artificial intelligence) Mean Arterial Pressure Adult Respiratory Distress Syndrome Anesthesia medical control systems Biomedical monitoring bispectral index medical computing Closed loop systems manufacturing automation protocol MITIGATION ACTION PLANS control ring Optimal control

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Application of reinforcement learning-based algorithms in CO2 allowance and electricity markets

Application of reinforcement learning-based algorithms in CO...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Nanduri, Vishnuteja Department of Industrial and Manufacturing Engineering University of Wisconsin-Milwaukee Milwaukee WI 53211 United States

ISBN: (纸本)9781424498888

Climate change is one of the most important challenges faced by the world this century. In the U.S., the electric power industry is the largest emitter of CO2, contributing to the climate crisis. Federal emissions control bills in the form of cap-and-trade programs are currently idling in the U.S. Congress. In the mean time, ten states in the northeastern U.S. have adopted a regional cap-and-trade program to reduce CO2 levels and also to increase investments in cleaner technologies. Many of the states in which the cap-and-trade programs are active operate under a restructured market paradigm, where generators compete to supply power. This research presents a bi-level game-theoretic model to capture competition between generators in cap-and-trade markets and restructured electricity markets. The solution to the game-theoretic model is obtained using a reinforcement learning based algorithm. © 2011 ieee.

关键词： reinforcement learning

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Multi-Objective reinforcement learning for AUV Thruster Failure Recovery

Multi-Objective Reinforcement Learning for AUV Thruster Fail...

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

作者： Ahmadzadeh, Seyed Reza Kormushev, Petar Caldwell, Darwin G. Ist Italiano Tecnol Dept Adv Robot Via Morego 30 I-16163 Genoa Italy

ISBN: (纸本)9781479945528

This paper investigates learning approaches for discovering fault-tolerant control policies to overcome thruster failures in Autonomous Underwater Vehicles (AUV). The proposed approach is a model-based direct policy search that learns on an on-board simulated model of the vehicle. When a fault is detected and isolated the model of the AUV is reconfigured according to the new condition. To discover a set of optimal solutions a multi-objective reinforcement learning approach is employed which can deal with multiple conflicting objectives. Each optimal solution can be used to generate a trajectory that is able to navigate the AUV towards a specified target while satisfying multiple objectives. The discovered policies are executed on the robot in a closed-loop using AUV's state feedback. Unlike most existing methods which disregard the faulty thruster, our approach can also deal with partially broken thrusters to increase the persistent autonomy of the AUV. In addition, the proposed approach is applicable when the AUV either becomes under-actuated or remains redundant in the presence of a fault. We validate the proposed approach on the model of the Girona500 AUV.

关键词： autonomous underwater vehicles closed loop systems control engineering computing fault diagnosis learning (artificial intelligence) mobile robots optimal control state feedback AUV state feedback AUV thruster failure recovery Girona500 AUV closed-loop conflicting objective fault detection fault-tolerant control policy faulty thruster model-based direct policy search multiobjective reinforcement learning approach on-board simulated model optimal solution Optimization Sociology Statistics Trajectory Vectors Vehicle dynamics Vehicles Autonomous underwater vehicles control engineering computing Closed loop systems State feedback optimal solution trajectory Sociology vehicle Vehicle dynamics Mobile robots Defect detection Fault diagnosis learning (artificial intelligence) Optimal control CLOSED LOOP

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Beyond Exponential Utility Functions: A Variance-Adjusted Approach for Risk-Averse reinforcement learning

Beyond Exponential Utility Functions: A Variance-Adjusted Ap...

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

作者： Gosavi, Abhijit A. Das, Sajal K. Murray, Susan L. Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

ISBN: (纸本)9781479945528

Utility theory has served as a bedrock for modeling risk in economics. Where risk is involved in decision-making, for solving Markov decision processes (MDPs) via utility theory, the exponential utility (EU) function has been used in the literature as an objective function for capturing risk-averse behavior. The EU function framework uses a so-called risk-averseness coefficient (RAC) that seeks to quantify the risk appetite of the decision-maker. Unfortunately, as we show in this paper, the EU framework suffers from computational deficiencies that prevent it from being useful in practice for solution methods based on reinforcement learning (RL). In particular, the value function becomes very large and typically the computer overflows. We provide a simple example to demonstrate this. Further, we show empirically how a variance-adjusted (VA) approach, which approximates the EU function objective for reasonable values of the RAC, can be used in the RL algorithm. The VA framework in a sense has two objectives: maximize expected returns and minimize variance. We conduct empirical studies on a VA-based RL algorithm on the semi-MDP (SMDP), which is a more general version of the MDP. We conclude with a mathematical proof of the boundedness of the iterates in our algorithm.

关键词： Markov processes decision making economics learning (artificial intelligence) mathematical analysis risk analysis utility theory EU function MDP Markov decision process RAC VA approach exponential utility functions mathematical proof risk-averse reinforcement learning risk-averseness coefficient variance-adjusted approach Computers Equations learning (artificial intelligence) Linear programming Mathematical model Measurement Markov chain utility theory formal proof economics AKT1 gene Computers decision making mathematical analysis linear programming Risk Management risk analysis learning (artificial intelligence) Mathematical Model

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Safe reinforcement learning in high-risk tasks through policy improvement

Safe reinforcement learning in high-risk tasks through polic...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Garcia Polo, Francisco Javier Fernandez Rebollo, Fernando Computer Science Department Universidad Carlos III de Madrid Avenida de la Universidad 30 28911 Leganés Madrid Spain

ISBN: (纸本)9781424498888

reinforcement learning (RL) methods are widely used for dynamic control tasks. In many cases, these are high risk tasks where the trial and error process may select actions which execution from unsafe states can be catastrophic. In addition, many of these tasks have continuous state and action spaces, making the learning problem harder and unapproachable with conventional RL algorithms. So, when the agent begins to interact with a risky and large state-action space environment, an important question arises: how can we avoid that the exploration of the state-action space causes damages in the learning (or other) systems. In this paper, we define the concept of risk and address the problem of safe exploration in the context of RL. Our notion of safety is concerned with states that can lead to damage. Moreover, we introduce an algorithm that safely improves suboptimal but robust behaviors for continuous state and action control tasks, and that learns efficiently from the experience gathered from the environment. We report experimental results using the helicopter hovering task from the RL Competition. © 2011 ieee.

关键词： reinforcement learning

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Online reinforcement learning neural network controller design for nanomanipulation

Online reinforcement learning neural network controller desi...

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ieee International symposium on Approximate dynamic programming and reinforcement learning

作者： Yang, Qinmin Jagannathan, S. Univ Missouri Dept Elect & Comp Engn Rolla MO 65401 USA

ISBN: (纸本)9781424407064

In this paper, a novel reinforcement learning neural network (NN)-based controller, referred to adaptive critic controller, is proposed for affine nonlinear discrete-time systems with applications to nanomanipulation. In the online NN reinforcement learning method, one NN is designated as the critic NN, which approximates the long-term cost function by assuming that the states of the nonlinear systems is available for measurement. An action NN is employed to derive an optimal control signal to track a desired system trajectory while minimizing the cost function. Online updating weight tuning schemes for these two NNs are also derived. By using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the tracking error and weight estimates is shown. Nanomanipulation implies manipulating objects with nanometer size. It takes several hours to perform a simple task in the nanoscale world. To accomplish the task automatically the proposed online learning control design is evaluated for the task of nanomanipulation and verified in the simulation environment.

关键词： neural network reinforcement learning on-line learning dynamic programming Lyapunov method nanomanipulation

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Active exploration for robot parameter selection in episodic reinforcement learning

Active exploration for robot parameter selection in episodic...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Kroemer, Oliver Peters, Jan Max Planck Institute 38 Spemannstr. Tuebingen 72012 Germany

ISBN: (纸本)9781424498888

As the complexity of robots and other autonomous systems increases, it becomes more important that these systems can adapt and optimize their settings actively. However, such optimization is rarely trivial. Sampling from the system is often expensive in terms of time and other costs, and excessive sampling should therefore be avoided. The parameter space is also usually continuous and multi-dimensional. Given the inherent exploration-exploitation dilemma of the problem, we propose treating it as an episodic reinforcement learning problem. In this reinforcement learning framework, the policy is defined by the system's parameters and the rewards are given by the system's performance. The rewards accumulate during each episode of a task. In this paper, we present a method for efficiently sampling and optimizing in continuous multidimensional spaces. The approach is based on Gaussian process regression, which can represent continuous non-linear mappings from parameters to system performance. We employ an upper confidence bound policy, which explicitly manages the trade-off between exploration and exploitation. Unlike many other policies for this kind of problem, we do not rely on a discretization of the action space. The presented method was evaluated on a real robot. The robot had to learn grasping parameters in order to adapt its grasping execution to different objects. The proposed method was also tested on a more general gain tuning problem. The results of the experiments show that the presented method can quickly determine suitable parameters and is applicable to real online learning applications. © 2011 ieee.

关键词： reinforcement learning

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reinforcement learning in multidimensional continuous action spaces

Reinforcement learning in multidimensional continuous action...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Pazis, Jason Lagoudakis, Michail G. Department of Computer Science Duke University Durham NC 27708-0129 United States Department of Electronic and Computer Engineering Technical University of Crete Chania Crete 73100 Greece

ISBN: (纸本)9781424498888

The majority of learning algorithms available today focus on approximating the state (V ) or state-action (Q) value function and efficient action selection comes as an afterthought. On the other hand, real-world problems tend to have large action spaces, where evaluating every possible action becomes impractical. This mismatch presents a major obstacle in successfully applying reinforcement learning to real-world problems. In this paper we present an effective approach to learning and acting in domains with multidimensional and/or continuous control variables where efficient action selection is embedded in the learning process. Instead of learning and representing the state or state-action value function of the MDP, we learn a value function over an implied augmented MDP, where states represent collections of actions in the original MDP and transitions represent choices eliminating parts of the action space at each step. Action selection in the original MDP is reduced to a binary search by the agent in the transformed MDP, with computational complexity logarithmic in the number of actions, or equivalently linear in the number of action dimensions. Our method can be combined with any discrete-action reinforcement learning algorithm for learning multidimensional continuous-action policies using a state value approximator in the transformed MDP. Our preliminary results with two well-known reinforcement learning algorithms (Least-Squares Policy Iteration and Fitted Q-Iteration) on two continuous action domains (1-dimensional inverted pendulum regulator, 2-dimensional bicycle balancing) demonstrate the viability and the potential of the proposed approach. © 2011 ieee.

关键词： reinforcement learning

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High-order local dynamic programming

High-order local dynamic programming

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作者： Tassa, Yuval Todorov, Emanuel Interdisciplinary Center for Neural Computation Hebrew University Jerusalem Israel Applied Mathematics and Computer Science and Engineering University of Washington Seattle United States

ISBN: (纸本)9781424498888

We describe a new local dynamic programming algorithm for solving stochastic continuous Optimal Control problems. We use cubature integration to both propagate the state distribution and perform the Bellman backup. The algorithm can approximate the local policy and cost-to-go with arbitrary function bases. We compare the classic quadratic cost-to-go/linear-feedback controller to a cubic cost-to-go/quadratic policy controller on a 10-dimensional simulated swimming robot, and find that the higher order approximation yields a more general policy with a larger basin of attraction. © 2011 ieee.

关键词： dynamic programming

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DHP adaptive critic motion control of autonomous wheeled mobile robot

DHP adaptive critic motion control of autonomous wheeled mob...

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ieee International symposium on Approximate dynamic programming and reinforcement learning

作者： Lin, Wei-Song Yang, Ping-Chieh Natl Taiwan Univ Dept Elect Engn Inst Elect Engn 1 Sec 4Roosevelt Rd Taipei 106 Taiwan

ISBN: (纸本)9781424407064

Autonomous drive of wheeled mobile robot (WMR) needs implementing velocity and path tracking control subject to complex dynamical constraints. Conventionally, this control design is obtained by analysis and synthesis of the WMR system. This paper presents the dual heuristic programming (DHF) adaptive critic design of the motion control system that enables WMR to achieve the control purpose simply by learning through trial. The design consists of an adaptive critic velocity neuro-control loop and a posture neuro-control loop. The neural weights in the velocity neuro-controller (VNC) are corrected with the DHP adaptive critic method. The designer simply expresses the control objective with a utility function. The VNC learns by sequential optimization to satisfy the control objective. The posture neuro-controller (PNC) approximates the inverse velocity model of WMR so as to map planned positions to desired velocities. Supervised drive of WMR in variant velocities supplies training samples for the PNC and VNC to setup the neural weights. In autonomous drive, the learning mechanism keeps improving the PNC and VNC. The design is evaluated on an experimental WMR. The excellent results make it certain that the DHP adaptive critic motion control design enables WMR to develop the control ability autonomously.

关键词： adaptive critic design autonomous robot neuro-control dual heuristic programming reinforcement learning

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