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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71-80 订阅

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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-...

引用

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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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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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 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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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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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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The Second Order Temporal Difference Error for Sarsa(λ)

The Second Order Temporal Difference Error for Sarsa(λ)

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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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Using approximate dynamic programming for Estimating the Revenues of a Hydrogen-based High-Capacity Storage Device

Using Approximate Dynamic Programming for Estimating the Rev...

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

作者： Francois-Lavet, Vincent Fonteneau, Raphael Ernst, Damien Univ Liege Dept Elect Engn & Comp Sci B-4000 Liege Belgium

ISBN: (纸本)9781479945528

This paper proposes a methodology to estimate the maximum revenue that can be generated by a company that operates a high-capacity storage device to buy or sell electricity on the day-ahead electricity market. The methodology exploits the dynamic programming (DP) principle and is specified for hydrogen-based storage devices that use electrolysis to produce hydrogen and fuel cells to generate electricity from hydrogen. Experimental results are generated using historical data of energy prices on the Belgian market. They show how the storage capacity and other parameters of the storage device influence the optimal revenue. The main conclusion drawn from the experiments is that it may be advisable to invest in large storage tanks to exploit the inter-seasonal price fluctuations of electricity.

关键词： dynamic programming electrolysis fuel cells hydrogen storage power markets Belgian market day-ahead electricity market dynamic programming principle high-capacity storage device hydrogen-based storage devices interseasonal price fluctuations maximum revenue estimation optimal revenue dynamic programming Electricity Electrochemical processes Fuel cells Hydrogen Hydrogen storage

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Bayesian active learning with basis functions

Bayesian active learning with basis functions

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ieee symposium on Adaptive dynamic programming and reinforcement learning

作者： Ryzhov, Ilya O. Powell, Warren B. Operations Research and Financial Engineering Princeton University Princeton NJ 08544 United States

ISBN: (纸本)9781424498888

A common technique for dealing with the curse of dimensionality in approximate dynamic programming is to use a parametric value function approximation, where the value of being in a state is assumed to be a linear combination of basis functions. Even with this simplification, we face the exploration/exploitation dilemma: an inaccurate approximation may lead to poor decisions, making it necessary to sometimes explore actions that appear to be suboptimal. We propose a Bayesian strategy for active learning with basis functions, based on the knowledge gradient concept from the optimal learning literature. The new method performs well in numerical experiments conducted on an energy storage problem. © 2011 ieee.

关键词： dynamic programming

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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

作者： Stingu, Emanuel Lewis, Frank L. Automation and Robotics Research Institute University of Texas at Arlington Arlington TX United States

ISBN: (纸本)9781424498888

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. © 2011 ieee.

关键词： reinforcement learning

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