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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181-190 订阅

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The Knowledge Gradient Policy for Offline learning with Independent Normal Rewards

The Knowledge Gradient Policy for Offline Learning with Inde...

引用

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

作者： Peter Frazier Warren Powell Department of Operations Research and Financial Engineering Princeton University Engineering Princeton NJ USA

We define a new type of policy, the knowledge gradient policy, in the context of an offline learning problem. We show how to compute the knowledge gradient policy efficiently and demonstrate through Monte Carlo simula... 详细信息

关键词： Mirrors Knowledge engineering Bandwidth Time measurement Response surface methodology Operations research Bayesian methods Performance evaluation dynamic programming learning

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Toward effective combination of off-line and on-line training in ADP framework

Toward effective combination of off-line and on-line trainin...

引用

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

作者： Danil Prokhorov Toyota Technical Center Ann Arbor MI USA

We are interested in finding the most effective combination between off-line and on-line/real-time training in approximate dynamic programming. We introduce our approach of combining proven off-line methods of training for robustness with a group of on-line methods. Training for robustness is carried out on reasonably accurate models with the multi-stream Kalman filter method (Feldkamp et al., 1998), whereas on-line adaptation is performed either with the help of a critic or by methods resembling reinforcement learning. We also illustrate importance of using recurrent neural networks for both controller/actor and critic

关键词： Neurocontrollers Robustness Recurrent neural networks Neural networks adaptive control dynamic programming Robust control Programmable control learning Uncertainty

来源：评论

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Coupling perception and action using minimax optimal control

Coupling perception and action using minimax optimal control

引用

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

作者： Tom Erez William D. Smart Washington University Saint Louis MO USA

This paper proposes a novel approach for coupling perception and action through minimax dynamic programming. We tackle domains where the agent has some control over the observation process (e.g. via the manipulation of some sensors), and show how to transform the system so that an optimal control solution can be sought with standard algorithms. We demonstrate our method in a toy domain, where an agent guides two point masses (ldquohandsrdquo) to a target in a 2D scene with obstacles. The agent can direct the gaze of a virtual ldquoeyerdquo to different parts of the scene, thereby reducing the observation noise for elements of the scene in that vicinity and improving the quality of feedback control. In this manner, motor control of the eye allots attentional resources. We propose a unified framework that treats both perception and action as interdependent components of the same optimal control task. The implications of uncertainty on task performance are uncovered by deploying an adversary whose strength to do harm is proportional to the instantaneous level of state uncertainty. We transform the partially-observable system to a fully-observable by coupling the state dynamics with a state-estimation filter, and so augment the state space to include an explicit representation of the instantaneous state uncertainty. The augmented system is high-dimensional, but through minimax differential dynamic programming, a local method that is less susceptible to the curse of dimensionality, we are able to solve for the optimal control of the hands and the eye at the same time, allowing for the emergence of interesting phenomena such as hand-eye coordination, saccades and smooth pursuit.

关键词： Minimax techniques Optimal control Layout Uncertainty dynamic programming Control systems Sensor systems Noise reduction Feedback control Motor drives

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Using ADP to Understand and Replicate Brain Intelligence: the Next Level Design

Using ADP to Understand and Replicate Brain Intelligence: th...

引用

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

作者： Paul J. Werbos National Science Foundation Arlington VA USA

Since the 1960's the author proposed that we could understand and replicate the highest level of intelligence seen in the brain, by building ever more capable and general systems for adaptive dynamic programming (ADP) - like "reinforcement learning" but based on approximating the Bellman equation and allowing the controller to know its utility function. Growing empirical evidence on the brain supports this approach. adaptive critic systems now meet tough engineering challenges and provide a kind of first-generation model of the brain. Lewis, Prokhorov and myself have early second-generation work. Mammal brains possess three core capabilities - creativity/imagination and ways to manage spatial and temporal complexity - even beyond the second generation. This paper reviews previous progress, and describes new tools and approaches to overcome the spatial complexity gap.

关键词： adaptive systems Intelligent structures Buildings Programmable control adaptive control dynamic programming learning Equations Control systems Brain modeling

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reinforcement learning in multidimensional continuous action spaces

Reinforcement learning in multidimensional continuous action...

引用

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

作者： Jason Pazis Michail G. Lagoudakis Department of Computer Science Duke University Durham NC USA Department of Electronic and Computer Engineering Technical University of Crete Crete Greece

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.

关键词： Aerospace electronics Complexity theory Binary trees learning Vegetation Markov processes Approximation algorithms

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Convergence of Model-Based Temporal Difference learning for Control

Convergence of Model-Based Temporal Difference Learning for ...

引用

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

作者： Hado van Hasselt Marco A. Wiering Department of Information and Computing Sciences University of Utrecht Utrecht Netherlands

A theoretical analysis of model-based temporal difference learning for control is given, leading to a proof of convergence. This work differs from earlier work on the convergence of temporal difference learning by proving convergence to the optimal value function. This means that not the values of the current policy are found, but instead the policy is updated in such a manner that ultimately the optimal policy is guaranteed to be reached

关键词： Convergence learning dynamic programming Intelligent systems Telephony Stochastic processes

来源：评论

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Randomly Sampling Actions In dynamic programming

Randomly Sampling Actions In Dynamic Programming

引用

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

作者： Christopher G. Atkeson Robotics Institute Carnegie Mellon University Pittsburgh PA USA

We describe an approach towards reducing the curse of dimensionality for deterministic dynamic programming with continuous actions by randomly sampling actions while computing a steady state value function and policy. This approach results in globally optimized actions, without searching over a discretized multidimensional grid. We present results on finding time invariant control laws for two, four, and six dimensional deterministic swing up problems with up to 480 million discretized states

关键词： Sampling methods dynamic programming Cost function Computational efficiency Interpolation Steady-state learning Robots USA Councils Multidimensional systems

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On a Successful Application of Multi-Agent reinforcement learning to Operations Research Benchmarks

On a Successful Application of Multi-Agent Reinforcement Lea...

引用

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

作者： Thomas Gabel Martin Riedmiller Department of Mathematics and Computer Science Institute of Cognitive Science University of Osnabrück Osnabruck Germany

In this paper, we suggest and analyze the use of approximate reinforcement learning techniques for a new category of challenging benchmark problems from the field of operations research. We demonstrate that interpreting and solving the task of job-shop scheduling as a multi-agent learning problem is beneficial for obtaining near-optimal solutions and can very well compete with alternative solution approaches. The evaluation of our algorithms focuses on numerous established operations research benchmark problems

关键词： learning Operations research Scheduling algorithm dynamic programming Application software Mathematics Computer science Cognitive science Bicycles Bridges

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Active exploration for robot parameter selection in episodic reinforcement learning

Active exploration for robot parameter selection in episodic...

引用

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

作者： Oliver Kroemer Jan Peters Max-Planck Institute Tubingen Germany

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.

关键词： Robots Ground penetrating radar Tuning Upper bound Kernel Grasping Convergence

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Knowledge Transfer Using Local Features

Knowledge Transfer Using Local Features

引用

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

作者： Martin Stolle Christopher G. Atkeson Robotics Institute Carnegie Mellon University Pittsburgh PA USA

We present a method for reducing the effort required to compute policies for tasks based on solutions to previously solved tasks. The key idea is to use a learned intermediate policy based on local features to create an initial policy for the new task. In order to further improve this initial policy, we developed a form of generalized policy iteration. We achieve a substantial reduction in computation needed to find policies when previous experience is available

关键词： Knowledge transfer learning Automatic control Navigation dynamic programming Robots Artificial intelligence Strips Legged locomotion

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