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267
Perseus: Randomized pointbased value iteration for POMDPs
 Journal of Artificial Intelligence Research
, 2005
"... Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Pointbased approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent’s belief space. We present a ra ..."
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Cited by 141 (11 self)
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Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Pointbased approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent’s belief space. We present a randomized pointbased value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Contrary to other pointbased methods, Perseus backs up only a (randomly selected) subset of points in the belief set, sufficient for improving the value of each belief point in the set. We show how the same idea can be extended to dealing with continuous action spaces. Experimental results show the potential of Perseus in large scale POMDP problems. 1.
Treebased batch mode reinforcement learning
 Journal of Machine Learning Research
, 2005
"... Reinforcement learning aims to determine an optimal control policy from interaction with a system or from observations gathered from a system. In batch mode, it can be achieved by approximating the socalled Qfunction based on a set of fourtuples (xt,ut,rt,xt+1) where xt denotes the system state a ..."
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Cited by 134 (28 self)
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Reinforcement learning aims to determine an optimal control policy from interaction with a system or from observations gathered from a system. In batch mode, it can be achieved by approximating the socalled Qfunction based on a set of fourtuples (xt,ut,rt,xt+1) where xt denotes the system state at time t, ut the control action taken, rt the instantaneous reward obtained and xt+1 the successor state of the system, and by determining the control policy from this Qfunction. The Qfunction approximation may be obtained from the limit of a sequence of (batch mode) supervised learning problems. Within this framework we describe the use of several classical treebased supervised learning methods (CART, Kdtree, tree bagging) and two newly proposed ensemble algorithms, namely extremely and totally randomized trees. We study their performances on several examples and find that the ensemble methods based on regression trees perform well in extracting relevant information about the optimal control policy from sets of fourtuples. In particular, the totally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accuracy results are provided by the extremely randomized trees.
Learning nearoptimal policies with Bellmanresidual minimization based fitted policy iteration and a single sample path
 MACHINE LEARNING JOURNAL (2008) 71:89129
, 2008
"... ..."
Evolutionary function approximation for reinforcement learning
 Journal of Machine Learning Research
, 2006
"... Ø�ÓÒ�ÔÔÖÓÜ�Ñ�Ø�ÓÒ�ÒÓÚ�Ð�ÔÔÖÓ��ØÓ�ÙØÓÑ�Ø��ÐÐÝ× � Ø�ÓÒ�Ð���×�ÓÒ×Ì��×Ø��×�×�ÒÚ�×Ø���Ø�×�ÚÓÐÙØ�ÓÒ�ÖÝ�ÙÒ �Ò�ÓÖ�Ñ�ÒØÐ��ÖÒ�Ò�ÔÖÓ�Ð�Ñ×�Ö�Ø��×Ù�×�ØÓ�Ø��×�Ø�×� × ÁÒÑ�ÒÝÑ���Ò�Ð��ÖÒ�Ò�ÔÖÓ�Ð�Ñ×�Ò���ÒØÑÙ×ØÐ��ÖÒ Ñ�ÒØ���Ò×Ø�ÒØ��Ø�ÓÒÓ��ÚÓÐÙØ�ÓÒ�ÖÝ�ÙÒØ�ÓÒ�ÔÔÖÓÜ�Ñ � Ù�Ð×Ø��Ø�Ö���ØØ�Ö��Ð�ØÓÐ��ÖÒÁÔÖ�×�ÒØ��ÙÐÐÝ�ÑÔÐ � Ø�Ó ..."
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Cited by 72 (15 self)
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Ø�ÓÒ�ÔÔÖÓÜ�Ñ�Ø�ÓÒ�ÒÓÚ�Ð�ÔÔÖÓ��ØÓ�ÙØÓÑ�Ø��ÐÐÝ× � Ø�ÓÒ�Ð���×�ÓÒ×Ì��×Ø��×�×�ÒÚ�×Ø���Ø�×�ÚÓÐÙØ�ÓÒ�ÖÝ�ÙÒ �Ò�ÓÖ�Ñ�ÒØÐ��ÖÒ�Ò�ÔÖÓ�Ð�Ñ×�Ö�Ø��×Ù�×�ØÓ�Ø��×�Ø�×� × ÁÒÑ�ÒÝÑ���Ò�Ð��ÖÒ�Ò�ÔÖÓ�Ð�Ñ×�Ò���ÒØÑÙ×ØÐ��ÖÒ Ñ�ÒØ���Ò×Ø�ÒØ��Ø�ÓÒÓ��ÚÓÐÙØ�ÓÒ�ÖÝ�ÙÒØ�ÓÒ�ÔÔÖÓÜ�Ñ � Ù�Ð×Ø��Ø�Ö���ØØ�Ö��Ð�ØÓÐ��ÖÒÁÔÖ�×�ÒØ��ÙÐÐÝ�ÑÔÐ � Ø�ÓÒÛ���ÓÑ��Ò�×Æ��Ì�Ò�ÙÖÓ�ÚÓÐÙØ�ÓÒ�ÖÝÓÔØ�Ñ�Þ � Ð�Ø�Ò��ÙÒØ�ÓÒ�ÔÔÖÓÜ�Ñ�ØÓÖÖ�ÔÖ�×�ÒØ�Ø�ÓÒ×Ø��Ø�Ò��Ð� Ø�ÓÒØ��Ò�ÕÙ�Û�Ø�ÉÐ��ÖÒ�Ò��ÔÓÔÙÐ�ÖÌ�Ñ�Ø�Ó�Ì� � �Æ��ÒØ�Ò��Ú��Ù�ÐÐ��ÖÒ�Ò�Ì��×Ñ�Ø�Ó��ÚÓÐÚ�×�Ò��Ú� � ÓÔØ�Ñ�Þ�Ø�ÓÒ��ÐÐ�ÒØ��×�Ø��ÓÖÝ��Ú�ÐÓÔ�Ò��«�Ø�Ú�Ö��Ò �ÓÖÁÒ×Ø����ØÖ���Ú�×ÓÒÐÝÔÓ×�Ø�Ú��Ò�Ò���Ø�Ú�Ö�Û�Ö� × ÔÖÓ�Ð�Ñ××Ù��×ÖÓ�ÓØÓÒØÖÓÐ��Ñ�ÔÐ�Ý�Ò��Ò�×Ý×Ø�Ñ �ÒÛ���Ø�����ÒØÒ�Ú�Ö×��×�Ü�ÑÔÐ�×Ó�ÓÖÖ�Ø����Ú 1.
Protovalue functions: A laplacian framework for learning representation and control in markov decision processes
 Journal of Machine Learning Research
, 2006
"... This paper introduces a novel spectral framework for solving Markov decision processes (MDPs) by jointly learning representations and optimal policies. The major components of the framework described in this paper include: (i) A general scheme for constructing representations or basis functions by d ..."
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Cited by 66 (10 self)
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This paper introduces a novel spectral framework for solving Markov decision processes (MDPs) by jointly learning representations and optimal policies. The major components of the framework described in this paper include: (i) A general scheme for constructing representations or basis functions by diagonalizing symmetric diffusion operators (ii) A specific instantiation of this approach where global basis functions called protovalue functions (PVFs) are formed using the eigenvectors of the graph Laplacian on an undirected graph formed from state transitions induced by the MDP (iii) A threephased procedure called representation policy iteration comprising of a sample collection phase, a representation learning phase that constructs basis functions from samples, and a final parameter estimation phase that determines an (approximately) optimal policy within the (linear) subspace spanned by the (current) basis functions. (iv) A specific instantiation of the RPI framework using leastsquares policy iteration (LSPI) as the parameter estimation method (v) Several strategies for scaling the proposed approach to large discrete and continuous state spaces, including the Nyström extension for outofsample interpolation of eigenfunctions, and the use of Kronecker sum factorization to construct compact eigenfunctions in product spaces such as factored MDPs (vi) Finally, a series of illustrative discrete and continuous control tasks, which both illustrate the concepts and provide a benchmark for evaluating the proposed approach. Many challenges remain to be addressed in scaling the proposed framework to large MDPs, and several elaboration of the proposed framework are briefly summarized at the end.
Transfer Learning for Reinforcement Learning Domains: A Survey
"... The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of ..."
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Cited by 48 (7 self)
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The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work.
Regularization and feature selection in leastsquares temporal difference learning (full version). Available at http://ai.stanford.edu/˜kolter
, 2009
"... We consider the task of reinforcement learning with linear value function approximation. Temporal difference algorithms, and in particular the LeastSquares Temporal Difference (LSTD) algorithm, provide a method for learning the parameters of the value function, but when the number of features is la ..."
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Cited by 48 (1 self)
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We consider the task of reinforcement learning with linear value function approximation. Temporal difference algorithms, and in particular the LeastSquares Temporal Difference (LSTD) algorithm, provide a method for learning the parameters of the value function, but when the number of features is large this algorithm can overfit to the data and is computationally expensive. In this paper, we propose a regularization framework for the LSTD algorithm that overcomes these difficulties. In particular, we focus on the case of l1 regularization, which is robust to irrelevant features and also serves as a method for feature selection. Although the l1 regularized LSTD solution cannot be expressed as a convex optimization problem, we present an algorithm similar to the Least Angle Regression (LARS) algorithm that can efficiently compute the optimal solution. Finally, we demonstrate the performance of the algorithm experimentally. 1.
Neural fitted Q iteration – first experiences with a data efficient neural reinforcement learning method
 In 16th European Conference on Machine Learning
, 2005
"... Abstract. This paper introduces NFQ, an algorithm for efficient and effective training of a Qvalue function represented by a multilayer perceptron. Based on the principle of storing and reusing transition experiences, a modelfree, neural network based Reinforcement Learning algorithm is proposed. ..."
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Cited by 45 (14 self)
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Abstract. This paper introduces NFQ, an algorithm for efficient and effective training of a Qvalue function represented by a multilayer perceptron. Based on the principle of storing and reusing transition experiences, a modelfree, neural network based Reinforcement Learning algorithm is proposed. The method is evaluated on three benchmark problems. It is shown empirically, that reasonably few interactions with the plant are needed to generate control policies of high quality. 1
Analyzing feature generation for valuefunction approximation
 In Proceedings of the 24th International Conference on Machine Learning
, 2007
"... We analyze a simple, Bellmanerrorbased approach to generating basis functions for valuefunction approximation. We show that it generates orthogonal basis functions that provably tighten approximation error bounds. We also illustrate the use of this approach in the presence of noise on some sample ..."
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Cited by 43 (5 self)
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We analyze a simple, Bellmanerrorbased approach to generating basis functions for valuefunction approximation. We show that it generates orthogonal basis functions that provably tighten approximation error bounds. We also illustrate the use of this approach in the presence of noise on some sample problems. 1.