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70
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 224 (42 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.
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 204 (17 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.
Approximate Policy Iteration with a Policy Language Bias
 Journal of Artificial Intelligence Research
, 2003
"... We explore approximate policy iteration (API), replacing the usual costfunction learning step with a learning step in policy space. We give policylanguage biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve. ..."
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Cited by 140 (18 self)
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We explore approximate policy iteration (API), replacing the usual costfunction learning step with a learning step in policy space. We give policylanguage biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve.
Policy search for motor primitives in robotics
 Advances in Neural Information Processing Systems 22 (NIPS 2008
, 2009
"... Many motor skills in humanoid robotics can be learned using parametrized motor primitives as done in imitation learning. However, most interesting motor learning problems are highdimensional reinforcement learning problems often beyond the reach of current methods. In this paper, we extend previou ..."
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Cited by 117 (24 self)
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Many motor skills in humanoid robotics can be learned using parametrized motor primitives as done in imitation learning. However, most interesting motor learning problems are highdimensional reinforcement learning problems often beyond the reach of current methods. In this paper, we extend previous work on policy learning from the immediate reward case to episodic reinforcement learning. We show that this results in a general, common framework also connected to policy gradient methods and yielding a novel algorithm for policy learning that is particularly wellsuited for dynamic motor primitives. The resulting algorithm is an EMinspired algorithm applicable to complex motor learning tasks. We compare this algorithm to several wellknown parametrized policy search methods and show that it outperforms them. We apply it in the context of motor learning and show that it can learn a complex BallinaCup task using a real Barrett WAMTM robot arm. 1
Exploiting Structure to Efficiently Solve Large Scale Partially Observable Markov Decision Processes
, 2005
"... Partially observable Markov decision processes (POMDPs) provide a natural and principled framework to model a wide range of sequential decision making problems under uncertainty. To date, the use of POMDPs in realworld problems has been limited by the poor scalability of existing solution algorithm ..."
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Cited by 91 (6 self)
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Partially observable Markov decision processes (POMDPs) provide a natural and principled framework to model a wide range of sequential decision making problems under uncertainty. To date, the use of POMDPs in realworld problems has been limited by the poor scalability of existing solution algorithms, which can only solve problems with up to ten thousand states. In fact, the complexity of finding an optimal policy for a finitehorizon discrete POMDP is PSPACEcomplete. In practice, two important sources of intractability plague most solution algorithms: large policy spaces and large state spaces. On the other hand,
Reinforcement Learning in Robotics: A Survey
"... Reinforcement learning offers to robotics a framework and set oftoolsfor the design of sophisticated and hardtoengineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between di ..."
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Cited by 39 (2 self)
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Reinforcement learning offers to robotics a framework and set oftoolsfor the design of sophisticated and hardtoengineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between modelbased and modelfree as well as between value functionbased and policy search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and
Learning by demonstration with critique from a human teacher. HRI
, 2007
"... Learning by demonstration can be a powerful and natural tool for developing robot control policies. That is, instead of tedious handcoding, a robot may learn a control policy by interacting with a teacher. In this work we present an algorithm for learning by demonstration in which the teacher opera ..."
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Cited by 31 (2 self)
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Learning by demonstration can be a powerful and natural tool for developing robot control policies. That is, instead of tedious handcoding, a robot may learn a control policy by interacting with a teacher. In this work we present an algorithm for learning by demonstration in which the teacher operates in two phases. The teacher first demonstrates the task to the learner. The teacher next critiques learner performance of the task. This critique is used by the learner to update its control policy. In our implementation we utilize a 1Nearest Neighbor technique which incorporates both training dataset and teacher critique. Since the teacher critiques performance only, they do not need to guess at an effective critique for the underlying algorithm. We argue that this method is particularly wellsuited to human teachers, who are generally better at assigning credit to performances than to algorithms. We have applied this algorithm to the simulated task of a robot intercepting a ball. Our results demonstrate improved performance with teacher critiquing, where performance is measured by both execution success and efficiency.
R.: Analysis of a classificationbased policy iteration algorithm
 In: Proceedings of the 27th International Conference on Machine Learning
, 2010
"... Abstract We present a classificationbased policy iteration algorithm, called Direct Policy Iteration, and provide its finitesample analysis. Our results state a performance bound in terms of the number of policy improvement steps, the number of rollouts used in each iteration, the capacity of the ..."
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Cited by 31 (9 self)
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Abstract We present a classificationbased policy iteration algorithm, called Direct Policy Iteration, and provide its finitesample analysis. Our results state a performance bound in terms of the number of policy improvement steps, the number of rollouts used in each iteration, the capacity of the considered policy space, and a new capacity measure which indicates how well the policy space can approximate policies that are greedy w.r.t. any of its members. The analysis reveals a tradeoff between the estimation and approximation errors in this classificationbased policy iteration setting. We also study the consistency of the method when there exists a sequence of policy spaces with increasing capacity.
Machine Learning for Motor Skills in Robotics.
, 2007
"... Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach pure ..."
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Cited by 25 (3 self)
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Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks of future robots. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trialanderror. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the highdimensional domains of manipulator and humanoid robotics and usually scaling was only achieved in precisely prestructured domains. We have investigated the ingredients for a general approach to motor skill learning in order to get one step closer towards humanlike performance. For doing so, we study two major components for such an approach, i.e., firstly, a theoretically wellfounded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting.
Monte Carlo Value Iteration for ContinuousState POMDPs
 WORKSHOP ON THE ALGORITHMIC FOUNDATIONS OF ROBOTICS
, 2010
"... Partially observable Markov decision processes (POMDPs) have been successfully applied to various robot motion planning tasks under uncertainty. However, most existing POMDP algorithms assume a discrete state space, while the natural state space of a robot is often continuous. This paper presents Mo ..."
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Cited by 22 (4 self)
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Partially observable Markov decision processes (POMDPs) have been successfully applied to various robot motion planning tasks under uncertainty. However, most existing POMDP algorithms assume a discrete state space, while the natural state space of a robot is often continuous. This paper presents Monte Carlo Value Iteration (MCVI) for continuousstate POMDPs. MCVI samples both a robot’s state space and the corresponding belief space, and avoids inefficient a priori discretization of the state space as a grid. Both theoretical results and preliminary experimental results indicate that MCVI is a promising new approach for robot motion planning under uncertainty.