Reinforcement learning for robot soccer

Riedmiller, Martin; Gabel, Thomas; Hafner, Roland; Lange, Sascha

doi:10.1007/s10514-009-9120-4

Cited by 230 publications

(127 citation statements)

References 33 publications

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“…The reason behind this problem is the high complexity of the robots hardware and the complexity of the robot-to-robot interactions. Examples of techniques to reduce the state space dimension have been used by Riedmiller et al (2009). In this work, the authors applied neural networks as function approximators together with fast learning algorithms (Kalyanakrishnan and Stone 2007).…”

Section: Automatic Design Methodsmentioning

confidence: 99%

Swarm robotics: a review from the swarm engineering perspective

et al. 2013

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Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.

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Section: Automatic Design Methodsmentioning

confidence: 99%

Swarm robotics: a review from the swarm engineering perspective

et al. 2013

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“…Some recent examples of NFQ in real-world applications are learning to swing-up and balance a real cart-pole system, time optimal position control of pneumatic devices, and learning to accurately steer a real car within less than half an hour of driving . The following briefly describes the learning of a neural dribble controller for a RoboCup MidSize League robot (for more details, see also Riedmiller et al (2009)). The autonomous robot (figure 6) uses a camera as its main sensor and is fitted with an omnidirectional drive.…”

Section: Nfq In Control Applicationsmentioning

confidence: 99%

Batch Reinforcement Learning

Lange

Gabel

Riedmiller

2012

Adaptation, Learning, and Optimization

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320

225

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Batch reinforcement learning is a subfield of dynamic programming-based reinforcement learning. Originally defined as the task of learning the best possible policy from a fixed set of a priori-known transition samples, the (batch) algorithms developed in this field can be easily adapted to the classical online case, where the agent interacts with the environment while learning. Due to the efficient use of collected data and the stability of the learning process, this research area has attracted a lot of attention recently. In this chapter, we introduce the basic principles and the theory behind batch reinforcement learning, describe the most important algorithms, exemplarily discuss ongoing research within this field, and briefly survey real-world applications of batch reinforcement learning.

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“…Abbeel et al [2006,2007], Atkeson and Schaal [1997], Atkeson [1998] Asada et al [1996], Bakker et al [2003], Benbrahim et al [1992], Benbrahim and Franklin [1997], Birdwell and Livingston [2007], Bitzer et al [2010], Conn and Peters II [2007], Duan et al [2007Duan et al [ , 2008, Fagg et al [1998], Gaskett et al [2000], Gräve et al [2010], Hafner and Riedmiller [2007], Huang and Weng [2002], Ilg et al [1999], Katz et al [2008], Kimura et al [2001], Kirchner [1997], Kroemer et al [2009, Latzke et al [2007], Lizotte et al [2007], Mahadevan and Connell [1992], Mataric [1997], Nemec et al [2009Nemec et al [ , 2010, Oßwald et al [2010], Paletta et al [2007], Platt et al [2006], Riedmiller et al [2009], Rottmann et al [2007], Kaelbling [1998, 2002] called the value function, and use it to reconstruct the optimal policy. A wide variety of methods exist and can be split mainly into three classes: (i) dynamic programming-based optimal control approaches such as policy iteration or value iteration, (ii) rollout-based Monte Carlo methods and (iii) temporal difference methods such as TD(λ), Q-learning, and SARSA.…”

Section: Model-basedmentioning

confidence: 99%

Learning motor skills: from algorithms to robot experiments

Kober

Peters

2014

It - Information Technology

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Die Veröffentlichung steht unter folgender Creative Commons Lizenz: Namensnennung -Keine kommerzielle Nutzung -Keine Bearbeitung 2.0 Deutschland http://creativecommons.org/licenses/by-nc-nd/2.0/de/ Abstract Ever since the word "robot" was introduced to the English language by KarelČapek's play "Rossum's Universal Robots" in 1921, robots have been expected to become part of our daily lives. In recent years, robots such as autonomous vacuum cleaners, lawn mowers, and window cleaners, as well as a huge number of toys have been made commercially available. However, a lot of additional research is required to turn robots into versatile household helpers and companions. One of the many challenges is that robots are still very specialized and cannot easily adapt to changing environments and requirements. Since the 1960s, scientists attempt to provide robots with more autonomy, adaptability, and intelligence. Research in this field is still very active but has shifted focus from reasoning based methods towards statistical machine learning. Both navigation (i.e., moving in unknown or changing environments) and motor control (i.e., coordinating movements to perform skilled actions) are important sub-tasks.In this thesis, we will discuss approaches that allow robots to learn motor skills. We mainly consider tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The presented tasks correspond to sports and games but the presented techniques will also be applicable to more mundane household tasks. Motor skills can often be represented by motor primitives. Such motor primitives encode elemental motions which can be generalized, sequenced, and combined to achieve more complex tasks. For example, a forehand and a backhand could be seen as two different motor primitives of playing table tennis. We show how motor primitives can be employed to learn motor skills on three different levels. First, we discuss how a single motor skill, represented by a motor primitive, can be learned using reinforcement learning. Second, we show how such learned motor primitives can be generalized to new situations. Finally, we present first steps towards using motor primitives in a hierarchical setting and how several motor primitives can be combined to achieve more complex tasks.To date, there have been a number of successful applications of learning motor primitives employing imitation learning. However, many interesting motor learning problems are high-dimensional reinforcement learning problems which are often beyond the reach of current reinforcement learning methods. We review research on reinforcement learning applied to robotics and point out key challenges and important strategies to render reinforcement learning tractable. Based on these insights, we introduce novel learning approaches both for single and generalized motor skills.For learning single motor skills, we study parametrized policy search methods and introduce a framework of reward-weighted imi...

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Reinforcement learning for robot soccer

Cited by 230 publications

References 33 publications

Swarm robotics: a review from the swarm engineering perspective

Swarm robotics: a review from the swarm engineering perspective

Batch Reinforcement Learning

Learning motor skills: from algorithms to robot experiments

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