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Reinforcement Learning on a Omnidirectional Mobile Robot
 In Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), Las Vegas
, 2003
"... With this paper we describe a well suited, scalable problem for reinforcement learning approaches in the field of mobile robots. We show a suitable representation of the problem for a reinforcement approach and present our results with a model based standard algorithm. Two different approximators fo ..."
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Cited by 9 (5 self)
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With this paper we describe a well suited, scalable problem for reinforcement learning approaches in the field of mobile robots. We show a suitable representation of the problem for a reinforcement approach and present our results with a model based standard algorithm. Two different approximators for the value function are used, a grid based approximator and a neural network based approximator.
Improving iterative repair strategies for scheduling with the SVM
, 2005
"... Resource constraint project scheduling (RCPSP) is an NPhard benchmark problem in scheduling which takes into account the limitation of resources' availabilities in real life production processes. We here present an application of machine learning to adapt simple greedy strategies. ..."
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Cited by 7 (0 self)
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Resource constraint project scheduling (RCPSP) is an NPhard benchmark problem in scheduling which takes into account the limitation of resources' availabilities in real life production processes. We here present an application of machine learning to adapt simple greedy strategies.
Making a Robot Learn to Play Soccer Using Reward and Punishment
"... Abstract In this paper, we show how reinforcement learning can be applied to real robots to achieve optimal robot behavior. As example, we enable an autonomous soccer robot to learn intercepting a rolling ball. Main focus is on how to adapt the Qlearning algorithm to the needs of learning strategie ..."
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Cited by 6 (3 self)
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Abstract In this paper, we show how reinforcement learning can be applied to real robots to achieve optimal robot behavior. As example, we enable an autonomous soccer robot to learn intercepting a rolling ball. Main focus is on how to adapt the Qlearning algorithm to the needs of learning strategies for real robots and how to transfer strategies learned in simulation onto real robots. 1
Reinforcement Learning in a Nutshell
"... Abstract. We provide a concise introduction to basic approaches to reinforcement learning from the machine learning perspective. The focus is on value function and policy gradient methods. Some selected recent trends are highlighted. 1 ..."
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Cited by 2 (2 self)
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Abstract. We provide a concise introduction to basic approaches to reinforcement learning from the machine learning perspective. The focus is on value function and policy gradient methods. Some selected recent trends are highlighted. 1
Convergence of Synchronous Reinforcement Learning with Linear Function Approximation
, 2004
"... Synchronous reinforcement learning (RL) algorithms with linear function approximation are representable as inhomogeneous matrix iterations of a special form (Schoknecht & Merke, 2003). In this paper we state conditions of convergence for general inhomogeneous matrix iterations and prove th ..."
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Synchronous reinforcement learning (RL) algorithms with linear function approximation are representable as inhomogeneous matrix iterations of a special form (Schoknecht & Merke, 2003). In this paper we state conditions of convergence for general inhomogeneous matrix iterations and prove that they are both necessary and su#cient. This result extends the work presented in (Schoknecht & Merke, 2003), where only a su#cient condition of convergence was proved. As the condition of convergence is necessary and sufficient, the new result is suitable to prove convergence and divergence of RL algorithms with function approximation. We use the theorem to deduce a new concise proof of convergence for the synchronous residual gradient algorithm (Baird, 1995). Moreover, we derive a counterexample for which the uniform RL algorithm (Merke & Schoknecht, 2002) diverges. This yields a negative answer to the open question if the uniform RL algorithm converges for arbitrary multiple transitions.
Generalization in Reinforcement Learning and the Use of ObservationsBased Learning
"... In this paper we review the eld of reinforcement learning under the aspect of generalization abilities. We develop a framework describing the facets of generalization in reinforcement learning and formulate some consequences like the introduction of observations, among others. We show the princi ..."
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In this paper we review the eld of reinforcement learning under the aspect of generalization abilities. We develop a framework describing the facets of generalization in reinforcement learning and formulate some consequences like the introduction of observations, among others. We show the principles of learning in this framework and give an exemplary observationbased learning algorithm.