Above are papers related to the following paper: [Paper_ID] Paper #1121739. 	[Title] Simultaneous Learning of Control Laws and Local Environment Representations for Intelligent Navigation Robots [Abstract] Two issues of an intelligent navigation robot have been addressed in this work. First is the robot's ability to learn a representation of the local environment and use this representation to identify which local environment it is in. This is done by first extracting features from the sensors which are more informative than just distances of obstacles in various directions. Using these features a reduced ring representation (RRR) of the local environment is derived. As the robot navigates, it learns the RRR signatures of all the new environment types it encounters. For purpose of identification, a ring matching criteria is proposed where the robot tries to match the RRR from the sensory input to one of the RRRs in its library. The second issue addressed is that of learning hill climbing control laws in the local environments. Unlike conventional neuro-controllers, a reinforcement learning framework, where the robot first learns a model of the environment and then learns the control law in terms of a neural network is proposed here. The reinforcement function is generated from the sensory inputs of the robot before and after a control action is taken. Three key results shown in this work are that (1) The robot is able to build its library of RRR signatures perfectly even with significant sensor noise for eight different local environ-mets, (2) It was able to identify its local environment with an accuracy of more than 96%, once the library is build, and (3) the robot was able to learn adequate hill climbing control laws which take it to the distinctive state of the local environment for five different environment types. Please summarise all the given information.

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Above are papers related to the following paper: [Paper_ID] Paper #3229. 	[Title] Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2 [Abstract] Neural computation, also called connectionism, parallel distributed processing, neural network modeling or brain-style computation, has grown rapidly in the last decade. Despite this explosion, and ultimately because of impressive applications, there has been a dire need for a concise introduction from a theoretical perspective, analyzing the strengths and weaknesses of connectionist approaches and establishing links to other disciplines, such as statistics or control theory. The Introduction to the Theory of Neural Computation by Hertz, Krogh and Palmer (subsequently referred to as HKP) is written from the perspective of physics, the home discipline of the authors. The book fulfills its mission as an introduction for neural network novices, provided that they have some background in calculus, linear algebra, and statistics. It covers a number of models that are often viewed as disjoint. Critical analyses and fruitful comparisons between these models Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #118559. 	[Title] On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning [Abstract] Several researchers have demonstrated how neural networks can be trained to compensate for nonlinear signal distortion in e.g. digital satellite communications systems. These networks, however, require that both the original signal and its distorted version are known. Therefore, they have to be trained off-line, and they cannot adapt to changing channel characteristics. In this paper, a novel dual reinforcement learning approach is proposed that can adapt on-line while the system is performing. Assuming that the channel characteristics are the same in both directions, two predistorters at each end of the communication channel co-adapt using the output of the other predistorter to determine their own reinforcement. Using the common Volterra Series model to simulate the channel, the system is shown to successfully learn to compensate for distortions up to 30%, which is significantly higher than what might be expected in an actual channel. Please summarise all the given information.

Paper #1121739 has the title `Simultaneous Learning of Control Laws and Local Environment Representations for Intelligent Navigation Robots  
` and the abstract `Two issues of an intelligent navigation robot have been addressed in this work. First is the robot's ability to learn a representation of the local environment and use this representation to identify which local environment it is in. This is done by first extracting features from the sensors which are more informative than just distances of obstacles in various directions. Using these features a reduced ring representation (RRR) of the local environment is derived. As the robot navigates, it learns the RRR signatures of all the new environment types it encounters. For purpose of identification, a ring matching criteria is proposed where the robot tries to match the RRR from the sensory input to one of the RRRs in its library. The second issue addressed is that of learning hill climbing control laws in the local environments. Unlike conventional neuro-controllers, a reinforcement learning framework, where the robot first learns a model of the environment and then learns the control law in terms of a neural network is proposed here. The reinforcement function is generated from the sensory inputs of the robot before and after a control action is taken. Three key results shown in this work are that (1) The robot is able to build its library of RRR signatures perfectly even with significant sensor noise for eight different local environ-mets, (2) It was able to identify its local environment with an accuracy of more than 96%, once the library is build, and (3) the robot was able to learn adequate hill climbing control laws which take it to the distinctive state of the local environment for five different environment types.
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Question: Which category should Paper #1121739 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1121739 has the title `Simultaneous Learning of Control Laws and Local Environment Representations for Intelligent Navigation Robots  
` and the abstract `Two issues of an intelligent navigation robot have been addressed in this work. First is the robot's ability to learn a representation of the local environment and use this representation to identify which local environment it is in. This is done by first extracting features from the sensors which are more informative than just distances of obstacles in various directions. Using these features a reduced ring representation (RRR) of the local environment is derived. As the robot navigates, it learns the RRR signatures of all the new environment types it encounters. For purpose of identification, a ring matching criteria is proposed where the robot tries to match the RRR from the sensory input to one of the RRRs in its library. The second issue addressed is that of learning hill climbing control laws in the local environments. Unlike conventional neuro-controllers, a reinforcement learning framework, where the robot first learns a model of the environment and then learns the control law in terms of a neural network is proposed here. The reinforcement function is generated from the sensory inputs of the robot before and after a control action is taken. Three key results shown in this work are that (1) The robot is able to build its library of RRR signatures perfectly even with significant sensor noise for eight different local environ-mets, (2) It was able to identify its local environment with an accuracy of more than 96%, once the library is build, and (3) the robot was able to learn adequate hill climbing control laws which take it to the distinctive state of the local environment for five different environment types.
`.
Question: Which category should Paper #1121739 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Reinforcement_Learning