Above are papers related to the following paper: [Paper_ID] Paper #230300. 	[Title] Learning in Continuous Domains with Delayed Rewards [Abstract] Much has been done to develop learning techniques for delayed reward problems in worlds where the actions and/or states are approximated by discrete representations. Although this is acceptable in some applications there are many more situations where such an approximation is difficult and unnatural. For instance, in applications such as robotic,s where real machines interact with the real world, learning techniques that use real valued continuous quantities are required. Presented in this paper is an extension to Q-learning that uses both real valued states and actions. This is achieved by introducing activation strengths to each actuator system of the robot. This allow all actuators to be active to some continuous amount simultaneously. Learning occurs by incrementally adapting both the expected future reward to goal evaluation function and the gradients of that function with respect to each actuator system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6209. 	[Title] Transfer of Learning by Composing Solutions of Elemental Sequential Tasks [Abstract] Although building sophisticated learning agents that operate in complex environments will require learning to perform multiple tasks, most applications of reinforcement learning have focussed on single tasks. In this paper I consider a class of sequential decision tasks (SDTs), called composite sequential decision tasks, formed by temporally concatenating a number of elemental sequential decision tasks. Elemental SDTs cannot be decomposed into simpler SDTs. I consider a learning agent that has to learn to solve a set of elemental and composite SDTs. I assume that the structure of the composite tasks is unknown to the learning agent. The straightforward application of reinforcement learning to multiple tasks requires learning the tasks separately, which can waste computational resources, both memory and time. I present a new learning algorithm and a modular architecture that learns the decomposition of composite SDTs, and achieves transfer of learning by sharing the solutions of elemental SDTs across multiple composite SDTs. The solution of a composite SDT is constructed by computationally inexpensive modifications of the solutions of its constituent elemental SDTs. I provide a proof of one aspect of the learning algorithm. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1123553. 	[Title] Modeling the Student with Reinforcement Learning [Abstract] We describe a methodology for enabling an intelligent teaching system to make high level strategy decisions on the basis of low level student modeling information. This framework is less costly to construct, and superior to hand coding teaching strategies as it is more responsive to the learner's needs. In order to accomplish this, reinforcement learning is used to learn to associate superior teaching actions with certain states of the student's knowledge. Reinforcement learning (RL) has been shown to be flexible in handling noisy data, and does not need expert domain knowledge. A drawback of RL is that it often needs a significant number of trials for learning. We propose an off-line learning methodology using sample data, simulated students, and small amounts of expert knowledge to bypass this problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6216. 	[Title] Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding [Abstract] On large problems, reinforcement learning systems must use parameterized function approximators such as neural networks in order to generalize between similar situations and actions. In these cases there are no strong theoretical results on the accuracy of convergence, and computational results have been mixed. In particular, Boyan and Moore reported at last year's meeting a series of negative results in attempting to apply dynamic programming together with function approximation to simple control problems with continuous state spaces. In this paper, we present positive results for all the control tasks they attempted, and for one that is significantly larger. The most important differences are that we used sparse-coarse-coded function approximators (CMACs) whereas they used mostly global function approximators, and that we learned online whereas they learned o*ine. Boyan and Moore and others have suggested that the problems they encountered could be solved by using actual outcomes ("rollouts"), as in classical Monte Carlo methods, and as in the TD() algorithm when = 1. However, in our experiments this always resulted in substantially poorer performance. We conclude that reinforcement learning can work robustly in conjunction with function approximators, and that there is little justification at present for avoiding the case of general . Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113739. 	[Title] Decision Tree Function Approximation in Reinforcement Learning [Abstract] We present a decision tree based approach to function approximation in reinforcement learning. We compare our approach with table lookup and a neural network function approximator on three problems: the well known mountain car and pole balance problems as well as a simulated automobile race car. We find that the decision tree can provide better learning performance than the neural network function approximation and can solve large problems that are infeasible using table lookup. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #976334. 	[Title] Applying Online Search Techniques to Continuous-State Reinforcement Learning key to the success of the local [Abstract] In this paper, we describe methods for efficiently computing better solutions to control problems in continuous state spaces. We provide algorithms that exploit online search to boost the power of very approximate value functions discovered by traditional reinforcement learning techniques. We examine local searches, where the agent performs a finite-depth lookahead search, and global searches, where the agent performs a search for a trajectory all the way from the current state to a goal state. The key to the success of the global methods lies in using aggressive state-space search techniques such as uniform-cost search and A fl , tamed into a tractable form by exploiting neighborhood relations and trajectory constraints that arise from continuous-space dynamic control. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #96847. 	[Title] Generality versus Size in Genetic Programming [Abstract] Genetic Programming (GP) uses variable size representations as programs. Size becomes an important and interesting emergent property of the structures evolved by GP. The size of programs can be both a controlling and a controlled factor in GP search. Size influences the efficiency of the search process and is related to the generality of solutions. This paper analyzes the size and generality issues in standard GP and GP using subroutines and addresses the question whether such an analysis can help control the search process. We relate the size, generalization and modularity issues for programs evolved to control an agent in a dynamic and non-deterministic environment, as exemplified by the Pac-Man game. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #118259. 	[Title] Emergent Hierarchical Control Structures: Learning Reactive/Hierarchical Relationships in Reinforcement Environments [Abstract] The use of externally imposed hierarchical structures to reduce the complexity of learning control is common. However, it is acknowledged that learning the hierarchical structure itself is an important step towards more general (learning of many things as required) and less bounded (learning of a single thing as specified) learning. Presented in this paper is a reinforcement learning algorithm called Nested Q-learning that generates a hierarchical control structure in reinforcement learning domains. The emergent structure combined with learned bottom-up reactive reactions results in a reactive hierarchical control system. Effectively, the learned hierarchy decomposes what would otherwise be a monolithic evaluation function into many smaller evaluation functions that can be recombined without the loss of previously learned information. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #118260. 	[Title] Learning Hierarchical Control Structures for Multiple Tasks and Changing Environments [Abstract] While the need for hierarchies within control systems is apparent, it is also clear to many researchers that such hierarchies should be learned. Learning both the structure and the component behaviors is a difficult task. The benefit of learning the hierarchical structures of behaviors is that the decomposition of the control structure into smaller transportable chunks allows previously learned knowledge to be applied to new but related tasks. Presented in this paper are improvements to Nested Q-learning (NQL) that allow more realistic learning of control hierarchies in reinforcement environments. Also presented is a simulation of a simple robot performing a series of related tasks that is used to compare both hierarchical and non-hierarchal learning techniques. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1116347. 	[Title] Fast Online Q() [Abstract] Q()-learning uses TD()-methods to accelerate Q-learning. The update complexity of previous online Q() implementations based on lookup-tables is bounded by the size of the state/action space. Our faster algorithm's update complexity is bounded by the number of actions. The method is based on the observation that Q-value updates may be postponed until they are needed. Please summarise all the given information.

Paper #230300 has the title `Learning in Continuous Domains with Delayed Rewards  
` and the abstract `Much has been done to develop learning techniques for delayed reward problems in worlds where the actions and/or states are approximated by discrete representations. Although this is acceptable in some applications there are many more situations where such an approximation is difficult and unnatural. For instance, in applications such as robotic,s where real machines interact with the real world, learning techniques that use real valued continuous quantities are required. Presented in this paper is an extension to Q-learning that uses both real valued states and actions. This is achieved by introducing activation strengths to each actuator system of the robot. This allow all actuators to be active to some continuous amount simultaneously. Learning occurs by incrementally adapting both the expected future reward to goal evaluation function and the gradients of that function with respect to each actuator system. 
`.
Question: Which category should Paper #230300 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #230300 has the title `Learning in Continuous Domains with Delayed Rewards  
` and the abstract `Much has been done to develop learning techniques for delayed reward problems in worlds where the actions and/or states are approximated by discrete representations. Although this is acceptable in some applications there are many more situations where such an approximation is difficult and unnatural. For instance, in applications such as robotic,s where real machines interact with the real world, learning techniques that use real valued continuous quantities are required. Presented in this paper is an extension to Q-learning that uses both real valued states and actions. This is achieved by introducing activation strengths to each actuator system of the robot. This allow all actuators to be active to some continuous amount simultaneously. Learning occurs by incrementally adapting both the expected future reward to goal evaluation function and the gradients of that function with respect to each actuator system. 
`.
Question: Which category should Paper #230300 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