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 #6163. 	[Title] Generalization in Reinforcement Learning: Safely Approximating the Value Function [Abstract] To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to the curse of dimensionality in reinforcement learning and dynamic programming is to replace the lookup table with a generalizing function approximator such as a neural net. Although this has been successful in the domain of backgammon, there is no guarantee of convergence. In this paper, we show that the combination of dynamic programming and function approximation is not robust, and in even very benign cases, may produce an entirely wrong policy. We then introduce Grow-Support, a new algorithm which is safe from divergence yet can still reap the benefits of successful generalization. Please summarise all the given information.

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 #3243. 	[Title] A System for Induction of Oblique Decision Trees [Abstract] This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6196. 	[Title] The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces [Abstract] Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. In high dimensions it is essential that learning does not plan uniformly over a state-space. Parti-game maintains a decision-tree partitioning of state-space and applies techniques from game-theory and computational geometry to efficiently and adaptively concentrate high resolution only on critical areas. The current version of the algorithm is designed to find feasible paths or trajectories to goal regions in high dimensional spaces. Future versions will be designed to find a solution that optimizes a real-valued criterion. Many simulated problems have been tested, ranging from two-dimensional to nine-dimensional state-spaces, including mazes, path planning, non-linear dynamics, and planar snake robots in restricted spaces. In all cases, a good solution is found in less than ten trials and a few minutes. 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.

Above are papers related to the following paper: [Paper_ID] Paper #6213. 	[Title] Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction, [Abstract] This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods; and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage. 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 #96851. 	[Title] Balancing Accuracy and Parsimony in Genetic Programming 1 [Abstract] Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this paper we investigate the fundamental relationship between the performance and complexity of the evolved structures. The essence of the parsimony problem is demonstrated empirically by analyzing error landscapes of programs evolved for neural network synthesis. We consider genetic programming as a statistical inference problem and apply the Bayesian model-comparison framework to introduce a class of fitness functions with error and complexity terms. An adaptive learning method is then presented that automatically balances the model-complexity factor to evolve parsimonious programs without losing the diversity of the population needed for achieving the desired training accuracy. The effectiveness of this approach is empirically shown on the induction of sigma-pi neural networks for solving a real-world medical diagnosis problem as well as benchmark tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1365. 	[Title] Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex. [Abstract] Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feed-forward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedfor-ward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28249. 	[Title] Truncating Temporal Differences: On the Efficient Implementation of TD() for Reinforcement Learning [Abstract] Temporal difference (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor . Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the efficient and general implementation of TD() for arbitrary , for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suffer from both inefficiency and lack of generality. The TTD (Truncated Temporal Differences) procedure is proposed as an alternative, that indeed only approximates TD(), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using &gt; 0 with the TTD procedure allows one to obtain a significant learning speedup at essentially the same cost as usual TD(0) learning. 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 #4584. 	[Title] References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages [Abstract] Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic neural networks. Technical Report FKI-129-90, Technische Universitat Munchen, Institut fu Informatik. Servan-Schreiber, D., Cleermans, A., and McClelland, J. (1988). Encoding sequential structure in simple recurrent networks. Technical Report CMU-CS-88-183, Carnegie Mellon University, Computer Science Department. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #594025. 	[Title] Evolving a Generalised Behaviour: Artificial Ant Problem Revisited [Abstract] This research aims to demonstrate that a solution for artificial ant problem [4] is very likely to be non-general and relying on the specific characteristics of the Santa Fe trail. It then presents a consistent method which promotes producing general solutions. Using the concepts of training and testing from machine learning research, the method can be useful in producing general behaviours for simulation environments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #227178. 	[Title] Evolving Visual Routines [Abstract] Traditional machine vision assumes that the vision system recovers a a complete, labeled description of the world [ Marr, 1982 ] . Recently, several researchers have criticized this model and proposed an alternative model which considers perception as a distributed collection of task-specific, task-driven visual routines [ Aloimonos, 1993, Ullman, 1987 ] . Some of these researchers have argued that in natural living systems these visual routines are the product of natural selection [ Ramachandran, 1985 ] . So far, researchers have hand-coded task-specific visual routines for actual implementations (e.g. [ Chapman, 1993 ] ). In this paper we propose an alternative approach in which visual routines for simple tasks are evolved using an artificial evolution approach. We present results from a series of runs on actual camera images, in which simple routines were evolved using Genetic Programming techniques [ Koza, 1992 ] . The results obtained are promising: the evolved routines are able to correctly classify up to 93% of the images, which is better than the best algorithm we were able to write by hand. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #114. 	[Title] Learning to Act using Real-Time Dynamic Programming [Abstract] fl The authors thank Rich Yee, Vijay Gullapalli, Brian Pinette, and Jonathan Bachrach for helping to clarify the relationships between heuristic search and control. We thank Rich Sutton, Chris Watkins, Paul Werbos, and Ron Williams for sharing their fundamental insights into this subject through numerous discussions, and we further thank Rich Sutton for first making us aware of Korf's research and for his very thoughtful comments on the manuscript. We are very grateful to Dimitri Bertsekas and Steven Sullivan for independently pointing out an error in an earlier version of this article. Finally, we thank Harry Klopf, whose insight and persistence encouraged our interest in this class of learning problems. This research was supported by grants to A.G. Barto from the National Science Foundation (ECS-8912623 and ECS-9214866) and the Air Force Office of Scientific Research, Bolling AFB (AFOSR-89-0526). 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 #887. 	[Title] Some studies in machine learning using the game of checkers. IBM Journal, 3(3):211-229, 1959. Some [Abstract]  Please summarise all the given information.

Paper #6216 has the title `Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding  
` and the 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 .
`.
Question: Which category should Paper #6216 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #6216 has the title `Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding  
` and the 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 .
`.
Question: Which category should Paper #6216 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