Above are papers related to the following paper: [Paper_ID] Paper #1152910. 	[Title]  [Abstract] Empirical Comparison of Gradient Descent and Exponentiated Gradient Descent in Supervised and Reinforcement Learning Technical Report 96-70 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #341188. 	[Title] A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms [Abstract] Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of value-function-based reinforcement-learning algorithms. The usefulness of the theorem lies in how it allows the asynchronous convergence of a complex reinforcement-learning algorithm to be proven by verifying that a simpler synchronous algorithm converges. We illustrate the application of the theorem by analyzing the convergence of Q-learning, model-based reinforcement learning, Q-learning with multi-state updates, Q-learning for Markov games, and risk-sensitive reinforcement learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28491. 	[Title] Machine Learning,  Reinforcement Learning with Replacing Eligibility Traces [Abstract] The eligibility trace is one of the basic mechanisms used in reinforcement learning to handle delayed reward. In this paper we introduce a new kind of eligibility trace, the replacing trace, analyze it theoretically, and show that it results in faster, more reliable learning than the conventional trace. Both kinds of trace assign credit to prior events according to how recently they occurred, but only the conventional trace gives greater credit to repeated events. Our analysis is for conventional and replace-trace versions of the o*ine TD(1) algorithm applied to undiscounted absorbing Markov chains. First, we show that these methods converge under repeated presentations of the training set to the same predictions as two well known Monte Carlo methods. We then analyze the relative efficiency of the two Monte Carlo methods. We show that the method corresponding to conventional TD is biased, whereas the method corresponding to replace-trace TD is unbiased. In addition, we show that the method corresponding to replacing traces is closely related to the maximum likelihood solution for these tasks, and that its mean squared error is always lower in the long run. Computational results confirm these analyses and show that they are applicable more generally. In particular, we show that replacing traces significantly improve performance and reduce parameter sensitivity on the "Mountain-Car" task, a full reinforcement-learning problem with a continuous state space, when using a feature-based function approximator. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #782486. 	[Title] Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques [Abstract] In this paper, we propose an unsupervised neural network allowing a robot to learn sensori-motor associations with a delayed reward. The robot task is to learn the "meaning" of pictograms in order to "survive" in a maze. First, we introduce a new neural conditioning rule (PCR: Probabilistic Conditioning Rule) allowing to test hypotheses (associations between visual categories and movements) during a given time span. Second, we describe a real maze experiment with our mobile robot. We propose a neural architecture to solve this problem and we discuss the difficulty to build visual categories dynamically while associating them to movements. Third, we propose to use our algorithm on a simulation in order to test it exhaustively. We give the results for different kind of mazes and we compare our system to an adapted version of the Q-learning algorithm. Finally, we conclude by showing the limitations of approaches that do not take into account the intrinsic complexity of a reasonning based on image recognition. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #153598. 	[Title] Analytical Mean Squared Error Curves in Temporal Difference Learning [Abstract] We have calculated analytical expressions for how the bias and variance of the estimators provided by various temporal difference value estimation algorithms change with o*ine updates over trials in absorbing Markov chains using lookup table representations. We illustrate classes of learning curve behavior in various chains, and show the manner in which TD is sensitive to the choice of its step size and eligibility trace parameters. Please summarise all the given information.

Paper #1152910 has the title `nan` and the abstract `Empirical Comparison of Gradient Descent and Exponentiated Gradient Descent in Supervised and Reinforcement Learning Technical Report 96-70 
`.
Question: Which category should Paper #1152910 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1152910 has the title `nan` and the abstract `Empirical Comparison of Gradient Descent and Exponentiated Gradient Descent in Supervised and Reinforcement Learning Technical Report 96-70 
`.
Question: Which category should Paper #1152910 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