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Generalization and scaling in reinforcement learning
www.cs.duke.edu/~mlittman/docs/nips-crbp.ps (Postscript - 140KB) Abstract:
In associative reinforcement learning, an environment generates input vectors, a learning system ge...
Reinforcement Learning with high-dimensional continuous actions
Technical Report WL-TR-93-1147, Wright Laboratory, Wright-Patterson Air Force Base, 1993 (HTML - ) Abstract:
Many reinforcement learning systems, such as Q-learning, or advantage updating, require that a func...
Residual Algorithms: Reinforcement Learning with Function Approximation
Armand Prieditis & Stuart Russell, eds. Machine Learning: Proceedings of the Twelfth International Conference, 9-12 July, Morgan Kaufman Publishers, San Francisco, CA (Postscript - 780 KB) Abstract:
A number of reinforcement learning algorithms have been developed that are guaranteed to converge t...
Generalization in Reinforcement Learning: Safely Approximating the Value Function
Proceedings of Neural Information Processings Systems 7, Morgan Kaufmann, January 1995 (8 pages) (compressed Postscript - 743 KB) Abstract:
A straightforward approach to the curse of dimensionality in reinforcement learning and dynamic pro...
Learning Evaluation Functions for Large Acyclic Domains
ICML-96 (Postscript - 147KB) Abstract:
Some of the most successful recent applications of reinforcement learning have used neural netw...
State abstraction in MAXQ hierarchical reinforcement learning
unpublished ( gzipped Postscript - 102Kb) Abstract:
Many researchers have explored methods for hierarchical reinforcement learning (RL) with tempora...
Reinforcement Learning With Continuous Action Values
unpublished ( gzipped Postscript - 120KB) Abstract:
The problem of reinforcement learning in the case of a continuous action set remains largely unsolv...
Vector Quantization Applied to Reinforcement Learning
Proceedings of the Fifth Workshop on RoboCup. Stockholm, Sweden. August, 1999. IJCAI'99 (Postscript - 202 KB) Abstract:
Reinforcement learning has proven to be a set of successful techniques for finding optimal policies ...
Learning policies for partially observable environments: Scaling up
Proceedings of the Twelfth International Conference on Machine Learning (Postscript - 315K) Abstract:
Partially observable Markov decision processes (POMDPs) model decision problems in which an agent t...
Reinforcement Learning for Continuous Stochastic Control Problems
Neural Information Processing Systems, 1997 (Postscript - 809KB) Abstract:
This paper is concerned with the problem of Reinforcement Learning for continuous state space and ...
A convergent Reinforcement Learning algorithm in the continuous case based on a Finite Difference method
IJCAI'1997 (compressed Postscript - 225Kb) Abstract:
In this paper, we propose a convergent Reinforcement Learning algorithm for solving optimal contr...
A Convergent Reinforcement Learning algorithm in the continuous case : the Finite-Element Reinforcement Learning
International Conference on Machine Learning, 1996 (Postscript - 197Kb) Abstract:
This paper presents a direct reinforcement learning algorithm, called Finite-Element Reinforcem...
A general convergence method for Reinforcement Learning in the continuous case
European Conference on Machine Learning, 1998 (compressed Postscript - 230Kb) Abstract:
In this paper, we propose a general method for designing convergent Reinforcement Learning algorit...
Finite-Element methods with local triangulation refinement for continuous Reinforcement Learning problems
European Conference on Machine Learning, 1997 (compressed Postscript - 283Kb) Abstract:
This paper presents a reinforcement learning algorithm for generating an adaptive control for a ...
Variable resolution discretization for high-accuracy solutions of optimal control problems
IJCAI'99 ( gzipped Postscript - 315KB) Abstract:
State abstraction is of central importance in reinforcement learning and Markov Decision Processes. ...
Gradient Descent Approaches to Neural-Net-Based Solutions of the Hamilton-Jacobi-Bellman Equation.
IJCNN'99 ( gzipped Postscript - 128KB) Abstract:
In this paper we investigate new approaches to dynamic-programming-based optimal control of contin...
Kernel-Based Reinforcement Learning
Department of Statistics, Stanford University, Technical Report No. 1999-8 (Postscript - 240 KB) Abstract:
Kernel-based methods have recently attracted increased attention in the machine learning literature...
Reinforcement Learning With Soft State Aggregation
NIPS 7 ( gzipped Postscript - )
Abstract: It is widely accepted that the use of more compact representations than lookup tables is crucial to scaling...
Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding
Advances in Neural Information Processing Systems 8, pp. 1038-1044, MIT Press (compressed Postscript - 230 KB) Abstract:
On large problems, reinforcement learning systems must use parameterized function approximat...
Scaling up average reward reinforcement learning by approximating the domain models and the value function
Proceedings of the Thirteenth International Conference on Machine Learning, pages 471-479. Morgan Kaufmann, 1996 (Postscript - ) Abstract:
Almost all the work in Average-reward Reinforcement Learning (ARL) so far has focused on table-base...
Model-based Average Reward Reinforcement Learning
Artificial Intelligenec (Postscript - 53 pages) Abstract:
Reinforcement Learning (RL) is the study of programs that improve their performance by receiving r...
Model-based Average Reward Reinforcement Learning
Artificial Intelligenec (Postscript - 53 pages) Abstract:
Reinforcement Learning (RL) is the study of programs that improve their performance by receiving re...
Feature-Based Methods for Large Scale Dynamic Programming
Machine Learning, Vol. 22, 1996, pp. 59-94. Abstract:
We develop a methodological framework and present a few different ways in which dynamic programmin...
Learning and Value Function Approximation in Complex Decision Processes
PhD Thesis (Postscript - 1691 KB) Abstract:
In principle, a wide variety of sequential decision problems -- ranging from dynamic resource alloc...
Generalization in the XCS classifier system
Genetic Programming 1998: Proceedings of the Third Annual Conference. San Francisco, CA: Morgan Kaufmann. (Postscript - 211964 bytes) Abstract:
This paper studies two changes to XCS, a classifier system in which fitness is based on prediction...