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Results 1 - 10
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645
Classifier prediction based on tile coding
- In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation
, 2006
"... This paper introduces XCSF extended with tile coding prediction: each classifier implements a tile coding approximator; the genetic algorithm is used to adapt both classifier conditions (i.e., to partition the problem) and the parameters of each approximator; thus XCSF evolves an ensemble of tile co ..."
Abstract
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Cited by 11 (0 self)
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This paper introduces XCSF extended with tile coding prediction: each classifier implements a tile coding approximator; the genetic algorithm is used to adapt both classifier conditions (i.e., to partition the problem) and the parameters of each approximator; thus XCSF evolves an ensemble of tile
Eliminating Conflict Misses for Tiled Codes
"... Tiling is a powerful compiler technique for exploiting data locality in scientific codes. However, previous research has shown conflict misses occurring due to caches with limited associativity can significantly degrade the performance of tiled codes. Two approaches for avoiding conflict misses are ..."
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Tiling is a powerful compiler technique for exploiting data locality in scientific codes. However, previous research has shown conflict misses occurring due to caches with limited associativity can significantly degrade the performance of tiled codes. Two approaches for avoiding conflict misses
Adaptive Tile Coding for Value Function Approximation
"... Reinforcement learning problems are commonly tackled by estimating the optimal value function. In many real-world problems, learning this value function requires a function approximator, which maps states to values via a parameterized function. In practice, the success of function approximators depe ..."
Abstract
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Cited by 19 (0 self)
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depends on the ability of the human designer to select an appropriate representation for the value function. This paper presents adaptive tile coding, a novel method that automates this design process for tile coding, a popular function approximator, by beginning with a simple representation with few
On Continuous-Action Q-Learning Via Tile Coding . . .
- IN UNDER REVIEW
, 2004
"... Reinforcement learning (RL) is a powerful machine-learning methodology that has an established theoretical foundation and has proven effective in a variety of small, simulated domains. There has been considerable work on applying RL, a method originally conceived for discrete state-action spaces ..."
Abstract
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Cited by 2 (0 self)
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-action spaces, to problems with continuous states. The extension of RL to allow continuous actions, on the other hand, has seen relatively little research. One proposed approach to allowing continuous actions is to represent the value function using a tile-coding function approximator. We introduce
Generating Efficient Tiled Code for Distributed Memory Machines
- Parallel Computing
, 2000
"... Abstract — Tiling can improve the performance of nested loops on distributed memory machines by exploiting coarse-grain parallelism and reducing communication overhead and frequency. Tiling calls for a compilation approach that performs first computation distribution and then data distribution, both ..."
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Cited by 11 (2 self)
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Abstract — Tiling can improve the performance of nested loops on distributed memory machines by exploiting coarse-grain parallelism and reducing communication overhead and frequency. Tiling calls for a compilation approach that performs first computation distribution and then data distribution
Function approximation via tile coding: Automating parameter choice
- of Lecture Notes in Artificial Intelligence
, 2005
"... Abstract. Reinforcement learning (RL) is a powerful abstraction of sequential decision making that has an established theoretical foundation and has proven effective in a variety of small, simulated domains. The success of RL on realworld problems with large, often continuous state and action spaces ..."
Abstract
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Cited by 28 (8 self)
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spaces hinges on effective function approximation. Of the many function approximation schemes proposed, tile coding strikes an empirically successful balance among representational power, computational cost, and ease of use and has been widely adopted in recent RL work. This paper demonstrates
ADAPTIVE TILE CODING METHODS FOR THE GENERALIZATION OF VALUE FUNCTIONS IN THE RL STATE SPACE
"... The performance of a Reinforcement Learning (RL) agent depends on the accuracy of the approximated state value functions. Tile coding (Sutton and Barto, 1998), a function approximator method, generalizes the approximated state value functions for the entire state space using a set of tile features ( ..."
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The performance of a Reinforcement Learning (RL) agent depends on the accuracy of the approximated state value functions. Tile coding (Sutton and Barto, 1998), a function approximator method, generalizes the approximated state value functions for the entire state space using a set of tile features
Fuzzy and Tile Coding Approximation Techniques for Coevolution in Reinforcement Learning
, 2005
"... This thesis investigates reinforcement learning algorithms suitable for learning in large state space problems and coevolution. In order to learn in large state spaces, the state space must be collapsed to a computationally feasible size and then generalised about. This thesis presents two new imple ..."
Abstract
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Cited by 2 (0 self)
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a practical investigation into the design of fuzzy membership functions and tile coding schemas. A critical analysis of the fuzzy algorithms to a related technique in function approximation, a coarse coding approach called tile coding is given in the context of three different simulation
A Data Locality Optimizing Algorithm
, 1991
"... This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange, reversal, skewing and tiling. The loop transformation algorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory that unifi ..."
Abstract
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Cited by 804 (16 self)
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This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange, reversal, skewing and tiling. The loop transformation algorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory
Task Space Tile Coding: In-Task and Cross-Task Generalization in Reinforcement Learning
"... Abstract. Exploiting the structure of a domain is an important prerequisite for being able to efficiently use reinforcement learning in larger state spaces. In this paper, we show how to benefit from the explicit representation of structural features in so-called structure space aspectualizable stat ..."
Abstract
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Cited by 2 (0 self)
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state spaces. We introduce task space tile coding as a mechanism to achieve generalization over states with identical structural properties. This leads to a significant improvement of learning performance. Policies learned with task space tile coding can also be applied to unknown environments sharing
Results 1 - 10
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645
