'Function approximation' diaporamas de présentation

Introduction to Radial Basis Function Networks

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Introduction to Radial Basis Function Networks

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Learning

Learning. computation making predictions choosing actions acquiring episodes statistics algorithm gradient ascent (eg of the likelihood) correlation Kalman filtering implementation flavours of Hebbian synaptic plasticity neuromodulation. Forms of Learning. supervised learning

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Data Driven Function Approximation

Data Driven Function Approximation. One link. Given X t what is the joint angle P. Y. x t. P. x. Plot1d. Plot a function that maps joint angle P to tool position X t. clear all fstr=input('input a function: x.^2+cos(x) :','s'); fx=inline(fstr); range=2*pi; x=linspace(-range,range);

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Reinforcement Learning Generalization and Function Approximation

Reinforcement Learning Generalization and Function Approximation. Subramanian Ramamoorthy School of Informatics 28 February, 2012. Function Approximation in RL: Why?. Tabular V p : What should we do when the state spaces are very large and this becomes computationally expensive?.

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Decision Making in Intelligent Systems Lecture 9

Decision Making in Intelligent Systems Lecture 9. BSc course Kunstmatige Intelligentie 2008 Bram Bakker Intelligent Systems Lab Amsterdam Informatics Institute Universiteit van Amsterdam bram@science.uva.nl. Overview of this lecture. Last lecture!

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Artificial Neural Networks - Introduction -

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CS 188: Artificial Intelligence Fall 2007

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Decision Making Under Uncertainty Lec #9: Approximate Value Function

Decision Making Under Uncertainty Lec #9: Approximate Value Function. UIUC CS 598: Section EA Professor: Eyal Amir Spring Semester 2006. Some slides by Jeremy Wyatt (U Birmingham), Ron Parr (Duke), Eduardo Alonso, and Craig Boutilier (Toronto). So Far. MDPs, RL Transition matrix (nxn)

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Chapter 8: Generalization and Function Approximation

Chapter 8: Generalization and Function Approximation. Look at how experience with a limited part of the state set be used to produce good behavior over a much larger part. Overview of function approximation (FA) methods and how they can be adapted to RL. Objectives of this chapter:.

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RL for Large State Spaces: Value Function Approximation

RL for Large State Spaces: Value Function Approximation. Alan Fern. * Based in part on slides by Daniel Weld. Large State Spaces. When a problem has a large state space we can not longer represent the V or Q functions as explicit tables Even if we had enough memory

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Data driven function approximation

Data driven function approximation. Plot1d. Plot an arbitrary one-dimensional function. clear all fstr=input('input a function: x.^2+cos(x) :','s'); fx=inline(fstr); range=2*pi; x=linspace(-range,range); y=fx(x); max_y=max(abs(y)); plot(x,y/max_y); hold on;. Sampling.

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Hybrid Agent-Based Modeling: Architectures,Analyses and Applications (Stage One)

Hybrid Agent-Based Modeling: Architectures,Analyses and Applications (Stage One). Li, Hailin. Outline. I ntroduction Least-Squares Method for Reinforcement Learning Evolutionary Algorithms For RL Problem (in progress)

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Function approximation

Function approximation. Nisheeth 31 st January 2019. The state space problem in model-free RL. Number of states quickly becomes too large Even for trivial applications Learning becomes too dependent on right choice of exploration parameters

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Lecture 5I Function Approximation

Lecture 5I Function Approximation. Data driven function approximation Projective hyper-tangent function. Interpolation vs approximation. Polynomial interpolation Constraints: f(x i )=y i for all I Polynomial approximation Minimization of mean square error Non-polynomial approximation

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Soft Computing

Soft Computing. Lecture 21 Review of using of NN for solving of real tasks. Autopilot airplane. LoFLYTE (Low-Observable Flight Test Experiment) is developed for NASA and USA Air Force by Accurate Automation Corp., Chattanooga, TN. Speed is 4-5 M

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A Constrained Optimization Approach For Function Approximations and System Identification

A Constrained Optimization Approach For Function Approximations and System Identification. Gianluca Di Muro and Silvia Ferrari Lab for Intelligent Systems and Controls (LISC) Duke University WCCI 2008 June 4 2008. Motivation.

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KIPA Game Engine Seminars

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TRAIN

TRAIN. Y_TRAIN. X. X_TRAIN: 114*2. D=cross_distance(X,centers) H=exp(-D/200)*a Y_HAT=H>0.5. centers=my_kmeans[X_TRAIN,3,]. D=cross_distance(X_TRAIN,centers). a=pinv(exp(-D))*Y_TRAIN. Y_HAT. TEST. X. D= cross_distance ( X,centers ). centers. H=exp(-D/200)*a). a. Y_HAT=H>0.5. Y_HAT.

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