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richard-churchman
cc0a2bee21
Included open-source Accord.net library code natively to the solution given the deprecated state of that project. Accord.net will be supported inside Jube for own needs (such as upgrading to .Net 7+). Accord.net libraries modified to compile to .Net 6. Modified Accord.net libraries to better support Newtonsoft Json.net serialisation. Included annotations in the Accord.net libraries to house important dependency statistics. Serialised json can be used independently of Jube to recall a Neural Network. Removal of Binary Serializer totally as a precursor to the projects support of .Net 7+. Extensive bug fixing in crucial functions of Neural Network training and recall. While this part of the system remains in Alpha, it is substantially more fit for purpose.
108 lines
3.7 KiB
C#
108 lines
3.7 KiB
C#
// Accord Machine Learning Library
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// The Accord.NET Framework
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// http://accord-framework.net
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//
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// AForge Machine Learning Library
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// AForge.NET framework
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//
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// Copyright © Andrew Kirillov, 2007-2008
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// [email protected]
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//
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// Copyright © César Souza, 2009-2017
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// cesarsouza at gmail.com
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//
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// This library is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 2.1 of the License, or (at your option) any later version.
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//
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// This library is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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// Lesser General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License along with this library; if not, write to the Free Software
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// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
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//
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namespace Accord.MachineLearning
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{
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using System;
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using Accord.Compat;
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/// <summary>
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/// Roulette wheel exploration policy.
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/// </summary>
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///
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/// <remarks><para>The class implements roulette whell exploration policy. Acording to the policy,
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/// action <b>a</b> at state <b>s</b> is selected with the next probability:</para>
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/// <code lang="none">
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/// Q( s, a )
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/// p( s, a ) = ------------------
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/// SUM( Q( s, b ) )
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/// b
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/// </code>
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/// <para>where <b>Q(s, a)</b> is action's <b>a</b> estimation (usefulness) at state <b>s</b>.</para>
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///
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/// <para><note>The exploration policy may be applied only in cases, when action estimates (usefulness)
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/// are represented with positive value greater then 0.</note></para>
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/// </remarks>
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///
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/// <seealso cref="BoltzmannExploration"/>
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/// <seealso cref="EpsilonGreedyExploration"/>
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/// <seealso cref="TabuSearchExploration"/>
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///
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[Serializable]
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public class RouletteWheelExploration : IExplorationPolicy
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{
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// random number generator
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[NonSerialized]
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private Random rand = Accord.Math.Random.Generator.Random;
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/// <summary>
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/// Initializes a new instance of the <see cref="RouletteWheelExploration"/> class.
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/// </summary>
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///
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public RouletteWheelExploration() { }
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/// <summary>
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/// Choose an action.
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/// </summary>
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///
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/// <param name="actionEstimates">Action estimates.</param>
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///
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/// <returns>Returns selected action.</returns>
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///
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/// <remarks>The method chooses an action depending on the provided estimates. The
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/// estimates can be any sort of estimate, which values usefulness of the action
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/// (expected summary reward, discounted reward, etc).</remarks>
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///
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public int ChooseAction(double[] actionEstimates)
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{
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// actions count
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int actionsCount = actionEstimates.Length;
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// actions sum
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double sum = 0, estimateSum = 0;
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for (int i = 0; i < actionsCount; i++)
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{
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estimateSum += actionEstimates[i];
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}
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// get random number, which determines which action to choose
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double actionRandomNumber = rand.NextDouble();
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for (int i = 0; i < actionsCount; i++)
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{
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sum += actionEstimates[i] / estimateSum;
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if (actionRandomNumber <= sum)
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return i;
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}
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return actionsCount - 1;
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}
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}
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}
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