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github | qihongl/mathCognition_PDP_RL-master | computeRwd.m | .m | mathCognition_PDP_RL-master/sim23.0_count/computeRwd.m | 2,905 | utf_8 | d4cae137f57b8cafdbb80b73dbc972c3 | function Rwd = computeRwd()
%% this function controls the reward policy
global w p h a;
w.actionCorrect = true;
% if there is remaining items
if targetRemain()
if a.action == p.mvRange +1 % saying "done"
Rwd = p.r.smallNeg;
w.errors = w.errors + 1;
w.done = true;
elseif a.action == p... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/sim21.3_hidden_targ/runAgent.m | 1,116 | utf_8 | fd6b1cdf0b65e1968929bf8e59d43f6c | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p mode;
%% initialize the state
initState();
updateState();
computeAnswer(); % compute the true 'answers'
%% train the model once
t = 0;
indices = zeros(1,p.maxIter);
while ~(w.done) && t < p.maxIter
%% choose act... |
github | qihongl/mathCognition_PDP_RL-master | trainOne.m | .m | mathCognition_PDP_RL-master/sim21.3_hidden_targ/trainOne.m | 764 | utf_8 | da63d60804f0250e07bd5b277c73438f | % just testing, a short cut for running the model
function record = trainOne(epoch, seed)
clear global
if nargin == 0
epoch = 10000;
seed = randi(99);
% seed = 66
end
%% run the simulation
global p
record = trainAgent(epoch, seed);
% save the simulation results
saveDirName = getSaveDir();
save([saveDir... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/sim21.3_hidden_targ/updateWeights.m | 3,224 | utf_8 | 6ac0e5327b5d806faf5a2738ef99bd08 | % written by professor Jay McClelland
function [ ] = updateWeights()
% this function controls:
% 1. the reward policy
% 2. the weight update
% 3. activate the "teaching"
global p a w buffer;
%% compute the reward values according to the reward policy
% compute the true reward at this time step
a.curRwd = compu... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/sim21.3_hidden_targ/showState.m | 2,369 | utf_8 | c21f87f660c0fca0cff629e57b109e9f | % written by professor Jay McClelland
function [ ] = showState( )
global p w d a;
%% plot current and expected rewards over time
axes(d.rwd);
plot(w.rS.time,a.curRwd,'-b*'); hold on;
plot(w.rS.time,a.expRwd,'-r*');
legend({'current reward', 'esimtated reward'},...
'Location','northwest', 'fontsize', d.F... |
github | qihongl/mathCognition_PDP_RL-master | initParams.m | .m | mathCognition_PDP_RL-master/sim21.3_hidden_targ/initParams.m | 4,193 | utf_8 | a6d1469d87ffef51350b0502be9a6178 | % written by professor Jay McClelland
function [] = initParams(epoch)
% This program initialize and preallocate the parameters needed for the
% model. This should be executed before the simulations.
global p a buffer
%% teaching strategy
% if teaching style is specified here, then trainGroup will use this
% va... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/sim21.3_hidden_targ/updateState.m | 1,137 | utf_8 | b7f489fdff68b16f52c2b5336d86ef59 | % written by professor Jay McClelland
function [ ] = updateState()
%this function uses the real state to update the internal state
%after Act is called to execute the hand or eye movement action
global w h p a;
%% compute the relative locations
% the relative locations of eye and hand
w.vS.eyePos = 0;
w.vS.... |
github | qihongl/mathCognition_PDP_RL-master | initState.m | .m | mathCognition_PDP_RL-master/sim21.3_hidden_targ/initState.m | 1,609 | utf_8 | 31fd190a63510d233a4f7fadf964e080 | % written by professor Jay McClelland
function [ ] = initState( )
global a w h p mode;
%realState is characterized by the position of a target to touch,
%position of eye, and position of hand 1-d space
%viewedState is the input I have given that my eye and hand are
%at particular positions w.r.t. the realSt... |
github | qihongl/mathCognition_PDP_RL-master | updateBuffer.m | .m | mathCognition_PDP_RL-master/sim21.3_hidden_targ/updateBuffer.m | 1,427 | utf_8 | d34f7ab9c772aa1e53a37f5420bf6604 | %% update the memory buffer using the current experience
function [ ] = updateBuffer()
global p a w buffer;
memoryIdx = min(a.bufferUsage+1, p.bufferSize);
if a.bufferUsage+1 <= p.bufferSize
saveCurrentExperience(memoryIdx)
else
% delete the 1st experience in the buffer
buffer(1) = [];
% preallocate a... |
github | qihongl/mathCognition_PDP_RL-master | trainAgent.m | .m | mathCognition_PDP_RL-master/sim21.3_hidden_targ/trainAgent.m | 1,906 | utf_8 | 66fb53004cf839722aceb5326c612ef8 | %% Trains the network n trials
% written by professor Jay McClelland
function [record] = trainAgent(epoch, seed)
%% initialization
% initialize parameters
global p a w mode;
initParams(epoch);
p.seed = seed;
rng(seed)
% preallocate
record.wts = cell(1,epoch / p.saveWtsInterval+1);
s.steps = nan(1,epoch);
... |
github | qihongl/mathCognition_PDP_RL-master | computeExpectedReward.m | .m | mathCognition_PDP_RL-master/sim21.3_hidden_targ/computeExpectedReward.m | 840 | utf_8 | 4bc9236135238e3ac1ded9a2234e8990 | %% compute the expected reward with the Q learning rule
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% given: act_next predicted Q value
% s_cur current state (input)
% r_cur current actual reward
% taskDone if the task is terminated
% return: dfRw... |
github | qihongl/mathCognition_PDP_RL-master | xorModel.m | .m | mathCognition_PDP_RL-master/demo_nn/xorModel.m | 2,382 | utf_8 | d9f345d48799c58909cf91f218172c57 | function [r] = xorModel()
% XOR input for x1 and x2
input = [0 0; 0 1; 1 0; 1 1];
% Desired output of XOR
output = [0;1;1;0];
% Initialize the bias
bias = [-1 -1 -1];
% Learning coefficient
p.lrate = 0.7;
% Number of learning p.epochs
p.epochs = 100;
% Calculate weights randomly using seed.
rand('state',sum... |
github | qihongl/mathCognition_PDP_RL-master | nnCostFunction.m | .m | mathCognition_PDP_RL-master/demo_nn/nnCostFunction.m | 2,476 | utf_8 | 75b1b1dffb03e5bf83082b319af6104c | %NNCOSTFUNCTION Implements the neural network cost function for a two layer
%neural network which performs classification
% [J grad] = NNCOSTFUNCTON(nn_params, hidden_layer_size, num_labels, ...
% X, y, lambda) computes the cost and gradient of the neural network. The
% parameters for the neural network are "unro... |
github | qihongl/mathCognition_PDP_RL-master | submit.m | .m | mathCognition_PDP_RL-master/demo_nn/submit.m | 17,129 | utf_8 | 90fe6bfd432e59c0a145fb1240d34ce5 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | qihongl/mathCognition_PDP_RL-master | submitWeb.m | .m | mathCognition_PDP_RL-master/demo_nn/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/sim21.0_replay/runAgent.m | 907 | utf_8 | ae308f723ec1df810d6ad2f5f3df1bc7 | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p mode;
%% initialize the state
initState();
updateState();
computeAnswer(); % compute the true 'answers'
%% train the model once
t = 0;
indices = zeros(1,p.maxIter);
while ~(w.done) && t < p.maxIter
%% choose act... |
github | qihongl/mathCognition_PDP_RL-master | trainOne.m | .m | mathCognition_PDP_RL-master/sim21.0_replay/trainOne.m | 763 | utf_8 | baf2c9aac6e263a6101d0dce66c2996a | % just testing, a short cut for running the model
function record = trainOne(epoch, seed)
clear global
if nargin == 0
epoch = 1000;
seed = randi(99);
% seed = 66
end
%% run the simulation
global p
record = trainAgent(epoch, seed);
% save the simulation results
saveDirName = getSaveDir();
save([saveDirN... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/sim21.0_replay/updateWeights.m | 2,844 | utf_8 | 5c90cbc7f20dbcd6a3d39fa03b26e417 | % written by professor Jay McClelland
function [ ] = updateWeights()
% this function controls:
% 1. the reward policy
% 2. the weight update
% 3. activate the "teaching"
global p a w buffer;
%% compute the reward values according to the reward policy
% compute the true reward at this time step
a.curRwd = compu... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/sim21.0_replay/showState.m | 2,369 | utf_8 | c21f87f660c0fca0cff629e57b109e9f | % written by professor Jay McClelland
function [ ] = showState( )
global p w d a;
%% plot current and expected rewards over time
axes(d.rwd);
plot(w.rS.time,a.curRwd,'-b*'); hold on;
plot(w.rS.time,a.expRwd,'-r*');
legend({'current reward', 'esimtated reward'},...
'Location','northwest', 'fontsize', d.F... |
github | qihongl/mathCognition_PDP_RL-master | initParams.m | .m | mathCognition_PDP_RL-master/sim21.0_replay/initParams.m | 3,828 | utf_8 | 10b38cae3dd90afd0b9ddcdf6c8eeceb | % written by professor Jay McClelland
function [] = initParams(epoch)
% This program initialize and preallocate the parameters needed for the
% model. This should be executed before the simulations.
global p a buffer
%% teaching strategy
% if teaching style is specified here, then trainGroup will use this
% va... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/sim21.0_replay/updateState.m | 1,137 | utf_8 | b7f489fdff68b16f52c2b5336d86ef59 | % written by professor Jay McClelland
function [ ] = updateState()
%this function uses the real state to update the internal state
%after Act is called to execute the hand or eye movement action
global w h p a;
%% compute the relative locations
% the relative locations of eye and hand
w.vS.eyePos = 0;
w.vS.... |
github | qihongl/mathCognition_PDP_RL-master | initState.m | .m | mathCognition_PDP_RL-master/sim21.0_replay/initState.m | 1,609 | utf_8 | 31fd190a63510d233a4f7fadf964e080 | % written by professor Jay McClelland
function [ ] = initState( )
global a w h p mode;
%realState is characterized by the position of a target to touch,
%position of eye, and position of hand 1-d space
%viewedState is the input I have given that my eye and hand are
%at particular positions w.r.t. the realSt... |
github | qihongl/mathCognition_PDP_RL-master | updateBuffer.m | .m | mathCognition_PDP_RL-master/sim21.0_replay/updateBuffer.m | 1,427 | utf_8 | d34f7ab9c772aa1e53a37f5420bf6604 | %% update the memory buffer using the current experience
function [ ] = updateBuffer()
global p a w buffer;
memoryIdx = min(a.bufferUsage+1, p.bufferSize);
if a.bufferUsage+1 <= p.bufferSize
saveCurrentExperience(memoryIdx)
else
% delete the 1st experience in the buffer
buffer(1) = [];
% preallocate a... |
github | qihongl/mathCognition_PDP_RL-master | trainAgent.m | .m | mathCognition_PDP_RL-master/sim21.0_replay/trainAgent.m | 1,902 | utf_8 | b4895d68c7528dd8540d75068bcfdd51 | %% Trains the network n trials
% written by professor Jay McClelland
function [record] = trainAgent(epoch, seed)
%% initialization
% initialize parameters
global p a w mode;
initParams(epoch);
p.seed = seed;
rng(seed)
% preallocate
record.wts = cell(1,epoch / p.saveWtsInterval+1);
s.steps = nan(1,epoch);
... |
github | qihongl/mathCognition_PDP_RL-master | computeExpectedReward.m | .m | mathCognition_PDP_RL-master/sim21.0_replay/computeExpectedReward.m | 795 | utf_8 | bf22d95e531588e0a74e43c7ac747fc9 | %% compute the expected reward with the Q learning rule
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% given: act_next predicted Q value
% s_cur current state (input)
% r_cur current actual reward
% taskDone if the task is terminated
% return: d... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/sim20.2_scaleRwd/runAgent.m | 935 | utf_8 | 63e17518c01b818647117d4c4ed5b343 | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p mode;
%% initialize the state
initState();
updateState();
computeAnswer(); % compute the true 'answers'
%% training the model once
i = 0;
indices = zeros(1,p.maxIter);
while ~(w.done) && i < p.maxIter
%% choose ... |
github | qihongl/mathCognition_PDP_RL-master | trainOne.m | .m | mathCognition_PDP_RL-master/sim20.2_scaleRwd/trainOne.m | 599 | utf_8 | 4926604e370b5eb40f082b7036b14f53 | % just testing, a short cut for running the model
function record = trainOne(epoch, seed)
if nargin == 0
epoch = 10000;
seed = randi(99);
end
%% run the simulation
record = trainAgent(epoch, seed);
%% save the simulation results
saveDirName = getSaveDir();
save([saveDirName '/' 'record'],'record');
save('reco... |
github | qihongl/mathCognition_PDP_RL-master | showWeights.m | .m | mathCognition_PDP_RL-master/sim20.2_scaleRwd/showWeights.m | 583 | utf_8 | a5170d34eef2535437720fe5e1a446a5 | function [ ] = showWeights( )
% plot the weights of the model
global p d a;
% plot weights around fovea
axes(d.heatWts)
% imagesc(-p.eyeRad:p.eyeRad,-p.mvRad:p.mvRad+1, a.wts)
visualizeWeightsMatrix(a.wts)
title(d.heatWts, 'Weights: visual -> action', 'fontsize', d.FONTSIZE)
xlabel(d.heatWts, 'Visual input layer', 'fon... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/sim20.2_scaleRwd/updateWeights.m | 1,063 | utf_8 | 00b5be7d5e214190466fa20d91795aea | % written by professor Jay McClelland
function [ ] = updateWeights()
% this function controls:
% 1. the reward policy
% 2. the weight update
% 3. activate the "teaching"
global p a w;
%% compute the reward values according to the reward policy
a.curRwd = computeRwd();
% if reward == -1, do sth diff
a.act_next... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/sim20.2_scaleRwd/showState.m | 2,369 | utf_8 | c21f87f660c0fca0cff629e57b109e9f | % written by professor Jay McClelland
function [ ] = showState( )
global p w d a;
%% plot current and expected rewards over time
axes(d.rwd);
plot(w.rS.time,a.curRwd,'-b*'); hold on;
plot(w.rS.time,a.expRwd,'-r*');
legend({'current reward', 'esimtated reward'},...
'Location','northwest', 'fontsize', d.F... |
github | qihongl/mathCognition_PDP_RL-master | initParams.m | .m | mathCognition_PDP_RL-master/sim20.2_scaleRwd/initParams.m | 2,284 | utf_8 | fb61794ed6079bc1bcc6f9fb35ec1de7 | % written by professor Jay McClelland
function [] = initParams(epoch)
% This program initialize and preallocate the parameters needed for the
% model. This should be executed before the simulations.
global p a
% p.teachingStyle = 4;
% 1 = final reward only
% 2 = intermediate reward
% 3 = final reward only + t... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/sim20.2_scaleRwd/updateState.m | 1,137 | utf_8 | b7f489fdff68b16f52c2b5336d86ef59 | % written by professor Jay McClelland
function [ ] = updateState()
%this function uses the real state to update the internal state
%after Act is called to execute the hand or eye movement action
global w h p a;
%% compute the relative locations
% the relative locations of eye and hand
w.vS.eyePos = 0;
w.vS.... |
github | qihongl/mathCognition_PDP_RL-master | initState.m | .m | mathCognition_PDP_RL-master/sim20.2_scaleRwd/initState.m | 1,665 | utf_8 | 4089ab3033ac759bf8c9857f7d1932f2 | % written by professor Jay McClelland
function [ ] = initState( )
global a w h p mode;
%realState is characterized by the position of a target to touch,
%position of eye, and position of hand 1-d space
%viewedState is the input I have given that my eye and hand are
%at particular positions w.r.t. the realSt... |
github | qihongl/mathCognition_PDP_RL-master | trainAgent.m | .m | mathCognition_PDP_RL-master/sim20.2_scaleRwd/trainAgent.m | 1,501 | utf_8 | 704f98828c45de576b681eb325399789 | %% Trains the network n trials
% written by professor Jay McClelland
function [record] = trainAgent(epoch, seed)
%% initialization
% initialize parameters
global p a w mode;
initParams(epoch);
p.seed = seed;
rng(seed)
% preallocate
record.a = cell(1,epoch);
s.steps = nan(1,epoch);
s.indices = cell(1,epoch... |
github | qihongl/mathCognition_PDP_RL-master | quiz.m | .m | mathCognition_PDP_RL-master/testModel/quiz.m | 862 | utf_8 | dbf78073f269b978120d7c8fef51a76a | %% This is a quiz for the model
% It come up with N questions and record the performance of the model
function finalScore = quiz(showPlot)
if nargin == 0
showPlot = false;
end
%% Parameters
dfNumQs = 50;
dfNumPlots = 1;
%% Get data from the current directory
filename = 'record.mat';
datapath = [pwd '/' filename];... |
github | qihongl/mathCognition_PDP_RL-master | testModel.m | .m | mathCognition_PDP_RL-master/testModel/testModel.m | 1,269 | utf_8 | dd32d0c9a12ea4543bd2f59f5ab242ab | %% An little quiz for the model
% this function takes a set of trained weights and parameters, come up with
% a counting question, let the model do the counting, and records its
% performance! So it is in some sense a little quiz for the model.
function [score] = testModel(showPlot, nItem)
global p a w mode;
setTestMo... |
github | qihongl/mathCognition_PDP_RL-master | evaluateModel_quiz.m | .m | mathCognition_PDP_RL-master/testModel/evaluateModel_quiz.m | 5,960 | utf_8 | 122331f697e460f401dfcda6fd889f10 | function [finalScore] = evaluateModel_quiz( score, numQs, showResults)
if nargin < 3
showResults = true;
end
% This function assume the data is the quiz data
% Namely, the input data can be converted to "cardinality X numQs matrix"
global p;
if numel(score) ~= p.maxItems * numQs
error('Size of the data is ... |
github | qihongl/mathCognition_PDP_RL-master | averagingWts.m | .m | mathCognition_PDP_RL-master/testModel/averagingWts.m | 907 | utf_8 | 1a61b1cc8274b64f6db55a545f6e70e8 | % take average of the weights
function averagingWts()
global p;
FONTSIZE = 14;
NSUBJ = 20;
NAME = 'record';
%% get the weights
wts = cell(NSUBJ,1);
for i = 1 : NSUBJ
filename = sprintf('%s%.2d.mat', NAME, i);
load(filename);
wts{i} = record.a.wts;
end
p = record.p;
%% averaging the weights
meanWts = zeros... |
github | qihongl/mathCognition_PDP_RL-master | getFilenames.m | .m | mathCognition_PDP_RL-master/testModel/getFilenames.m | 325 | utf_8 | d37407a050a3e5b0e8b18787e1159998 | %% get file names for all file, so that I can target them
function filenames = getFilenames(filename, nSubj)
% specify the format of the file
format = '.mat';
% create all file name
filenames = cell(nSubj,1);
% create all the file name
for n = 1 : nSubj
filenames{n} = sprintf(['%s%.2d' format], filename, n);
end... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/testModel/old/updateWeights.m | 830 | utf_8 | abe33ad8c85510746a004a1a18f50d60 | % written by professor Jay McClelland
function [ ] = updateWeights( )
global a w p;
%% Assign the reward values
% if the hand is touching a item, and that item is untouched before
if any(w.rS.handPos == w.rS.targPos) && (w.rS.targRemain(w.rS.handPos == w.rS.targPos) == true)
w.rS.targRemain(w.rS.targPos =... |
github | qihongl/mathCognition_PDP_RL-master | selectAction.m | .m | mathCognition_PDP_RL-master/testModel/old/selectAction.m | 778 | utf_8 | 26808e8e7b2ba2af3c3f796360d7b127 | % written by professor Jay McClelland
function [] = selectAction( )
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
global w a p;
%% compute the normalized activation
% a.net = a.wts * w.vS.visInput';
% scnet = p.smgain*a.net;
% a.act = exp(scnet)/sum(exp(scnet));
%% compute ... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/testModel/old/showState.m | 710 | utf_8 | 868e49a6bc76a8f2db42ea01f6091847 | % written by professor Jay McClelland
function [ ] = showState( )
global p w d a;
axes(d.rax);
plot(w.rS.time,a.Rwd,'-*'); hold on;
ylim(d.rax,[-0.1 1.1]); xlim(d.rax,[-0.25,w.rS.time+0.25]);
t = w.rS.time;
% plot the history of eye and hand positions
axes(d.hax);
plot(d.hax,[-p.spRad p.spRad],[t t]); hold... |
github | qihongl/mathCognition_PDP_RL-master | initParamsEtc.m | .m | mathCognition_PDP_RL-master/testModel/old/initParamsEtc.m | 1,091 | utf_8 | 69772c75f6cc6fe51c89b61450d7ce3f | % written by professor Jay McClelland
function [] = initParamsEtc( )
% This program initialize and preallocate the parameters needed for the
% model. This should be executed before the simulations.
global p d a
%% modeling parameters
p.wf = .2; % noise magnitude
p.lrate = .1; % learning rate
... |
github | qihongl/mathCognition_PDP_RL-master | choose.m | .m | mathCognition_PDP_RL-master/testModel/old/choose.m | 337 | utf_8 | 54e0f53b425d2927e123310d920c7989 | % written by professor Jay McClelland
function [ choice ] = choose(strengths)
%choose one of n alternatives according to it's strength
v = rand; % a number between 0 and 1
nstr = strengths/sum(strengths); %normalize strengths
cstr = cumsum(nstr); %get top edges of bins
choice = find(cstr>v,1); %returns the bin v ... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/testModel/old/updateState.m | 1,025 | utf_8 | 17a84148d48a20188df31db95a4f09ab | % written by professor Jay McClelland
function [ ] = updateState()
%this function uses the real state to update the internal state
%after Act is called to execute the hand or eye movement action
global w a d h p;
%% compute the relative locations
% the relative locations of eye and hand
w.vS.eyePos = 0;
w.v... |
github | qihongl/mathCognition_PDP_RL-master | initState.m | .m | mathCognition_PDP_RL-master/testModel/old/initState.m | 1,438 | utf_8 | 7d557634047b25efd393a78b171c94da | % written by professor Jay McClelland
function [ ] = initState( )
global a w d h p;
%realState is characterized by the position of a target to touch,
%position of eye, and position of hand
%in a 101-pixel one-d space
%viewedState is the input I have given that my eye and hand a... |
github | qihongl/mathCognition_PDP_RL-master | Act.m | .m | mathCognition_PDP_RL-master/testModel/old/Act.m | 404 | utf_8 | c8ce5a2f3bcdf3fa4195dafad94f8e76 | % written by professor Jay McClelland
function [ ] = Act( )
% here we act according to the action selected
% by selectAction
global w p;
%% perform the actions
% update the real locations of hand and eye
w.rS.handPos = w.rS.handPos + w.out.handStep;
w.rS.eyePos = w.rS.eyePos + w.out.eyeStep;
w.rS.td = 1; ... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/sim20.1_simplify/runAgent.m | 935 | utf_8 | 63e17518c01b818647117d4c4ed5b343 | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p mode;
%% initialize the state
initState();
updateState();
computeAnswer(); % compute the true 'answers'
%% training the model once
i = 0;
indices = zeros(1,p.maxIter);
while ~(w.done) && i < p.maxIter
%% choose ... |
github | qihongl/mathCognition_PDP_RL-master | trainOne.m | .m | mathCognition_PDP_RL-master/sim20.1_simplify/trainOne.m | 599 | utf_8 | 4926604e370b5eb40f082b7036b14f53 | % just testing, a short cut for running the model
function record = trainOne(epoch, seed)
if nargin == 0
epoch = 10000;
seed = randi(99);
end
%% run the simulation
record = trainAgent(epoch, seed);
%% save the simulation results
saveDirName = getSaveDir();
save([saveDirName '/' 'record'],'record');
save('reco... |
github | qihongl/mathCognition_PDP_RL-master | showWeights.m | .m | mathCognition_PDP_RL-master/sim20.1_simplify/showWeights.m | 583 | utf_8 | a5170d34eef2535437720fe5e1a446a5 | function [ ] = showWeights( )
% plot the weights of the model
global p d a;
% plot weights around fovea
axes(d.heatWts)
% imagesc(-p.eyeRad:p.eyeRad,-p.mvRad:p.mvRad+1, a.wts)
visualizeWeightsMatrix(a.wts)
title(d.heatWts, 'Weights: visual -> action', 'fontsize', d.FONTSIZE)
xlabel(d.heatWts, 'Visual input layer', 'fon... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/sim20.1_simplify/updateWeights.m | 789 | utf_8 | 3669dc6b40fe43a73900b9e88a5abf88 | % written by professor Jay McClelland
function [ ] = updateWeights()
% this function controls:
% 1. the reward policy
% 2. the weight update
% 3. activate the "teaching"
global p a w;
%% compute the reward values according to the reward policy
a.curRwd = computeRwd();
% if reward == -1, do sth diff
a.act_next... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/sim20.1_simplify/showState.m | 2,369 | utf_8 | c21f87f660c0fca0cff629e57b109e9f | % written by professor Jay McClelland
function [ ] = showState( )
global p w d a;
%% plot current and expected rewards over time
axes(d.rwd);
plot(w.rS.time,a.curRwd,'-b*'); hold on;
plot(w.rS.time,a.expRwd,'-r*');
legend({'current reward', 'esimtated reward'},...
'Location','northwest', 'fontsize', d.F... |
github | qihongl/mathCognition_PDP_RL-master | initParams.m | .m | mathCognition_PDP_RL-master/sim20.1_simplify/initParams.m | 2,308 | utf_8 | a404e821f49168803c171ca3ba039bb7 | % written by professor Jay McClelland
function [] = initParams(epoch)
% This program initialize and preallocate the parameters needed for the
% model. This should be executed before the simulations.
global p a
% p.teachingStyle = 4;
% 1 = final reward only
% 2 = intermediate reward
% 3 = final reward only + t... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/sim20.1_simplify/updateState.m | 1,137 | utf_8 | b7f489fdff68b16f52c2b5336d86ef59 | % written by professor Jay McClelland
function [ ] = updateState()
%this function uses the real state to update the internal state
%after Act is called to execute the hand or eye movement action
global w h p a;
%% compute the relative locations
% the relative locations of eye and hand
w.vS.eyePos = 0;
w.vS.... |
github | qihongl/mathCognition_PDP_RL-master | initState.m | .m | mathCognition_PDP_RL-master/sim20.1_simplify/initState.m | 1,609 | utf_8 | 31fd190a63510d233a4f7fadf964e080 | % written by professor Jay McClelland
function [ ] = initState( )
global a w h p mode;
%realState is characterized by the position of a target to touch,
%position of eye, and position of hand 1-d space
%viewedState is the input I have given that my eye and hand are
%at particular positions w.r.t. the realSt... |
github | qihongl/mathCognition_PDP_RL-master | trainAgent.m | .m | mathCognition_PDP_RL-master/sim20.1_simplify/trainAgent.m | 1,836 | utf_8 | f872b2318f4a2b603a383a725db112c5 | %% Trains the network n trials
% written by professor Jay McClelland
function [record] = trainAgent(epoch, seed)
%% initialization
% initialize parameters
global p a w mode;
initParams(epoch);
p.seed = seed;
rng(seed)
% preallocate
record.wts = cell(1,epoch/p.saveWtsInterval+1);
s.steps = nan(1,epoch);
... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/sim21.2_targetNet/runAgent.m | 1,097 | utf_8 | 0a7909b39dd7dae2f4ed5cc01985a057 | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p mode;
%% initialize the state
initState();
updateState();
computeAnswer(); % compute the true 'answers'
%% train the model once
t = 0;
indices = zeros(1,p.maxIter);
while ~(w.done) && t < p.maxIter
%% choose act... |
github | qihongl/mathCognition_PDP_RL-master | trainOne.m | .m | mathCognition_PDP_RL-master/sim21.2_targetNet/trainOne.m | 761 | utf_8 | baa37569ccd3dbfe38bfa11d25118de2 | % just testing, a short cut for running the model
function record = trainOne(epoch, seed)
clear global
if nargin == 0
epoch = 1000;
seed = randi(99);
seed = 66
end
%% run the simulation
global p
record = trainAgent(epoch, seed);
% save the simulation results
saveDirName = getSaveDir();
save([saveDirNam... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/sim21.2_targetNet/updateWeights.m | 2,572 | utf_8 | 24b42412fb538cfefef1b124761fbbad | % written by professor Jay McClelland
function [ ] = updateWeights()
% this function controls:
% 1. the reward policy
% 2. the weight update
% 3. activate the "teaching"
global p a w buffer;
%% compute the reward values according to the reward policy
% compute the true reward at this time step
%% experienc... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/sim21.2_targetNet/showState.m | 2,369 | utf_8 | c21f87f660c0fca0cff629e57b109e9f | % written by professor Jay McClelland
function [ ] = showState( )
global p w d a;
%% plot current and expected rewards over time
axes(d.rwd);
plot(w.rS.time,a.curRwd,'-b*'); hold on;
plot(w.rS.time,a.expRwd,'-r*');
legend({'current reward', 'esimtated reward'},...
'Location','northwest', 'fontsize', d.F... |
github | qihongl/mathCognition_PDP_RL-master | initParams.m | .m | mathCognition_PDP_RL-master/sim21.2_targetNet/initParams.m | 4,034 | utf_8 | edc8c0b7df81fb09e9b8ff2ff477eb2a | % written by professor Jay McClelland
function [] = initParams(epoch)
% This program initialize and preallocate the parameters needed for the
% model. This should be executed before the simulations.
global p a buffer
%% teaching strategy
% if teaching style is specified here, then trainGroup will use this
% va... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/sim21.2_targetNet/updateState.m | 1,137 | utf_8 | b7f489fdff68b16f52c2b5336d86ef59 | % written by professor Jay McClelland
function [ ] = updateState()
%this function uses the real state to update the internal state
%after Act is called to execute the hand or eye movement action
global w h p a;
%% compute the relative locations
% the relative locations of eye and hand
w.vS.eyePos = 0;
w.vS.... |
github | qihongl/mathCognition_PDP_RL-master | initState.m | .m | mathCognition_PDP_RL-master/sim21.2_targetNet/initState.m | 1,609 | utf_8 | 31fd190a63510d233a4f7fadf964e080 | % written by professor Jay McClelland
function [ ] = initState( )
global a w h p mode;
%realState is characterized by the position of a target to touch,
%position of eye, and position of hand 1-d space
%viewedState is the input I have given that my eye and hand are
%at particular positions w.r.t. the realSt... |
github | qihongl/mathCognition_PDP_RL-master | updateBuffer.m | .m | mathCognition_PDP_RL-master/sim21.2_targetNet/updateBuffer.m | 1,429 | utf_8 | 0f703ddb26cf30fa6a992dfeb79fcde6 | %% update the memory buffer using the current experience
function [ ] = updateBuffer()
global p a w buffer;
memoryIdx = min(a.bufferUsage+1, p.bufferSize);
if a.bufferUsage+1 <= p.bufferSize
saveCurrentExperience(memoryIdx)
else
% delete the 1st experience in the buffer
buffer(1) = [];
% preallocate a... |
github | qihongl/mathCognition_PDP_RL-master | trainAgent.m | .m | mathCognition_PDP_RL-master/sim21.2_targetNet/trainAgent.m | 2,003 | utf_8 | 3a7ed21f730accecdb1f79fcbcaf4da5 | %% Trains the network n trials
% written by professor Jay McClelland
function [record] = trainAgent(epoch, seed)
%% initialization
% initialize parameters
global p a w mode;
initParams(epoch);
p.seed = seed;
rng(seed)
% preallocate
record.wts = cell(1,epoch / p.saveWtsInterval+1);
s.steps = nan(1,epoch);
... |
github | qihongl/mathCognition_PDP_RL-master | computeExpectedReward.m | .m | mathCognition_PDP_RL-master/sim21.2_targetNet/computeExpectedReward.m | 916 | utf_8 | ccb6ec148b459c76de3fda938957ce9b | %% compute the expected reward with the Q learning rule
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% given: act_next predicted Q value
% s_cur current state (input)
% r_cur current actual reward
% taskDone if the task is terminated
% return: d... |
github | qihongl/mathCognition_PDP_RL-master | computeRwd.m | .m | mathCognition_PDP_RL-master/sim21.2_targetNet/computeRwd.m | 2,738 | utf_8 | 4db26f71f0f52e8ae38d3eef7f261097 | function Rwd = computeRwd()
%% this function controls the reward policy
global w p h a;
w.actionCorrect = true;
% if there is remaining items
if targetRemain()
if a.choice == p.mvRange +1 % saying "done"
Rwd = p.r.smallNeg;
w.errors = w.errors + 1;
w.done = true;
elseif a.choice == p... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/sim21.1_hiddenUnits/runAgent.m | 907 | utf_8 | ae308f723ec1df810d6ad2f5f3df1bc7 | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p mode;
%% initialize the state
initState();
updateState();
computeAnswer(); % compute the true 'answers'
%% train the model once
t = 0;
indices = zeros(1,p.maxIter);
while ~(w.done) && t < p.maxIter
%% choose act... |
github | qihongl/mathCognition_PDP_RL-master | trainOne.m | .m | mathCognition_PDP_RL-master/sim21.1_hiddenUnits/trainOne.m | 764 | utf_8 | da63d60804f0250e07bd5b277c73438f | % just testing, a short cut for running the model
function record = trainOne(epoch, seed)
clear global
if nargin == 0
epoch = 10000;
seed = randi(99);
% seed = 66
end
%% run the simulation
global p
record = trainAgent(epoch, seed);
% save the simulation results
saveDirName = getSaveDir();
save([saveDir... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/sim21.1_hiddenUnits/updateWeights.m | 3,029 | utf_8 | e2b08dbb44c4da518c2e64c92fe81047 | % written by professor Jay McClelland
function [ ] = updateWeights()
% this function controls:
% 1. the reward policy
% 2. the weight update
% 3. activate the "teaching"
global p a w buffer;
%% compute the reward values according to the reward policy
% compute the true reward at this time step
a.curRwd = compu... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/sim21.1_hiddenUnits/showState.m | 2,369 | utf_8 | c21f87f660c0fca0cff629e57b109e9f | % written by professor Jay McClelland
function [ ] = showState( )
global p w d a;
%% plot current and expected rewards over time
axes(d.rwd);
plot(w.rS.time,a.curRwd,'-b*'); hold on;
plot(w.rS.time,a.expRwd,'-r*');
legend({'current reward', 'esimtated reward'},...
'Location','northwest', 'fontsize', d.F... |
github | qihongl/mathCognition_PDP_RL-master | initParams.m | .m | mathCognition_PDP_RL-master/sim21.1_hiddenUnits/initParams.m | 4,039 | utf_8 | 6e227c3a8ebfc29ab41df9abd3be0bc0 | % written by professor Jay McClelland
function [] = initParams(epoch)
% This program initialize and preallocate the parameters needed for the
% model. This should be executed before the simulations.
global p a buffer
%% teaching strategy
% if teaching style is specified here, then trainGroup will use this
% va... |
github | qihongl/mathCognition_PDP_RL-master | computeFutureReward.m | .m | mathCognition_PDP_RL-master/sim21.1_hiddenUnits/computeFutureReward.m | 828 | utf_8 | 6577937c9a9f1142c512ac63bc28f977 | %% compute the expected reward with the Q learning rule
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% given: act_next predicted Q value
% s_cur current state (input)
% r_cur current actual reward
% taskDone if the task is terminated
% return: dfRw... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/sim21.1_hiddenUnits/updateState.m | 1,137 | utf_8 | b7f489fdff68b16f52c2b5336d86ef59 | % written by professor Jay McClelland
function [ ] = updateState()
%this function uses the real state to update the internal state
%after Act is called to execute the hand or eye movement action
global w h p a;
%% compute the relative locations
% the relative locations of eye and hand
w.vS.eyePos = 0;
w.vS.... |
github | qihongl/mathCognition_PDP_RL-master | initState.m | .m | mathCognition_PDP_RL-master/sim21.1_hiddenUnits/initState.m | 1,609 | utf_8 | 31fd190a63510d233a4f7fadf964e080 | % written by professor Jay McClelland
function [ ] = initState( )
global a w h p mode;
%realState is characterized by the position of a target to touch,
%position of eye, and position of hand 1-d space
%viewedState is the input I have given that my eye and hand are
%at particular positions w.r.t. the realSt... |
github | qihongl/mathCognition_PDP_RL-master | updateBuffer.m | .m | mathCognition_PDP_RL-master/sim21.1_hiddenUnits/updateBuffer.m | 1,427 | utf_8 | d34f7ab9c772aa1e53a37f5420bf6604 | %% update the memory buffer using the current experience
function [ ] = updateBuffer()
global p a w buffer;
memoryIdx = min(a.bufferUsage+1, p.bufferSize);
if a.bufferUsage+1 <= p.bufferSize
saveCurrentExperience(memoryIdx)
else
% delete the 1st experience in the buffer
buffer(1) = [];
% preallocate a... |
github | qihongl/mathCognition_PDP_RL-master | trainAgent.m | .m | mathCognition_PDP_RL-master/sim21.1_hiddenUnits/trainAgent.m | 1,906 | utf_8 | 66fb53004cf839722aceb5326c612ef8 | %% Trains the network n trials
% written by professor Jay McClelland
function [record] = trainAgent(epoch, seed)
%% initialization
% initialize parameters
global p a w mode;
initParams(epoch);
p.seed = seed;
rng(seed)
% preallocate
record.wts = cell(1,epoch / p.saveWtsInterval+1);
s.steps = nan(1,epoch);
... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/[past]/sim14_cdf/runAgent.m | 1,306 | utf_8 | 9bd9dc658681053014613a896d037422 | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p;
% rng(seed)
% w.seed = seed;
%% initialize the state
initState();
updateState();
computeAnswer(); % compute the true 'answers'
%% training the model once
i = 0;
indices = zeros(1,p.maxIter);
while ~(w.done) && i <... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/[past]/sim14_cdf/updateWeights.m | 706 | utf_8 | 53448a755be732aa3a23ac74686795a2 | % written by professor Jay McClelland
function [ ] = updateWeights()
% this function controls:
% 1. the reward policy
% 2. the weight update
% 3. activate the "teaching"
global a w p;
%% compute the reward values according to the reward policy
Rwd = computeRwd();
%% assign the reward values
a.Rwd = Rwd;
% ... |
github | qihongl/mathCognition_PDP_RL-master | testing.m | .m | mathCognition_PDP_RL-master/[past]/sim14_cdf/testing.m | 260 | utf_8 | 1e6564b3a7d8940acac43f80ac5f4041 | % just testing, a short cut for running the model
function testing(epoch)
if nargin == 0
epoch = 5000;
end
record = trainAgent(epoch);
save('record','record');
% eval the performance
quiz()
checkLearning()
beep % notice me that the program was ended
end
|
github | qihongl/mathCognition_PDP_RL-master | move.m | .m | mathCognition_PDP_RL-master/[past]/sim14_cdf/move.m | 403 | utf_8 | 76f0765b052705d3747442411be8e898 | % written by professor Jay McClelland
function [ ] = move( )
% here we act according to the action selected
% by selectAction
global w;
%% perform the actions
% update the real locations of hand and eye
w.rS.handPos = w.rS.handPos + w.out.handStep;
w.rS.eyePos = w.rS.eyePos + w.out.eyeStep;
w.rS.td = 1; ... |
github | qihongl/mathCognition_PDP_RL-master | initParams.m | .m | mathCognition_PDP_RL-master/[past]/sim14_cdf/initParams.m | 1,546 | utf_8 | a71df9726fde6a9c34d1bab8683a1c24 | % written by professor Jay McClelland
function [] = initParams(epoch)
% This program initialize and preallocate the parameters needed for the
% model. This should be executed before the simulations.
global p a
%% modeling parameters
p.wf = .1; % noise magnitude
p.lrate = .001; % learning rate
... |
github | qihongl/mathCognition_PDP_RL-master | computeRwd2.m | .m | mathCognition_PDP_RL-master/[past]/sim14_cdf/computeRwd2.m | 1,731 | utf_8 | bb2799e2a46161ad5a12b2658bbcd32f | function rwd = computeRwd2()
%% this function controls the reward policy
global w p h;
w.actionCorrect = true;
if isNext()
w.rS.targRemain(w.rS.handPos == w.rS.targPos) = false;
rwd = p.r.midPos;
if ~targetRemain()
rwd = p.r.midPos;
w.done = true;
end
else
if w.rS.handPos < nextOb... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/[past]/sim14_cdf/updateState.m | 947 | utf_8 | a76d60828bd889a9f748bff77920560c | % written by professor Jay McClelland
function [ ] = updateState()
%this function uses the real state to update the internal state
%after Act is called to execute the hand or eye movement action
global w h p;
%% compute the relative locations
% the relative locations of eye and hand
w.vS.eyePos = 0;
w.vS.ha... |
github | qihongl/mathCognition_PDP_RL-master | initState.m | .m | mathCognition_PDP_RL-master/[past]/sim14_cdf/initState.m | 1,334 | utf_8 | 4a0d05bf9ec01af0022c21f64c972170 | % written by professor Jay McClelland
function [ ] = initState( )
global a w h p;
%realState is characterized by the position of a target to touch,
%position of eye, and position of hand 1-d space
%viewedState is the input I have given that my eye and hand are
%at particular positions w.r.t. the realState o... |
github | qihongl/mathCognition_PDP_RL-master | plotResults.m | .m | mathCognition_PDP_RL-master/[past]/sim14_cdf/plotResults.m | 544 | utf_8 | da928d22f60607e06a82b5f24fe4b624 | function plotResults(record)
global d;
% initialize the parameters for the plot
d.fh = figure();
d.fh.WindowStyle = 'docked';
d.rax = subplot(3,1,1);
d.hax = subplot(3,1,2);
d.wax = subplot(3,1,3);
% d.dtimes = 2.^(10:10);
% load('record.mat');
for i = 1 : size(record.r{size(record.r,2)}.h,2)
plotRes(i, record);
e... |
github | qihongl/mathCognition_PDP_RL-master | trainAgent.m | .m | mathCognition_PDP_RL-master/[past]/sim14_cdf/trainAgent.m | 1,198 | utf_8 | 68355bc92e8d3b406dafd1e184e4cbdb | % written by professor Jay McClelland
function [record] = trainAgent(epoch)
%% This function trains the network n trials
% initialize parameters
global p a w;
initParams(epoch);
% preallocate
record.a = cell(1,epoch);
record.s.steps = nan(1,epoch);
record.s.indices = cell(1,epoch);
record.s.completed = fals... |
github | qihongl/mathCognition_PDP_RL-master | computeRwd.m | .m | mathCognition_PDP_RL-master/[past]/sim14_cdf/computeRwd.m | 2,610 | utf_8 | 10c26b1fe0767fa60553b9a7741014f2 | function Rwd = computeRwd()
%% this function controls the reward policy
global w p h;
w.actionCorrect = true;
% if there is remaining items
if targetRemain()
if w.out.handStep == 0 % not moving
Rwd = p.r.smallNeg;
% if stop too long, also termiante
% if h(w.stateNum).w.out.handStep == 0... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/[past]/sim17.2_tanh/runAgent.m | 1,376 | utf_8 | 5c6c39d68b7ef942d3454ecd87566f56 | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p mode;
%% initialize the state
initState();
updateState();
computeAnswer(); % compute the true 'answers'
%% training the model once
i = 0;
indices = zeros(1,p.maxIter);
while ~(w.done) && i < p.maxIter
%% choose ... |
github | qihongl/mathCognition_PDP_RL-master | trainOne.m | .m | mathCognition_PDP_RL-master/[past]/sim17.2_tanh/trainOne.m | 489 | utf_8 | 480fbd634b40a2e355805264ff3e4ac7 | % just testing, a short cut for running the model
function record = trainOne(epoch, seed)
if nargin == 0
epoch = 5000;
seed = randi(99);
end
%% run the simulation
record = trainAgent(epoch, seed);
%% save the simulation results
saveDirName = getSaveDir();
save([saveDirName '/' 'record'],'record');
save('recor... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/[past]/sim17.2_tanh/updateWeights.m | 1,080 | utf_8 | 54f24f733fa17794774cc8bc65757296 | % written by professor Jay McClelland
function [ ] = updateWeights()
% this function controls:
% 1. the reward policy
% 2. the weight update
% 3. activate the "teaching"
global p a w;
%% compute the reward values according to the reward policy
curRwd = computeRwd();
a.aAct_next = a.wts_HA*tanh(a.wts_VH * w.vS.... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/[past]/sim17.2_tanh/showState.m | 1,317 | utf_8 | 5f98a0c96bcf734cfce4abd1d109c314 | % written by professor Jay McClelland
function [ ] = showState( )
global p w d a;
% plot current and expected rewards over time
axes(d.rwd);
plot(w.rS.time,a.Rwd,'-b*'); hold on;
plot(w.rS.time,a.dfRwd,'-r*');
legend({'current reward', 'discounted future reward'},...
'Location','northwest', 'fontsize', d.F... |
github | qihongl/mathCognition_PDP_RL-master | move.m | .m | mathCognition_PDP_RL-master/[past]/sim17.2_tanh/move.m | 403 | utf_8 | 76f0765b052705d3747442411be8e898 | % written by professor Jay McClelland
function [ ] = move( )
% here we act according to the action selected
% by selectAction
global w;
%% perform the actions
% update the real locations of hand and eye
w.rS.handPos = w.rS.handPos + w.out.handStep;
w.rS.eyePos = w.rS.eyePos + w.out.eyeStep;
w.rS.td = 1; ... |
github | qihongl/mathCognition_PDP_RL-master | initParams.m | .m | mathCognition_PDP_RL-master/[past]/sim17.2_tanh/initParams.m | 2,508 | utf_8 | 4d8bf4632a030e580aac329e072347f8 | % written by professor Jay McClelland
function [] = initParams(epoch)
% This program initialize and preallocate the parameters needed for the
% model. This should be executed before the simulations.
global p a
p.teachingStyle = 3;
% 1 = final reward only
% 2 = intermediate reward
% 3 = final reward only + tea... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/[past]/sim17.2_tanh/updateState.m | 924 | utf_8 | 84c86d58e1b29b01ec1c1a3fad73c7c1 | % written by professor Jay McClelland
function [ ] = updateState()
%this function uses the real state to update the internal state
%after Act is called to execute the hand or eye movement action
global w h p;
%% compute the relative locations
% the relative locations of eye and hand
w.vS.eyePos = 0;
w.vS.hand... |
github | qihongl/mathCognition_PDP_RL-master | initState.m | .m | mathCognition_PDP_RL-master/[past]/sim17.2_tanh/initState.m | 1,630 | utf_8 | 33299d877a4234e89c16dee856064680 | % written by professor Jay McClelland
function [ ] = initState( )
global a w h p mode;
%realState is characterized by the position of a target to touch,
%position of eye, and position of hand 1-d space
%viewedState is the input I have given that my eye and hand are
%at particular positions w.r.t. the realSt... |
github | qihongl/mathCognition_PDP_RL-master | plotResults.m | .m | mathCognition_PDP_RL-master/[past]/sim17.2_tanh/plotResults.m | 544 | utf_8 | da928d22f60607e06a82b5f24fe4b624 | function plotResults(record)
global d;
% initialize the parameters for the plot
d.fh = figure();
d.fh.WindowStyle = 'docked';
d.rax = subplot(3,1,1);
d.hax = subplot(3,1,2);
d.wax = subplot(3,1,3);
% d.dtimes = 2.^(10:10);
% load('record.mat');
for i = 1 : size(record.r{size(record.r,2)}.h,2)
plotRes(i, record);
e... |
github | qihongl/mathCognition_PDP_RL-master | trainAgent.m | .m | mathCognition_PDP_RL-master/[past]/sim17.2_tanh/trainAgent.m | 1,501 | utf_8 | 61e7aeb84f1a92e22f73306d60a6cdcd | %% Trains the network n trials
% written by professor Jay McClelland
function [record] = trainAgent(epoch, seed)
%% initialization
% initialize parameters
global p a w mode;
initParams(epoch);
p.seed = seed;
rng(seed)
% preallocate
record.a = cell(1,epoch);
s.steps = nan(1,epoch);
s.indices = cell(1,epoch... |
github | qihongl/mathCognition_PDP_RL-master | computeRwd.m | .m | mathCognition_PDP_RL-master/[past]/sim17.2_tanh/computeRwd.m | 2,205 | utf_8 | 8cad5b2bcbe004359ae289b13c3b08d7 | function Rwd = computeRwd()
%% this function controls the reward policy
global w p h a;
w.actionCorrect = true;
% if there is remaining items
if targetRemain()
if w.out.handStep == 0 % not moving
Rwd = p.r.smallNeg;
elseif ~isTouchingObj % touching empty spot
Rwd = p.r.smallNeg;
e... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/[past]/sim22.0_touchInput/runAgent.m | 935 | utf_8 | f8aa34d17d840c50bd4e81973476c2c5 | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p mode;
%% initialize the state
initState();
updateState();
% compute the true answers, for teacher demonstration
computeAnswer();
%% train the model once
t = 0;
indices = zeros(1,p.maxIter);
while ~(w.done) && t < ... |
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