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github | qihongl/mathCognition_PDP_RL-master | trainOne.m | .m | mathCognition_PDP_RL-master/[past]/sim22.0_touchInput/trainOne.m | 766 | utf_8 | f3211fdddc8a90f2cb37ab64dfcdfc99 | % 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([saveDi... |
github | qihongl/mathCognition_PDP_RL-master | showWeights.m | .m | mathCognition_PDP_RL-master/[past]/sim22.0_touchInput/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/[past]/sim22.0_touchInput/updateWeights.m | 2,693 | utf_8 | dd6cf90843b639edb61c2d98d1a02acc | % 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/[past]/sim22.0_touchInput/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/[past]/sim22.0_touchInput/initParams.m | 3,622 | utf_8 | 4ac7957e0b4493722f363ff2d19a6917 | % written by professor Jay McClelland
function [] = initParams(epoch)
% Initialize and preallocate the parameters needed for the model.
global p a buffer
%% simulation parameters
p.runs = epoch; % training upper lim
p.maxIter = 100; % terminate if cannot finish in 100 iter
p.saveWtsInterval =... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/[past]/sim22.0_touchInput/updateState.m | 1,139 | utf_8 | 6c4fb41b626e6f9ba4590073f39bc3fa | % 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/[past]/sim22.0_touchInput/initState.m | 1,835 | utf_8 | 50fdda048c2df17889bc904eaa8eca0d | % 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/[past]/sim22.0_touchInput/updateBuffer.m | 1,526 | utf_8 | a8800fed2e034da1b49ad2543a9a41e2 | %% 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/[past]/sim22.0_touchInput/trainAgent.m | 1,880 | utf_8 | 0a146998e62a71cdc4f21eebb53ea185 | %% 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/[past]/sim22.0_touchInput/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 | chooseAction.m | .m | mathCognition_PDP_RL-master/[past]/sim03_Xitems/chooseAction.m | 397 | utf_8 | fffb433a0ee855f4dd40db7fe24da0ca | function [action] = chooseAction(w,p,a)
% obtain the probability distribution for actions
prob = softmax(a.q(w.curs+1,:), p.qscale);
% choose action based
action = sample(prob);
end
function [action] = sample(pr)
rv = rand(1); % sample from a unifrom[0,1]
cp = cumsum(pr);
for i = 1:length(pr) % choose an a... |
github | qihongl/mathCognition_PDP_RL-master | softmax.m | .m | mathCognition_PDP_RL-master/[past]/sim03_Xitems/softmax.m | 622 | utf_8 | 90f2f58a719facf0fbef333ac0a0b761 | % SOFTMAX assign probabilities to options
% in my case, x is expected to be a vector of q values for a state: Q(s,:)
function [prob] = softmax(x, scale)
% if there is no input scaling factor, just don't scale it
if nargin == 1
scale = 1;
end
% transform when there is negative values
% TODO: this transformation i... |
github | qihongl/mathCognition_PDP_RL-master | initState.m | .m | mathCognition_PDP_RL-master/[past]/sim03_Xitems/initState.m | 951 | utf_8 | eb244da6de076ac9b70edeb21dcc59bb | % INITSTATE initialize the state parameters, or set up the "world"
% note that all things here are TRIAL SPECIFIC
% originally written by Professor Jay McClelland
function [w] = initState(p)
% preallocation
w.curs = 0; % current state
w.cura = 0; % current action
w.nexts = 0; % next state
w.nexta =... |
github | qihongl/mathCognition_PDP_RL-master | touch.m | .m | mathCognition_PDP_RL-master/[past]/sim03_Xitems/touch.m | 1,801 | utf_8 | 342ffa773bb2ffe05e52acdd0eb1f5e2 | %% a RL based model for counting
function output = touch(epochs, seed, doPlotting, showSteps, showProgress)
if nargin == 0
epochs = 100; seed = randi(99);
doPlotting = true; showSteps = false; showProgress = false;
end
rng(seed);
%% initialization
% modeing parameters
p = setupParameters(epochs);
% preallocat... |
github | qihongl/mathCognition_PDP_RL-master | setupParameters.m | .m | mathCognition_PDP_RL-master/[past]/sim03_Xitems/setupParameters.m | 704 | utf_8 | 09d63fb7966847f1c4fc72c0e9c8384d | %SETUPPARAMETERS returns the parameters for the model
function [p] = setupParameters(epochs)
% number of training epochs
p.trials = epochs;
% modeling parameters
p.gamma = 0.5; % discount factor
p.alpha = 0.2; % learning rate
p.qscale = 3; % softmax scaling factor
% other parameters
p.range = 6; ... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouchTemplate/runAgent.m | 479 | utf_8 | 64604dab4ae301f43144dd7967efcd2d | % written by professor Jay McClelland
function [ results ] = runAgent( )
%UNTITLED11 Summary of this function goes here
% Detailed explanation goes here
global a w h p d;
initState();
updateState();
showState();
i = 0;
while w.rS.handPos ~= w.rS.targPos && i < 50
selectAction();
Act();
updat... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouchTemplate/updateWeights.m | 532 | utf_8 | 6f5ad6a5a737cae150113a230254b5cc | % written by professor Jay McClelland
function [ ] = updateWeights( )
global a w p;
% change in weights equals input times reward prediction error
if w.rS.handPos == w.rS.targPos
Rwd = 1;
else
Rwd = max(a.wts*w.vS.visInput');
end
a.Rwd = Rwd;
a.dfRwd = Rwd*p.gamma^w.rS.td;
% isize = min(abs(a.dfRwd... |
github | qihongl/mathCognition_PDP_RL-master | selectAction.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouchTemplate/selectAction.m | 1,344 | utf_8 | 2b940e8b250810b9007f5be48f267838 | % 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/[past]/demo_fixTouchTemplate/showState.m | 1,044 | utf_8 | 9698c2fb8721bcf88d25036a172a1c38 | % written by professor Jay McClelland
function [ ] = showState( )
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
global w d a h;
%plot the positions of eye, hand, and target in the real state
% axes(d.rSax);
% cla;
% plot(d.rSax,[-50 50],[0 0]); hold on;
% plot(d.rSax,-50:5... |
github | qihongl/mathCognition_PDP_RL-master | initParamsEtc.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouchTemplate/initParamsEtc.m | 970 | utf_8 | b4218481cee376921d7758b4f704e786 | % written by professor Jay McClelland
function [] = initParamsEtc( )
%
global p d a
p.wf = .15;
% p.handTime = 5; % the amount of time needed to move hand
p.eyeTime = 1; % the amount of time needed to move eye
p.learner = 1; %
p.lrate = .1; % alpha
p.runs = 4096; % epochs
p.gamma ... |
github | qihongl/mathCognition_PDP_RL-master | choose.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouchTemplate/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/[past]/demo_fixTouchTemplate/updateState.m | 864 | utf_8 | ced4327b0d1a8f127ea3f5692b785e6a | % 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;
w.vS.eyePos = 0;
% assume we can target the hand position perfectly
w.vS.handPos = w.rS.handPos ... |
github | qihongl/mathCognition_PDP_RL-master | initState.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouchTemplate/initState.m | 1,262 | utf_8 | e2101e924706caaf51b295575f7e9aed | % 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 | trainAgent.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouchTemplate/trainAgent.m | 443 | utf_8 | ab5a17557f164e2c141d66db305d4a88 | % written by professor Jay McClelland
function [record] = trainAgent( )
global p d a;
initParamsEtc();
run = struct('results',[]);
di = 1;
for i = 1:p.runs
run.results = runAgent();
a.smgain = a.smgain + p.smirate;
if i == d.dtimes(di)
prstr = sprintf('%d: ',i);
rstr = input(prstr,... |
github | qihongl/mathCognition_PDP_RL-master | Act.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouchTemplate/Act.m | 656 | utf_8 | 6a60c5c1c7a2c99fd43c15d4de1944e6 | % written by professor Jay McClelland
function [ ] = Act( )
% here we act according to the action selected
% by selectAction
global w p;
%% where are hand and eye in rS
w.rS.handPos = w.rS.handPos + w.out.handStep;
w.rS.eyePos = w.rS.eyePos + w.out.eyeStep;
w.rS.td = 1; % the time difference
w.rS.time = w... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/[past]/sim16.2_punFac/runAgent.m | 1,392 | utf_8 | f58afc19f6593a1d0bea36a8402e7dca | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p mode;
% 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) &... |
github | qihongl/mathCognition_PDP_RL-master | trainOne.m | .m | mathCognition_PDP_RL-master/[past]/sim16.2_punFac/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]/sim16.2_punFac/updateWeights.m | 764 | utf_8 | 04391ab808a2c6442632b5f50d71b7e8 | % 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();
expRwd = max(a.wts*w.vS.visInput');
%% assi... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/[past]/sim16.2_punFac/showState.m | 1,317 | utf_8 | 177371f3562e9304f1bf71be51424ff1 | % 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]/sim16.2_punFac/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]/sim16.2_punFac/initParams.m | 1,845 | utf_8 | 80b107e35451b5e7017cdff4174b89a3 | % 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.runs = epoch; % training upper lim
p.maxIter = 100; % terminate if... |
github | qihongl/mathCognition_PDP_RL-master | computeRwd2.m | .m | mathCognition_PDP_RL-master/[past]/sim16.2_punFac/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]/sim16.2_punFac/updateState.m | 951 | utf_8 | 6c3388db9d1faa1711f00bbbdbd2b961 | % 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/[past]/sim16.2_punFac/initState.m | 1,594 | utf_8 | d62eadf6f64277fd0e0e4533bcc1baf3 | % 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]/sim16.2_punFac/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]/sim16.2_punFac/trainAgent.m | 1,241 | utf_8 | e0470615613797744739937eed5b812e | %% 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,epo... |
github | qihongl/mathCognition_PDP_RL-master | computeRwd.m | .m | mathCognition_PDP_RL-master/[past]/sim16.2_punFac/computeRwd.m | 2,255 | utf_8 | ffb5370d99695989298f09793052de31 | 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]/demo_fixTouch/runAgent.m | 477 | utf_8 | dbc7fe877cd329404d69ca2895247162 | % written by professor Jay McClelland
function [ results ] = runAgent( )
%UNTITLED11 Summary of this function goes here
% Detailed explanation goes here
global a w h p d;
initState();
updateState();
showState();
i = 0;
while w.rS.handPos ~= w.rS.targPos && i < 50
selectAction();
Act();
upd... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouch/updateWeights.m | 684 | utf_8 | b587e5cac41aa9be2130eeed1a3a78cd | % written by professor Jay McClelland
function [ ] = updateWeights( )
global a w p;
% change in weights equals input times reward prediction error
if w.rS.handPos == w.rS.targPos
aRwd = 1; %was Rwd actual reward
% else
% Rwd = max(a.wts*w.vS.visInput');
end
eRwd = max(a.wts*w.vS.visInput');
a.Rwd =... |
github | qihongl/mathCognition_PDP_RL-master | selectAction.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouch/selectAction.m | 867 | utf_8 | d70c66106d4e469fe255bd2b557d3731 | % written by professor Jay McClelland
function [] = selectAction( )
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
global w a p;
targGuess = choose(w.vS.visInput) - 51;
if p.learner == 0
if abs(targGuess) < 3
w.out.chooseHand = 1;
else
w.out.choo... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouch/showState.m | 1,044 | utf_8 | 9698c2fb8721bcf88d25036a172a1c38 | % written by professor Jay McClelland
function [ ] = showState( )
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
global w d a h;
%plot the positions of eye, hand, and target in the real state
% axes(d.rSax);
% cla;
% plot(d.rSax,[-50 50],[0 0]); hold on;
% plot(d.rSax,-50:5... |
github | qihongl/mathCognition_PDP_RL-master | initParamsEtc.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouch/initParamsEtc.m | 731 | utf_8 | cbc24e0e0866e3e43e5f10fc830a33a5 | % written by professor Jay McClelland
function [] = initParamsEtc( )
%
global p d a
p.wf = .25;
p.handTime = 5; % the amount of time needed to move hand
p.eyeTime = 1; % the amount of time needed to move eye
p.learner = 1; %
p.lrate = .01; % alpha
p.runs = 4096; % epochs
p.gamma =... |
github | qihongl/mathCognition_PDP_RL-master | choose.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouch/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/[past]/demo_fixTouch/updateState.m | 849 | utf_8 | 9af3d70b2c713638037079e192297aed | % 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;
w.vS.eyePos = 0;
w.vS.handPos = w.rS.handPos - w.rS.eyePos;
% the next line is the apparent new ... |
github | qihongl/mathCognition_PDP_RL-master | initState.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouch/initState.m | 1,321 | utf_8 | ac61257c191f02357b18d06327446712 | % 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 | trainAgent.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouch/trainAgent.m | 443 | utf_8 | ab5a17557f164e2c141d66db305d4a88 | % written by professor Jay McClelland
function [record] = trainAgent( )
global p d a;
initParamsEtc();
run = struct('results',[]);
di = 1;
for i = 1:p.runs
run.results = runAgent();
a.smgain = a.smgain + p.smirate;
if i == d.dtimes(di)
prstr = sprintf('%d: ',i);
rstr = input(prstr,... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights_v2.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouch/updateWeights_v2.m | 916 | utf_8 | cd5edb43c669077fb18aad932a218cd9 | % written by professor Jay McClelland
% not currently used - this version adds the actual reward and disc future
% reward rather than using the actual if obtained and disc future rwd
% otherwise
function [ ] = updateWeights( )
global a w p;
% change in weights equals input times reward prediction error
if w.rS... |
github | qihongl/mathCognition_PDP_RL-master | Act.m | .m | mathCognition_PDP_RL-master/[past]/demo_fixTouch/Act.m | 376 | utf_8 | 7dd57f820956d2e27329a066504c09c4 | % written by professor Jay McClelland
function [ ] = Act( )
% here we act according to the action selected
% by selectAction
global w p;
if w.out.chooseHand == 1
w.rS.handPos = w.rS.handPos + w.out.handStep;
w.rS.td = p.handTime;
else
w.rS.eyePos = w.rS.eyePos + w.out.eyeStep;
w.rS.td = p.... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/[past]/sim16.5_compleUnit/runAgent.m | 1,493 | utf_8 | 3af998953e263322252c3725d0de8f54 | % 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]/sim16.5_compleUnit/trainOne.m | 490 | utf_8 | 53151cd96a63c8a9cea27f940568c818 | % 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/[past]/sim16.5_compleUnit/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/[past]/sim16.5_compleUnit/updateWeights.m | 830 | utf_8 | c4affc82b8bfaac2f17165602e3c4a34 | % 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();
expRwd = max(a.wts*w.vS.visInput');
if ~w... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/[past]/sim16.5_compleUnit/showState.m | 2,195 | utf_8 | 04a85884cc5f6e7cf26638c607de124f | % 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',... |
github | qihongl/mathCognition_PDP_RL-master | move.m | .m | mathCognition_PDP_RL-master/[past]/sim16.5_compleUnit/move.m | 403 | utf_8 | a8c158962dd0428e90aff3bada221b17 | % 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]/sim16.5_compleUnit/initParams.m | 2,515 | utf_8 | cababa8a13fd9c153c5794dad3d818b1 | % 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 + tea... |
github | qihongl/mathCognition_PDP_RL-master | computeRwd2.m | .m | mathCognition_PDP_RL-master/[past]/sim16.5_compleUnit/computeRwd2.m | 1,991 | utf_8 | 45c515cd014a3a3691c14f39d80b2c7a | function Rwd = computeRwd2()
%% this function controls the reward policy
global w p h a;
w.actionCorrect = true;
if w.out.handStep == 0 % not moving
Rwd = p.r.smallNeg;
elseif ~isTouchingObj % touching empty spot
Rwd = p.r.smallNeg;
elseif objIsTouched % touching touched object
Rwd = p.r... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/[past]/sim16.5_compleUnit/updateState.m | 1,094 | utf_8 | 953f352a266c90e433fa16c58844ff12 | % 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/[past]/sim16.5_compleUnit/initState.m | 1,623 | utf_8 | 58ea3381194546e090037cf1e5458be7 | % 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]/sim16.5_compleUnit/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]/sim16.5_compleUnit/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]/sim16.5_compleUnit/computeRwd.m | 2,501 | utf_8 | 34e581ed0bd67cb9271f5978ee6c956f | 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 == ... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/[past]/sim16.5.3_sigmoid/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/[past]/sim16.5.3_sigmoid/trainOne.m | 594 | utf_8 | b04fa7f72976b17585c806628b34dfe6 | % 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/[past]/sim16.5.3_sigmoid/showWeights.m | 773 | utf_8 | 56b009e4997900472132fa009bf09ed3 | function [ ] = showWeights( )
% plot the weights of the model
global p d a;
% wts from visual input to hidden
axes(d.wts_VH)
imagesc(a.wts_VH)
title(d.wts_VH, 'Weights: visual -> hidden', 'fontsize', d.FONTSIZE)
xlabel(d.wts_VH, 'Visual input layer', 'fontsize', d.FONTSIZE)
ylabel(d.wts_VH, 'Hidden layer', 'fontsize',... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/[past]/sim16.5.3_sigmoid/updateWeights.m | 1,038 | utf_8 | bfd48c555d301bc8bcc5c70fb23ca954 | % 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();
a.aAct_next = a.wts_HA*sigmoid(a.wts_VH * ... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/[past]/sim16.5.3_sigmoid/showState.m | 2,342 | utf_8 | bd66686c8e3378bba847040f75636451 | % 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', 'estimated future reward'},...
'Location','northwest', 'fontsiz... |
github | qihongl/mathCognition_PDP_RL-master | initParams.m | .m | mathCognition_PDP_RL-master/[past]/sim16.5.3_sigmoid/initParams.m | 2,278 | utf_8 | 533b158e03aa0dbb7e60dc633e41dedd | % 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 + tea... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/[past]/sim16.5.3_sigmoid/updateState.m | 1,022 | utf_8 | a5543b71621025d356ba32965449739a | % 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/[past]/sim16.5.3_sigmoid/initState.m | 1,753 | utf_8 | 7c3c8f8878f051bc61f77927e8b048d7 | % 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/[past]/sim16.5.3_sigmoid/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 | runAgent.m | .m | mathCognition_PDP_RL-master/[past]/sim16.8_hiddenUnits/runAgent.m | 993 | utf_8 | 20e3f9ffb9184133ad47bd682682186e | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p mode;
%% initialize the state
initState();
updateState();
computeAnswer();
%% training the model once
i = 0;
indices = zeros(1,p.maxIter);
while ~(w.done) && i < p.maxIter
%% choose action
selectAction();
... |
github | qihongl/mathCognition_PDP_RL-master | trainOne.m | .m | mathCognition_PDP_RL-master/[past]/sim16.8_hiddenUnits/trainOne.m | 490 | utf_8 | 53151cd96a63c8a9cea27f940568c818 | % 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/[past]/sim16.8_hiddenUnits/showWeights.m | 773 | utf_8 | 56b009e4997900472132fa009bf09ed3 | function [ ] = showWeights( )
% plot the weights of the model
global p d a;
% wts from visual input to hidden
axes(d.wts_VH)
imagesc(a.wts_VH)
title(d.wts_VH, 'Weights: visual -> hidden', 'fontsize', d.FONTSIZE)
xlabel(d.wts_VH, 'Visual input layer', 'fontsize', d.FONTSIZE)
ylabel(d.wts_VH, 'Hidden layer', 'fontsize',... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/[past]/sim16.8_hiddenUnits/updateWeights.m | 1,927 | utf_8 | 2149de990aadca0a43ab29b938948673 | % 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*sigmoid(a.wts_VH * w.... |
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/[past]/sim16.8_hiddenUnits/showState.m | 2,161 | utf_8 | 33d849cf16ea8fb1c5e0f476aa23c64b | % 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',... |
github | qihongl/mathCognition_PDP_RL-master | move.m | .m | mathCognition_PDP_RL-master/[past]/sim16.8_hiddenUnits/move.m | 404 | utf_8 | f991ccf8031e923e5cb7bf57714e0235 | % 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]/sim16.8_hiddenUnits/initParams.m | 2,334 | utf_8 | 896ef64bf3aea760f34ae0ae2160a59f | % 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 = 2;
% 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]/sim16.8_hiddenUnits/updateState.m | 1,084 | utf_8 | 1be1d547bea5f33708312d440733ee19 | % 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/[past]/sim16.8_hiddenUnits/initState.m | 1,564 | utf_8 | 2cbd3d9d9d3194e4cd1bcabe49d8a259 | % written by professor Jay McClelland
function [ ] = initState( )
global a w h p mode;
%% specify the parameters
% a.act = zeros(p.mvRange+1,1);
a.dfRwd = 0;
a.Rwd = 0;
w.rS.time = 0;
w.rS.td = 0;
w.stateNum = -1;
% preallocation for activations
% a.old.hIn = zeros(p.nHidden,1);
% a.old.hAct = zeros(p... |
github | qihongl/mathCognition_PDP_RL-master | trainAgent.m | .m | mathCognition_PDP_RL-master/[past]/sim16.8_hiddenUnits/trainAgent.m | 1,894 | utf_8 | d70fec85e52717958ed9a0d491e17f01 | %% 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)
progBarLen = 10;
% preallocate
record.a = cell(1,epoch);
s = preallocateScores(epoch);
... |
github | qihongl/mathCognition_PDP_RL-master | computeRwd.m | .m | mathCognition_PDP_RL-master/[past]/sim16.8_hiddenUnits/computeRwd.m | 2,382 | utf_8 | ece39cb88c90cfd4bad47960845c3350 | function Rwd = computeRwd()
%% this function controls the reward policy
global w p h a;
% if there is remaining items
if targetRemain()
if a.choice == p.mvRange +1 % saying "done"
Rwd = p.r.smallNeg;
w.stopEarly = true;
w.done = true;
elseif a.choice == p.mvRad +1 % not moving
... |
github | qihongl/mathCognition_PDP_RL-master | runAgent.m | .m | mathCognition_PDP_RL-master/[past]/sim12_combineTeach/runAgent.m | 1,246 | utf_8 | 5e4d1f098e22cf86de2a6ecab8e97796 | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p;
% rng(seed)
% w.seed = seed;
%% initialize the state
initState();
updateState();
% compute the true answers
w.answer = computeAnswer();
%% training the model once
i = 0;
teachTrial = 0;
indices = zeros(1,p.maxIter);... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/[past]/sim12_combineTeach/updateWeights.m | 721 | utf_8 | 8553f7c79f7892efa4f60a039b22a6e0 | % 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;
a... |
github | qihongl/mathCognition_PDP_RL-master | selectAction.m | .m | mathCognition_PDP_RL-master/[past]/sim12_combineTeach/selectAction.m | 722 | utf_8 | 564f4a8eafd6f613fefa938376e1a26c | % written by professor Jay McClelland
function [] = selectAction( )
% It computes the output, and choose the action probabilistically.
global w a p;
%% compute the output activation
a.act = a.wts * w.vS.visInput';
% bias toward action 0 (don't move)
a.act(p.mvRad + 1) = a.act(p.mvRad + 1) + a.bias;
% choose a... |
github | qihongl/mathCognition_PDP_RL-master | testing.m | .m | mathCognition_PDP_RL-master/[past]/sim12_combineTeach/testing.m | 190 | utf_8 | 00551727050d696efb45a0c7eed98de5 | % just testing, a short cut for running the model
function testing(epoch)
if nargin == 0
epoch = 10;
end
record = trainAgent(epoch);
save('record','record');
% checkDevelop()
quiz()
end
|
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/[past]/sim12_combineTeach/showState.m | 1,122 | utf_8 | f34bd313a9d7904b8005de7e702b707c | % written by professor Jay McClelland
function [ ] = showState( )
global p w d a;
% plot rewards over time
axes(d.rax);
% if a.Rwd == p.r.smallNeg
% color = 'c';
% elseif a.Rwd == p.r.midNeg || a.Rwd == p.r.bigNeg
% color = 'b';
% elseif a.Rwd == p.r.midPos
% color = 'y';
% elseif a.Rwd == p.r.... |
github | qihongl/mathCognition_PDP_RL-master | initParams.m | .m | mathCognition_PDP_RL-master/[past]/sim12_combineTeach/initParams.m | 1,495 | utf_8 | 110cbcc7d5f909f3f0ad9638efba56ea | % 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 = .2; % noise magnitude
p.lrate = .01; % learning rate
p... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/[past]/sim12_combineTeach/updateState.m | 1,042 | utf_8 | 908fc46ea462321cb5bade36501f9a34 | % 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 h p;
%% 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/[past]/sim12_combineTeach/initState.m | 1,346 | utf_8 | f5d17e6ac041941df733e964fab3ede9 | % 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
%in a 101-pixel one-d space
%viewedState is the input I have given that my eye and hand are
%at particular positions w.r... |
github | qihongl/mathCognition_PDP_RL-master | plotResults.m | .m | mathCognition_PDP_RL-master/[past]/sim12_combineTeach/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]/sim12_combineTeach/trainAgent.m | 949 | utf_8 | 73bb1b6fb81e3c6dcea2d70a456f8d26 | % written by professor Jay McClelland
function [record] = trainAgent(epoch)
%% This function trains the network n trials
% initialize parameters
global p d a;
initParams(epoch);
% initPlot();
% preallocate
record.a = cell(1,epoch);
record.steps = nan(1,epoch);
% record.indices = cell(1,epoch);
% record.com... |
github | qihongl/mathCognition_PDP_RL-master | computeRwd.m | .m | mathCognition_PDP_RL-master/[past]/sim12_combineTeach/computeRwd.m | 2,411 | utf_8 | 8220c6a9c20be837ce0b2054677ad5e9 | function Rwd = computeRwd()
%% this function controls the reward policy
global w a p;
w.actionCorrect = true;
% if there is remaining items
if targetRemain()
% if a.choice == p.mvRad+1 % not moving
if w.out.handStep == 0
Rwd = p.r.smallNeg;
% if stop too long, also termiante
% w.stopC... |
github | qihongl/mathCognition_PDP_RL-master | Act.m | .m | mathCognition_PDP_RL-master/[past]/sim12_combineTeach/Act.m | 402 | utf_8 | 9240309e992077ffcf3f10b3cc146514 | % written by professor Jay McClelland
function [ ] = Act( )
% 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 | runAgent.m | .m | mathCognition_PDP_RL-master/[past]/sim10_forcing/runAgent.m | 1,273 | utf_8 | 2b39b01046313d8b18b63684ee0d47d1 | % written by professor Jay McClelland
function [ results ] = runAgent()
global a w h p;
% rng(seed)
% w.seed = seed;
%% initialize the state
initState();
updateState();
% compute the true answers
w.answer = computeAnswer(w);
%% training the model once
i = 0;
teachTrial = 0;
indices = zeros(1,p.maxIter)... |
github | qihongl/mathCognition_PDP_RL-master | updateWeights.m | .m | mathCognition_PDP_RL-master/[past]/sim10_forcing/updateWeights.m | 684 | utf_8 | 2a35707770a0830894255d38e3f647ef | % 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;
a... |
github | qihongl/mathCognition_PDP_RL-master | selectAction.m | .m | mathCognition_PDP_RL-master/[past]/sim10_forcing/selectAction.m | 725 | utf_8 | 97771aa99d8de06b91a45b864e56e9df | % written by professor Jay McClelland
function [] = selectAction( )
% It computes the output, and choose the action probabilistically.
global w a p;
%% compute the output activation
a.act = a.wts * w.vS.visInput';
% bias toward action 0 (don't move)
a.act(p.mvRad + 1) = a.act(p.mvRad + 1) + a.bias;
% choose a... |
github | qihongl/mathCognition_PDP_RL-master | testing.m | .m | mathCognition_PDP_RL-master/[past]/sim10_forcing/testing.m | 192 | utf_8 | ccef1f2affaa5f7136ec4542a7ccd35c | % just testing, a short cut for running the model
function testing(epoch)
if nargin == 0
epoch = 5000;
end
record = trainAgent(epoch);
save('record','record');
% checkDevelop()
quiz()
end
|
github | qihongl/mathCognition_PDP_RL-master | showState.m | .m | mathCognition_PDP_RL-master/[past]/sim10_forcing/showState.m | 1,122 | utf_8 | 7b511266ea2cd400a15d7791420eecf3 | % written by professor Jay McClelland
function [ ] = showState( )
global p w d a;
% plot rewards over time
axes(d.rax);
% if a.Rwd == p.r.smallNeg
% color = 'c';
% elseif a.Rwd == p.r.midNeg || a.Rwd == p.r.bigNeg
% color = 'b';
% elseif a.Rwd == p.r.midPos
% color = 'y';
% elseif a.Rwd == p.r.... |
github | qihongl/mathCognition_PDP_RL-master | initParams.m | .m | mathCognition_PDP_RL-master/[past]/sim10_forcing/initParams.m | 1,494 | utf_8 | 81f7a99a0781b30e023eccdaf67a577f | % 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 = .2; % noise magnitude
p.lrate = .01; % learning rate
p... |
github | qihongl/mathCognition_PDP_RL-master | updateState.m | .m | mathCognition_PDP_RL-master/[past]/sim10_forcing/updateState.m | 1,046 | utf_8 | faaf159ed3ff215d63ba4a9e6f61cf06 | % 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 h p;
%% compute the relative locations
% the relative locations of eye and hand
w.vS.eyePos = 0;
w.vS.... |
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