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RL_main_ActorCritic.m
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RL_main_ActorCritic.m
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%Quadcopter Renforcement Learning Simulation
%Written by: Brian Wade, 3 Jan 20
%MATLAB dependencies: Reinforcement learning, deep learning, global
%optimization, statistics and machine learning
%Parallel toolbox required if "want_parallel" or "want_gpu" is set to true
%% Initalize
clear
clc
close all
start_time=tic;
rng(0) %set random seed
%% User inputs
intial_radius = 0; %initial start radius (random direction) from target- m
initial_deviation = 3; %inital random angular velocity - deg/s
action_range = 3; %number of actions for each motor
want_parallel = false;
want_gpu = false;
view_networks = true; %show and save actor and critic networks
saveFinalAgent = true;
doTraining = true;
doSim = true;
num_sims = 10;
image_folder = 'Images';
agentName = 'AC';
learn_rate_critic = 1e-3;
grad_threshold_critic = 1;
L2RegFac_critic = 1e-5;
learn_rate_actor = 1e-4;
grad_threshold_actor = 1;
L2RegFac_actor = 1e-4;
criticStateFC1size=264;
criticStateFC2size=128;
actorFC1size=128;
actorFC2size=64;
DiscountFactor = 0.99;
EntropyLossWeight = 0.05;
NumStepsToLookAhead = 128;
MaxEpisodes = 40000;
ScoreAveragingWindowLength = 30;
StopTrainingCriteria = 'AverageReward';
StopTrainingValue = 290;
%sim run time
sim_start = 0; %start time of simulation
sim_end = 3; %end time of simulation in sec
dt = 0.01; %step size in sec
%% Multicore - Start pool of workers
%Start worker pool
if want_parallel == true
poolobj = gcp('nocreate'); % If no pool, do not create new one.
%delete(poolobj)
if isempty(poolobj)
poolsize_want = round(.9*feature('numcores'));
if poolsize_want < feature('numcores')
poolsize = poolsize_want;
else
poolsize = feature('numcores')-1;
end
parpool('local',poolsize);
else
poolsize = poolobj.NumWorkers;
end
end
%% Create agent folder for saved agents
agent_folder = 'Agents';
if ~exist(agent_folder, 'dir')
mkdir(agent_folder)
end
%% Setup Renforcement Learning Action and Observation Spaces
numObs = 13;
ObservationInfo = rlNumericSpec([numObs 1]);
ObservationInfo.Name = 'Quadcopter States';
ObservationInfo.Description = ...
'x, y, z, dx, dy, dz, phi, theta, psi, dphi, dtheta, dpsi, sim_time';
ii=0;
action_space = cell(action_range);
for i = -1:2/(action_range - 1):1
ii=ii+1;
jj=0;
for j = -1:2/(action_range - 1):1
jj=jj+1;
kk=0;
for k = -1:2/(action_range - 1):1
kk=kk+1;
ll=0;
for l = -1:2/(action_range - 1):1
ll=ll+1;
action_space{ii,jj,kk,ll}=[i j k l];
end
end
end
end
ActionInfo = rlFiniteSetSpec(action_space);
ActionInfo.Name = 'Quadcopter Action';
num_actions = length(ActionInfo.Elements);
%define custom environment
StepHandle = @(Action,LoggedSignals) QuadcopterStepFunction(Action,...
LoggedSignals,dt);
ResetHandle = @() QuadcopterResetFunction(intial_radius,initial_deviation);
env = rlFunctionEnv(ObservationInfo,ActionInfo,StepHandle,ResetHandle);
%% Critic Network
criticPath = [
imageInputLayer([numObs 1 1],'Normalization','none','Name','state')
fullyConnectedLayer(criticStateFC1size,'Name','CriticStateFC1')
reluLayer('Name','CriticRelu1')
fullyConnectedLayer(criticStateFC2size,'Name','CriticStateFC2')
reluLayer('Name','CriticRelu2')
fullyConnectedLayer(1, 'Name', 'CriticOutput')];
criticNetwork = layerGraph(criticPath);
if view_networks == true
figure
plot(criticNetwork)
image_file = 'criticNetwork.png';
image_save_path = fullfile(image_folder,image_file);
saveas(gcf,image_save_path)
end
if want_gpu == true
criticOpts = rlRepresentationOptions('LearnRate',learn_rate_critic,...
'GradientThreshold',grad_threshold_critic,'UseDevice',"gpu",...
'L2RegularizationFactor',L2RegFac_critic);
else
criticOpts = rlRepresentationOptions('LearnRate',learn_rate_critic,...
'GradientThreshold',grad_threshold_critic,...
'L2RegularizationFactor',L2RegFac_critic);
end
critic = rlValueRepresentation(criticNetwork,ObservationInfo,...
'Observation',{'state'},criticOpts);
%% Actor Network
actorPath = [
imageInputLayer([numObs 1 1],'Normalization','none','Name','state')
fullyConnectedLayer(actorFC1size, 'Name','ActorFC1')
reluLayer('Name','ActorRelu1')
fullyConnectedLayer(actorFC2size,'Name','ActorFC2')
reluLayer('Name','ActorRelu2')
fullyConnectedLayer(num_actions,'Name','ActorFC4')
softmaxLayer('Name','ActorOutput')];
actorNetwork = layerGraph(actorPath);
if view_networks == true
figure
plot(actorNetwork)
image_file = 'actorNetwork.png';
image_save_path = fullfile(image_folder,image_file);
saveas(gcf,image_save_path)
end
actorOpts = rlRepresentationOptions('LearnRate',learn_rate_actor,...
'GradientThreshold',grad_threshold_actor,...
'L2RegularizationFactor',L2RegFac_actor);
actor = rlStochasticActorRepresentation(actorNetwork,ObservationInfo,...
ActionInfo,'Observation',{'state'},actorOpts);
%% Create Agent
%steps per episode
steps_per_episode = ceil((sim_end - sim_start)/dt);
agentOptions = rlACAgentOptions(...
'SampleTime', dt,...
'EntropyLossWeight', EntropyLossWeight,...
'DiscountFactor', DiscountFactor);
if want_parallel == true %set up for A3C agent
agentOptions.NumStepsToLookAhead = NumStepsToLookAhead;
else %set up for normal AC agent
agentOptions.NumStepsToLookAhead = steps_per_episode; %MaxEpisodes;
end
agent = rlACAgent(actor, critic, agentOptions);
%% Setup Training for Agent
saved_agent_name = strcat('trained_quadcopter_', agentName, '_agent');
SaveAgentDirectory = fullfile(agent_folder,saved_agent_name);
trainOpts = rlTrainingOptions(...
'MaxEpisodes', MaxEpisodes,...
'MaxStepsPerEpisode', steps_per_episode,...
'Verbose', false,...
'Plots','training-progress',...
'StopOnError', "off",...
'StopTrainingCriteria', StopTrainingCriteria,...
'StopTrainingValue', StopTrainingValue,...
'ScoreAveragingWindowLength', ScoreAveragingWindowLength, ...
'SaveAgentDirectory', SaveAgentDirectory);
if want_parallel == true
trainOpts.UseParallel = true;
trainOpts.ParallelizationOptions.Mode = "async";
trainOpts.ParallelizationOptions.DataToSendFromWorkers = "gradients";
trainOpts.ParallelizationOptions.StepsUntilDataIsSent = ...
agentOptions.NumStepsToLookAhead;
end
%% Train the Agent
if doTraining == true
disp('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
disp('Starting the training!!!!!')
disp('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
trainingStats = train(agent,env,trainOpts);
if saveFinalAgent == true
save(trainOpts.SaveAgentDirectory,'agent')
figure()
plot(trainingStats.EpisodeIndex, trainingStats.EpisodeReward,...
'--','Color',[0.3010 0.7450 0.9330])
hold on
plot(trainingStats.EpisodeIndex, trainingStats.AverageReward,...
':','Color',[0 0.4470 0.7410], 'LineWidth',2)
plot(trainingStats.EpisodeIndex, trainingStats.EpisodeQ0,...
'-x','Color',[0.9290 0.6940 0.1250])
hold off
xlabel('Episode Number')
ylabel('Episode Reward')
title(strcat('Reward Training History for ', agentName, ' Agent'))
legend('Average Reward', 'Episode Reward', 'Episode Q0',...
'location', 'northwest')
image_file = strcat('TrainingHistory_', agentName, '.png');
image_save_path = fullfile(image_folder,image_file);
set(gcf,'position',[50,50,1200,400])
saveas(gcf,image_save_path)
end
disp(' ')
disp('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
disp('Training Complete!!!!!')
disp('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
else
disp('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
disp('Loading the trained agent!!!!!')
disp('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
load(trainOpts.SaveAgentDirectory,'agent')
end
%% Simulate and save the results
disp(' ')
disp('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
disp('Simulating the Quadcopter')
disp('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
disp(' ')
if doSim == true %%% need to update this
simOptions = rlSimulationOptions('MaxSteps',...
trainOpts.MaxStepsPerEpisode);
totalReward = zeros(num_sims,1);
num_steps = zeros(num_sims,1);
for i = 1:num_sims
experience = sim(env,agent,simOptions);
totalReward(i) = sum(experience.Reward);
num_steps(i) = length(experience.Reward.Time);
end
avg_Reward = mean(totalReward);
avg_steps = mean(num_steps);
disp(['Average Reward for ', num2str(num_sims), ...
' Simulated Episode = ',num2str(avg_Reward)])
disp(['Average Number of Steps for ', num2str(num_sims), ...
' Simulated Episode = ',num2str(avg_steps)])
pos_sim = experience.Observation.QuadcopterStates.Data(1:3,:);
x_sim = experience.Observation.QuadcopterStates.Data(1,:);
y_sim = experience.Observation.QuadcopterStates.Data(2,:);
z_sim = experience.Observation.QuadcopterStates.Data(3,:);
total_pos_sim = zeros(1,size(pos_sim,2));
for i = 1:size(pos_sim,2)
total_pos_sim(i) = norm(pos_sim(:,i));
end
figure()
subplot(1,3,1)
plot(total_pos_sim)
xlabel('Time (s)')
ylabel('Absolute Value of Position Vector (m)')
title('Magnitude of Displacement')
subplot(1,3,2)
plot(x_sim,y_sim)
xlabel('X-Direction Displacement (m)')
ylabel('Y-Direction Displacement (m)')
title('Lateral Displacement')
subplot(1,3,3)
plot(z_sim)
xlabel('Time (s)')
ylabel('Z-Direction Displacement (m)')
title('Vertical Displacement')
image_file = strcat('TrainingSample_', agentName, '.png');
image_save_path = fullfile(image_folder,image_file);
set(gcf,'position',[50,50,1200,400])
saveas(gcf,image_save_path)
end
%display total time to complete tasks
tElapsed = toc(start_time);
hour=floor(tElapsed/3600);
tRemain = tElapsed - hour*3600;
min=floor(tRemain/60);
sec = tRemain - min*60;
disp(' ')
disp(['Time to complete: ',num2str(hour),' hours, ',num2str(min),...
' minutes, ',num2str(sec),' seconds'])