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aprol.cpp
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#include <csignal>
#include <iostream>
#include <fstream>
#include <sstream>
#include <string>
#include <vector>
#include <map>
#include <assert.h>
#include <stdio.h>
#include <math.h>
#include <chrono>
#include <boost/filesystem.hpp>
#include <robot_dart/robot_dart_simu.hpp>
#include <robot_dart/control/pd_control.hpp>
// #include <robot_dart/control/hexa_control.hpp>
#include <hexa_control.hpp>
#include <limbo/limbo.hpp>
#include <map_elites/binary_map.hpp> // to load in archive_file (behavioural repertoire) from MAPELITES
#include <mcts/uct.hpp>
#include <svg/simple_svg.hpp>
#include <boost/program_options.hpp>
#include <astar/a_star.hpp>
#include <algorithm>
#include <robot_dart/gui/magnum/graphics.hpp>
#include <robot_dart/gui/magnum/windowless_graphics.hpp>
#include <dart/collision/bullet/BulletCollisionDetector.hpp>
#include <dart/constraint/ConstraintSolver.hpp>
#include"hbr_loading.hpp"
#include <hbr/Leg/fit_hexa_leg.hpp>
#include <hbr/Body_Acc/fit_hexa_body_gp.hpp>
#ifdef BASE_HBR
#include <hbr/Omnidirectional/fit_hexa_body.hpp>
#else
#include <hbr/Omnidirectional/fit_mt_me.hpp>
#endif
#include <hbr/HBR/quality_diversity_hbr.hpp>
#define ARCHIVE_SIZE 2
#define NUM_THREADS 32
using namespace limbo ; // for limbo objects and functions dont have to keep typing limbo::
template <typename T> // gaussaion_used in HexaState.move -
inline T gaussian_rand(T m = 0.0, T v = 1.0)
{
static std::random_device rd;
static std::mt19937 gen(rd());
std::normal_distribution<T> gaussian(m, v);
return gaussian(gen);
}// gaussian_rand
// b-a - function that obtains angle difference between the arrival_angle and the desired angle, used to compute the performance/fitness - used in HexaState
double angle_dist(double a, double b)
{
double theta = b - a;
while (theta < -M_PI)
theta += 2 * M_PI;
while (theta > M_PI)
theta -= 2 * M_PI;
return theta;
}// angle_dist
struct Params {
struct uct {
MCTS_DYN_PARAM(double, c);
};
struct spw {
MCTS_DYN_PARAM(double, a);
};
struct cont_outcome {
MCTS_DYN_PARAM(double, b);
};
struct mcts_node {
MCTS_DYN_PARAM(size_t, parallel_roots);
};
struct active_learning {
MCTS_DYN_PARAM(double, k);
MCTS_DYN_PARAM(double, scaling);
};
MCTS_DYN_PARAM(double, goal_x);
MCTS_DYN_PARAM(double, goal_y);
MCTS_DYN_PARAM(double, goal_theta);
MCTS_DYN_PARAM(double, iterations);
MCTS_DYN_PARAM(bool, learning);
MCTS_DYN_PARAM(size_t, collisions);
MCTS_PARAM(double, threshold, 1e-2);
MCTS_PARAM(double, cell_size, 0.5);
MCTS_PARAM(double, robot_radius, 0.25); //hexpod radius maximum value
struct kernel_exp {
BO_PARAM(double, l,0.03);
BO_PARAM(double, sigma_sq, 0.01);
}; //kernel_exp
struct kernel : public defaults::kernel {
BO_PARAM(double, noise, 0.001);
}; // kernel - from new version of limbo
struct archiveparams { // used to extract and store the data from the archive file from MAPELITES
struct elem_archive {
double x, y, cos_theta, sin_theta; //extracts and store behaviroual descriptor and performance
std::vector<double> controller; //to store the controller (motor parameters corresponding to the behaviorual descriptor
}; //elem_archive
struct classcomp {
bool operator()(const std::vector<double>& lhs, const std::vector<double>& rhs) const
{
assert(lhs.size() == ARCHIVE_SIZE && rhs.size() == ARCHIVE_SIZE);
int i = 0;
while (i < (ARCHIVE_SIZE - 1) && std::round(lhs[i] * 1000) == std::round(rhs[i] * 1000)) //lhs[i]==rhs[i])
i++;
return std::round(lhs[i] * 1000) < std::round(rhs[i] * 1000); //lhs[i]<rhs[i];
}
}; //classcomp
struct classcompequal {
bool operator()(const Eigen::VectorXd& lhs, const Eigen::VectorXd& rhs) const
{
assert(lhs.size() == ARCHIVE_SIZE && rhs.size() == ARCHIVE_SIZE);
int i = 0;
while (i < (ARCHIVE_SIZE - 1) && std::round(lhs[i] * 1000) == std::round(rhs[i] * 1000)) //lhs[i]==rhs[i])
i++;
return std::round(lhs[i] * 1000) == std::round(rhs[i] * 1000); //lhs[i]<rhs[i];
}
}; //classcompequal
using archive_t = std::map<std::vector<double>, elem_archive, classcomp>;
static archive_t archive;
static std::vector<archive_t> archives;
}; //archive_params
}; //Params
template <typename Params,int* PtrIndex>
struct MeanArchive {
MeanArchive(size_t dim_out = 4)
{
}
template <typename GP>
Eigen::VectorXd operator()(const Eigen::VectorXd& v, const GP&) const
{
Eigen::VectorXd r(4);
std::vector<double> vv(v.size(), 0.0);
Eigen::VectorXd::Map(&vv[0], v.size()) = v;
typename Params::archiveparams::elem_archive elem = Params::archiveparams::archives[*PtrIndex][vv];
// if(*PtrIndex ==1)
// std::cout<<"Index Map "<<*PtrIndex<<std::endl;
r << elem.x, elem.y, elem.cos_theta, elem.sin_theta;
// std::cout<<"Index Map "<<*PtrIndex<<" "<<r.transpose()<<std::endl;
return r;
}
}; // MeanArchive
struct SimpleObstacle { // struct for generating obtaining the position of the obstacles from the map
double _x, _y, _radius, _radius_sq;
SimpleObstacle(double x, double y, double r = 0.1) : _x(x), _y(y), _radius(r), _radius_sq(r * r) {}
}; //SimpleObstacle
struct BoxObstacle { // struct for generating obtaining the position of the obstacles from the map
double start_x, start_y, end_x, end_y, _height;
Eigen::Vector3d size(){
Eigen::Vector3d final_size;
final_size << end_x-start_x,end_y-start_y,_height;
return final_size;
}
Eigen::Vector6d pose(){
Eigen::Vector6d final_pose;
//center of box is used to place the box!
final_pose << 0.0,0.0,0.0,(end_x+start_x)/2,(end_y+start_y)/2,0;
return final_pose;
}
double width(){
if(start_x<end_x)
return (end_x-start_x);
else
return (start_x-end_x);
}
double height(){
if(start_y<end_y)
return (end_y-start_y);
else
return (start_y-end_y);
}
double x(){
if(start_x<end_x){
return start_x;
}
return end_x;
}
double y(){
if(start_y<end_y){
return start_y;
}
return end_y;
}
double c_x(){
return (end_x+start_x)/2;
}
double c_y(){
return (end_y+start_y)/2;
}
BoxObstacle(double x1, double y1, double x2, double y2,double height=0.9) : start_x(x1), start_y(y1), end_x(x2), end_y(y2), _height(height) {}
}; //BoxObstacle
bool collides(double x, double y, double r = Params::robot_radius());
namespace global {
std::shared_ptr<robot_dart::Robot> global_robot, simulated_robot; // simulated_robot is the clone of global_robot used for simulation
Eigen::Vector3d robot_pose;
std::vector<SimpleObstacle> obstacles;
std::vector<BoxObstacle> box_obstacles;
size_t height, width, entity_size, map_size, map_size_x, map_size_y;
svg::Dimensions dimensions;
std::shared_ptr<svg::Document> doc;
size_t target_num;
std::vector<std::string> dofs;
robot_dart::RobotDARTSimu simu(0.001);
std::vector<int> prior_map_indices,prior_map_trials;
std::vector<double> prior_map_means;
int latest_map_index = 0;
int* index_ptr = &latest_map_index;
std::vector<std::string> archive_files;
int n_policies_called = 0;//reset of number of action sampling calls
int n_random_policies_called = 0;//
double time_random_search = 0.0;
using kernel_t = kernel::Exp<Params>; // kernel is under namespace limbo in limbo.hpp - limbo::kernel::Exp
using mean_t = MeanArchive<Params,&latest_map_index>; // MeanArchive is written above
using GP_t = model::GP<Params, kernel_t, mean_t>; //model is under namespace limbo in limbo.hpp - limbo::model::GP
// GP_t gp_model(ARCHIVE_SIZE, 4);
std::vector<GP_t> gp_models;
// statistics
std::ofstream robot_file, ctrl_file, iter_file, misc_file;
int damaged_leg;
} //namespace global
// Stat GP - to save/write the gp.dat - used in reach_targets function
void write_gp(std::string filename)
{
std::ofstream ofs;
ofs.open(filename);
typedef typename Params::archiveparams::archive_t::const_iterator archive_it_t;
for (archive_it_t it = Params::archiveparams::archive.begin(); it != Params::archiveparams::archive.end(); it++) {
Eigen::VectorXd desc = Eigen::VectorXd::Map(it->first.data(), it->first.size());
Eigen::VectorXd mu;
double sigma;
int prior_map_index = global::prior_map_indices.back();
global::latest_map_index = prior_map_index;
std::tie(mu, sigma) = global::gp_models[prior_map_index].query(desc);
ofs << desc.transpose() << " "
<< mu.transpose() << " "
<< sigma << std::endl;
}
ofs.close();
} //write-gp
std::shared_ptr<robot_dart::Robot> box(size_t num, double x_pos, double y_pos, double x_dim, double y_dim, double height)
{
// random pose
Eigen::Vector6d pose = Eigen::Vector6d::Random();
// make sure it is above the ground
pose(0) = 0 ;
pose(1) = 0 ;
pose(2) = 0 ;
pose(3) = x_pos ;
pose(4) = y_pos ;
pose(5) = 0 ;
// random size
Eigen::Vector3d size = Eigen::Vector3d::Random().array() * Eigen::Vector3d(0.1, 0.2, 0.1).array() + 0.3;
size(0) = x_dim ;
size(1) = y_dim ;
size(2) = height ;
//red coloured box - create_box function found in robot_dart robot.cpp file
//return robot_dart::Robot::create_box(size, pose, "free", 1., dart::Color::Red(1.0), "box_" + std::to_string(num)); //"free" - the block can be pushed around and can fly
return robot_dart::Robot::create_box(size, pose, "fixed", 1., dart::Color::Red(1.0), "box_" + std::to_string(num)); //"fixed" - fixed at its position. not free to move.
} //box
std::shared_ptr<robot_dart::Robot> sphere(size_t num = 0)
{
// random pose
Eigen::Vector6d pose = Eigen::Vector6d::Random();
// make sure it is above the ground
pose(5) = 1.0;
// random size
Eigen::Vector3d size = Eigen::Vector3d::Random()[0] * Eigen::Vector3d(0.2, 0.2, 0.2).array() + 0.3;
// blue colored sphere - create_ellipsoid function found in robot_dart robot.cpp file
return robot_dart::Robot::create_ellipsoid(size, pose, "free", 1., dart::Color::Blue(1.0), "sphere_" + std::to_string(num));
} // sphere
// bool collides(double x, double y, double r)
// {
// for (size_t i = 0; i < global::obstacles.size(); i++) {
// double dx = x - global::obstacles[i]._x;
// double dy = y - global::obstacles[i]._y;
// if (std::sqrt(dx * dx + dy * dy) <= global::obstacles[i]._radius + r) {
// //std::cout<< x <<" ; "<< y<<" vs "<< global::obstacles[i]._x<<" ; "<< global::obstacles[i]._y<<std::endl;
// return true;
// }
// }
// return false;
// } // collides
//collision version for boxes
bool collides(double x, double y,double r)
{
double distance_x = 0,distance_y=0, cDist_sq = 0;
for (size_t i = 0; i < global::box_obstacles.size(); i++) {
distance_x = abs(x - (global::box_obstacles[i].c_x()));
distance_y = abs(y - (global::box_obstacles[i].c_y()));
// std::cout<<global::box_obstacles[i].c_x()<<" "<<global::box_obstacles[i].c_y()<<" "<<x<<" "<<y<<std::endl;
// std::cout<<distance_x<<" "<<distance_y<<" "<<global::box_obstacles[i].width()/2<<" "<<(global::box_obstacles[i].height()/2)<<std::endl;
if (distance_x > (global::box_obstacles[i].width()/2 + r)) { continue; }
if (distance_y > (global::box_obstacles[i].height()/2 + r)) { continue; }
if (distance_x <= (global::box_obstacles[i].width()/2)) { return true; }
if (distance_y <= (global::box_obstacles[i].height()/2)) { return true; }
cDist_sq = std::pow((distance_x - (global::box_obstacles[i].width()/2)),2) + std::pow((distance_y - (global::box_obstacles[i].height()/2)),2);
if(cDist_sq <= (r*r)){
// std::cout<<"Circle "<<cDist_sq<<" "<<(r*r)<<std::endl;
// std::cout<<"Here : True 3"<<std::endl;
return true;
}
// if( (x-radius < global::box_obstacles[i].x() + global::box_obstacles[i].width() &&
// x-radius > global::box_obstacles[i].x() &&
// y-radius < global::box_obstacles[i].y() + global::box_obstacles[i].height() &&
// y-radius > global::box_obstacles[i].y()) ||
// ( x+radius < global::box_obstacles[i].x() + global::box_obstacles[i].width() &&
// x+radius > global::box_obstacles[i].x() &&
// y+radius < global::box_obstacles[i].y() + global::box_obstacles[i].height() &&
// y+radius > global::box_obstacles[i].y()))
// {
// return true;
// }
}
return false;
} // collides
// bool collides_segment(const Eigen::Vector2d& start, const Eigen::Vector2d& end)
// {
// const double epsilon = 1e-6;
// double Dx = end(0) - start(0);
// double Dy = end(1) - start(1);
// for (size_t i = 0; i < global::obstacles.size(); i++) {
// double Fx = start(0) - global::obstacles[i]._x;
// double Fy = start(1) - global::obstacles[i]._y;
// double a = Dx * Dx + Dy * Dy;
// double b = 2.0 * (Fx * Dx + Fy * Dy);
// double c = (Fx * Fx + Fy * Fy) - global::obstacles[i]._radius_sq;
// double discriminant = b * b - 4.0 * a * c;
// if (discriminant > epsilon) {
// discriminant = std::sqrt(discriminant);
// double t1 = (-b - discriminant) / (2.0 * a);
// double t2 = (-b + discriminant) / (2.0 * a);
// if (t1 > epsilon && (t1 - 1.0) < epsilon) {
// return true;
// }
// if (t2 > epsilon && (t2 - 1.0) < epsilon) {
// return true;
// }
// }
// }
// return false;
// } // collides_segment
bool intersect_segment(double x1, double y1,double x2, double y2, double x3, double y3,double x4, double y4){
double t = (((x1-x3)*(y3-y4))-((y1-y3)*(x3-x4)))/(((x1-x2)*(y3-y4))-((y1-y2)*(x3-x4)));
double u = (((x1-x3)*(y1-y2))-((y1-y3)*(x1-x2)))/(((x1-x2)*(y3-y4))-((y1-y2)*(x3-x4)));
if(u<1 && u>0 && t<1 && t>0){
return true;
}
return false;
}
//collision version for boxes
bool collides_segment(const Eigen::Vector2d& start, const Eigen::Vector2d& end)
{
for (size_t i = 0; i < global::box_obstacles.size(); i++) {
bool l1 = intersect_segment(global::box_obstacles[i].start_x,global::box_obstacles[i].start_y,global::box_obstacles[i].start_x,global::box_obstacles[i].end_y, start(0),start(1), end(0),end(1));
bool l2 = intersect_segment(global::box_obstacles[i].start_x,global::box_obstacles[i].start_y,global::box_obstacles[i].end_x,global::box_obstacles[i].start_y, start(0),start(1), end(0),end(1));
bool l3 = intersect_segment(global::box_obstacles[i].end_x,global::box_obstacles[i].end_y,global::box_obstacles[i].end_x,global::box_obstacles[i].start_y, start(0),start(1), end(0),end(1));
bool l4 = intersect_segment(global::box_obstacles[i].end_x,global::box_obstacles[i].end_y,global::box_obstacles[i].start_x,global::box_obstacles[i].end_y, start(0),start(1), end(0),end(1));
if (l1||l2||l3||l4){
return true;
}
}
return false;
} // collides_box_segment
// bool collides(const Eigen::Vector2d& start, const Eigen::Vector2d& end, double r = Params::robot_radius())
// {
// Eigen::Vector2d dir = end - start;
// Eigen::Vector2d perp = Eigen::Vector2d(-dir(1), dir(0));
// Eigen::Vector2d A = start + perp * r;
// Eigen::Vector2d B = end + perp * r;
// Eigen::Vector2d C = end - perp * r;
// Eigen::Vector2d D = start - perp * r;
// return (collides(start(0), start(1), r) || collides(end(0), end(1), r) || collides_segment(A, B) || collides_segment(B, C) || collides_segment(C, D) || collides_segment(D, A));
// } //collides
//collision version for boxes
bool collides(const Eigen::Vector2d& start, const Eigen::Vector2d& end, double r = Params::robot_radius())
{
Eigen::Vector2d dir = end - start;
Eigen::Vector2d perp = Eigen::Vector2d(-dir(1), dir(0));
Eigen::Vector2d A = start + perp * r;
Eigen::Vector2d B = end + perp * r;
Eigen::Vector2d C = end - perp * r;
Eigen::Vector2d D = start - perp * r;
return (collides(start(0), start(1),r) || collides(end(0), end(1),r) || collides_segment(A, B) || collides_segment(B, C) || collides_segment(C, D) || collides_segment(D, A));
} //collides
bool astar_collides(int x, int y, int x_new, int y_new) // used in Default Policy
{
Eigen::Vector2d s(x * Params::cell_size(), y * Params::cell_size());
Eigen::Vector2d t(x_new * Params::cell_size(), y_new * Params::cell_size());
return collides(s, t, Params::robot_radius() * 1.3);
// return collides_box(s, t);
} //astar_collides
template <typename State, typename Action>
struct DefaultPolicy {
// Action operator()(const std::shared_ptr<State>& state)
Action operator()(const State* state, bool draw = false)
{
global::n_policies_called +=1;
size_t N = 100;
double dx = state->_x - Params::goal_x();
double dy = state->_y - Params::goal_y();
double d = dx * dx + dy * dy;
if (d <= Params::cell_size() * Params::cell_size()) {
Action best_action;
double best_value = std::numeric_limits<double>::max();
for (size_t i = 0; i < N; i++) {
Action act = state->random_action();
auto final = state->move(act, true);
double dx = final._x - Params::goal_x();
double dy = final._y - Params::goal_y();
double val = dx * dx + dy * dy;
if (collides(final._x, final._y))
val = std::numeric_limits<double>::max();
if (best_action._desc.size() == 0 || val < best_value) {
//std::cout<<"DEPOL:operator " <<std::endl;
best_value = val;
best_action = act;
}
}
//std::cout<<"DEPOL1 "<< best_action._desc.size()<<std::endl;
return best_action;
}
astar::AStar<> a_star;
astar::Node ss(std::round(state->_x / Params::cell_size()), std::round(state->_y / Params::cell_size()), global::map_size_x, global::map_size_y);
if (collides(ss._x * Params::cell_size(), ss._y * Params::cell_size()) || ss._x <= 0 || ss._x >= int(global::map_size_x) || ss._y <= 0 || ss._y >= int(global::map_size_y)) {
astar::Node best_root = ss;
bool changed = false;
double val = std::numeric_limits<double>::max();
for (int i = -1; i <= 1; i++) {
for (int j = -1; j <= 1; j++) {
if (i == 0 && j == 0)
continue;
int x_new = ss._x + i;
int y_new = ss._y + j;
double dx = x_new * Params::cell_size() - state->_x;
double dy = y_new * Params::cell_size() - state->_y;
double v = dx * dx + dy * dy;
if (!collides(x_new * Params::cell_size(), y_new * Params::cell_size()) && x_new > 0 && x_new < int(global::map_size_x) && y_new > 0 && y_new < int(global::map_size_y) && v < val) {
best_root._x = x_new;
best_root._y = y_new;
val = v;
changed = true;
}
}
}
if (!changed)
state->random_action();
ss = best_root;
}
astar::Node ee(std::round(Params::goal_x() / Params::cell_size()), std::round(Params::goal_y() / Params::cell_size()), global::map_size_x, global::map_size_y);
auto path = a_star.search(ss, ee, astar_collides, global::map_size_x, global::map_size_y);
if (path.size() < 2) {
// std::cout << "Error: A* path size less than 2: " << path.size() << ". Returning random action!" << std::endl;
return state->random_action();
}
// std::cout<<"Here1"<<std::endl;
astar::Node best = path[1];
// Find best action
Eigen::Vector2d best_pos(best._x * Params::cell_size(), best._y * Params::cell_size());
Eigen::Vector2d init_vec = Eigen::Vector2d(state->_x, state->_y);
// First check if we have clean path
if (path.size() >= 3 && (best_pos - init_vec).norm() < Params::cell_size()) {
Eigen::Vector2d new_pos(path[2]._x * Params::cell_size(), path[2]._y * Params::cell_size());
if (!collides(init_vec, new_pos, 1.5 * Params::robot_radius())) {
best = path[2];
best_pos = new_pos;
}
}
// We are going to just use the best from sampled actions
// Put best position better in space (to avoid collisions) if we are not in a safer region
if (collides(best_pos(0), best_pos(1), 2.0 * Params::robot_radius())) {
SimpleObstacle closest_obs(0, 0, 0);
double closet_dist = std::numeric_limits<double>::max();
for (auto obs : global::obstacles) {
double dx = best_pos(0) - obs._x;
double dy = best_pos(1) - obs._y;
double v = dx * dx + dy * dy;
if (v < closet_dist) {
closet_dist = v;
closest_obs = obs;
}
}
Eigen::Vector2d dir_to_obs = best_pos - Eigen::Vector2d(closest_obs._x, closest_obs._y);
Eigen::Vector2d new_best = best_pos;
double step = Params::cell_size();
size_t n = 0;
bool b = false;
do {
new_best = best_pos.array() + (n + 1) * step * dir_to_obs.array();
n++;
b = collides(new_best(0), new_best(1), 2.0 * Params::robot_radius());
// if (b) {
// svg::Circle circle_target(svg::Point(new_best(1) * global::entity_size, new_best(0) * global::entity_size), Params::cell_size() * global::entity_size, svg::Fill(), svg::Stroke(2, svg::Color::Red));
// (*global::doc) << circle_target;
// }
// else {
// svg::Circle circle_target(svg::Point(new_best(1) * global::entity_size, new_best(0) * global::entity_size), Params::cell_size() * global::entity_size, svg::Fill(), svg::Stroke(2, svg::Color::Blue));
// (*global::doc) << circle_target;
// }
} while (b && n < 10);
// svg::Polyline polyline_clean(svg::Stroke(1.0, svg::Color::Black));
// polyline_clean << svg::Point(closest_obs._y * global::entity_size, closest_obs._x * global::entity_size);
// polyline_clean << svg::Point(best_pos(1) * global::entity_size, best_pos(0) * global::entity_size);
// (*global::doc) << polyline_clean;
//
// std::cout << "n: " << n << std::endl;
if (n < 10)
best_pos = new_best;
}
// if (draw) {
// svg::Circle circle_target(svg::Point(best_pos(1) * global::entity_size, best_pos(0) * global::entity_size), Params::cell_size() * global::entity_size, svg::Fill(), svg::Stroke(2, svg::Color::Green));
// (*global::doc) << circle_target;
// }
Action best_action;
double best_value = std::numeric_limits<double>::max();
for (size_t i = 0; i < N; i++) {
Action act = state->random_action();
auto final = state->move(act, true);
double dx = final._x - best_pos(0);
double dy = final._y - best_pos(1);
double val = dx * dx + dy * dy;
if (collides(final._x, final._y))
{
val = std::numeric_limits<double>::max();
}
if (best_action._desc.size()==0 || val < best_value) {
best_value = val;
best_action = act;
}
}
return best_action;
}
};//Default Policy
template <typename Params>
struct HexaAction {
Eigen::VectorXd _desc;
HexaAction() {}
HexaAction(Eigen::VectorXd desc) : _desc(desc) {}
bool operator==(const HexaAction& other) const
{
return (typename Params::archiveparams::classcompequal()(_desc, other._desc));
}
};
template <typename Params>
struct HexaState {
double _x, _y, _theta;
static constexpr double _epsilon = 1e-3;
HexaState()
{
_x = _y = _theta = 0;
}
HexaState(double x, double y, double theta)
{
_x = x;
_y = y;
_theta = theta;
}
bool valid(const HexaAction<Params>& act) const
{
if(act._desc.size()==0)
{
std::cout<<"INVALID DESC"<<std::endl;
return false;
}
// HexaState<Params> tmp = move(act);
// // Check if state is outside of bounds
// if (tmp._x < 0.0 || tmp._x > global::map_size * Params::cell_size() || tmp._y < 0.0 || tmp._y > global::map_size * Params::cell_size())
// return false;
//
// if (collides(tmp._x, tmp._y) || collides(Eigen::Vector2d(_x, _y), Eigen::Vector2d(tmp._x, tmp._y)))
// return false;
return true;
}
HexaAction<Params> next_action() const
{
return DefaultPolicy<HexaState<Params>, HexaAction<Params>>()(this);
}
HexaAction<Params> random_action() const
{
global::n_random_policies_called +=1;
tools::rgen_int_t rgen(0, Params::archiveparams::archives[global::latest_map_index].size() - 1);
typedef typename Params::archiveparams::archive_t::const_iterator archive_it_t;
HexaAction<Params> act;
double time_running = 0.0;
do {
auto t1 = std::chrono::steady_clock::now();
archive_it_t it = Params::archiveparams::archives[global::latest_map_index].begin();
std::advance(it, rgen.rand());
time_running = std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::steady_clock::now() - t1).count();
try{
Eigen::VectorXd desc = Eigen::VectorXd::Map(it->first.data(), it->first.size());
act._desc = desc;
}
catch (const std::bad_alloc& e) {
std::cout << "Allocation failed: " << e.what()<<" "<<it->first.size()<<" Map Index: "<<global::latest_map_index<< '\n';
}
} while (!valid(act));
global::time_random_search += time_running;
return act;
}
HexaState move(const HexaAction<Params>& action, bool no_noise = false) const
{
double x_new, y_new, theta_new;
Eigen::VectorXd mu;
double sigma;
int prior_map_index = global::prior_map_indices.back();
global::latest_map_index = prior_map_index;
std::tie(mu, sigma) = global::gp_models[prior_map_index].query(action._desc);
/*
std::ofstream ofs;
ofs.open("exp/rte_v2_adapt/visualise_MCTS/move_gp.dat", std::ios_base::app);
ofs << action._desc.transpose() << " "
<< mu.transpose() << " "
<< sigma << std::endl;
ofs.close();
*/
if (!no_noise) {
// std::cout << mu.transpose() << std::endl;
mu(0) = std::max(-1.5, std::min(1.5, gaussian_rand(mu(0), sigma)));
mu(1) = std::max(-1.5, std::min(1.5, gaussian_rand(mu(1), sigma)));
mu(2) = std::max(-1.0, std::min(1.0, gaussian_rand(mu(2), sigma)));
mu(3) = std::max(-1.0, std::min(1.0, gaussian_rand(mu(3), sigma)));
//std::cout << "WITH NOISE - To: " << mu.transpose() << std::endl;
}
double theta = std::atan2(mu(3), mu(2));
double c = std::cos(_theta), s = std::sin(_theta);
Eigen::MatrixXd tr(3, 3);
tr << c, -s, _x, s, c, _y, 0, 0, 1;
Eigen::Vector3d tr_pos;
tr_pos << mu(0), mu(1), 1.0;
tr_pos = tr * tr_pos;
x_new = tr_pos(0);
y_new = tr_pos(1);
theta_new = _theta + theta;
while (theta_new < -M_PI)
theta_new += 2 * M_PI;
while (theta_new > M_PI)
theta_new -= 2 * M_PI;
///std::cout << "(" << _x << "," << _y << "," << _theta << ") with (" << mu(0) << "," << mu(1) << "," << mu(2) << ") -> (" << x_new << "," << y_new << "," << theta_new << ")" << std::endl;
return HexaState(x_new, y_new, theta_new);
}
bool terminal() const
{
// Check if state is outside of bounds
if (_x < 0.0 || _x >= global::map_size_x * Params::cell_size() || _y < 0.0 || _y >= global::map_size_y * Params::cell_size())
{
//std::cout<<"--A--"<<std::endl;
//std::cout<<"_x "<< _x<<std::endl;
//std::cout<<"_y "<< _y<<std::endl;
//std::cout<<" global::map_size_x * Params::cell_size() "<< global::map_size_x * Params::cell_size()<<std::endl;
//std::cout<<" global::map_size_y * Params::cell_size() "<< global::map_size_y * Params::cell_size()<<std::endl;
return true;
}
// Check if state is goal
if (goal())
{
//std::cout<<"--B--"<<std::endl;
return true;
}
// Check if state is colliding
if (collides(_x, _y))
{
//std::cout<<"--C--"<<std::endl;
return true;
}
return false;
}
bool goal() const
{
double dx = _x - Params::goal_x();
double dy = _y - Params::goal_y();
double threshold_xy = Params::cell_size() * Params::cell_size() / 4.0;
if ((dx * dx + dy * dy) < threshold_xy)
return true;
return false;
}
bool operator==(const HexaState& other) const
{
double dx = _x - other._x;
double dy = _y - other._y;
double dth = std::abs(angle_dist(other._theta, _theta));
return ((dx * dx + dy * dy) < (Params::cell_size() * Params::cell_size() / 4.0) && dth < 0.3);
}
}; //HexaState
struct RewardFunction {
template <typename State>
double operator()(std::shared_ptr<State> from_state, HexaAction<Params> action, std::shared_ptr<State> to_state)
{
// Check if state is outside of bounds
if (to_state->_x < 0.0 || to_state->_x >= global::map_size_x * Params::cell_size() || to_state->_y < 0.0 || to_state->_y >= global::map_size_y * Params::cell_size())
return -1000.0;
// Return values
if (collides(to_state->_x, to_state->_y) || collides(Eigen::Vector2d(from_state->_x, from_state->_y), Eigen::Vector2d(to_state->_x, to_state->_y)))
return -1000.0;
if (to_state->goal())
return 100.0;
return 0.0;
}
}; // RewardFunction
std::map<std::vector<double>, Params::archiveparams::elem_archive, Params::archiveparams::classcomp> load_archive(std::string archive_name)
{
std::map<std::vector<double>, Params::archiveparams::elem_archive, Params::archiveparams::classcomp> archive;
size_t lastindex = archive_name.find_last_of(".");
std::string extension = archive_name.substr(lastindex + 1);
std::cout << "Loading text file..."<<archive_name.c_str() << std::endl;
std::ifstream monFlux(archive_name.c_str());
if (monFlux) {
std::string line;
while (std::getline(monFlux, line)) {
std::istringstream iss(line);
std::vector<double> numbers;
double num;
while (iss >> num) {
numbers.push_back(num);
}
// if (numbers.size() < 39) //36+2+1
// continue;
//std::cout << "HERE1" << std::endl;
int init_i = 0;
if (numbers.size() > 39)
init_i = 1;
// for(auto n: numbers){
// std::cout<<n<<" ";
// }
// std::cout<<std::endl;
//std::cout << "HERE2" << std::endl;
Params::archiveparams::elem_archive elem;
std::vector<double> candidate(ARCHIVE_SIZE);
int size_data = 45;//36+8+1
#ifdef HBR
{
size_data = 7;
init_i = 1;
}
#endif
for (int i = 0; i < size_data; i++) {
// std::cout << "HERE3" << std::endl;
if(ARCHIVE_SIZE == 8){
double data = numbers[init_i + i];
if (i == 0) {
candidate[i] = data;
elem.x = (data*3)-1.5 ;
}
if (i == 1) {
candidate[i] = data;
elem.y = (data*3)-1.5 ;
}
if (i<8){
candidate[i] = data;
}
if (i == 8) {
elem.cos_theta = std::cos(data) ;
elem.sin_theta = std::sin(data) ;
}
if (i >= 9){
elem.controller.push_back(data);
}
if (elem.controller.size() == 36) {
archive[candidate] = elem;
}
}
else{
double data = numbers[init_i + i];
if (i == 0) {
#ifndef HBR
// candidate[i] = (data-0.5)*2;
// elem.x = (data-0.5)*2 ;
candidate[i] = data;
elem.x = (data*3)-1.5;
#else
candidate[i] = data;
elem.x = (data*3)-1.5;
#endif
}
if (i == 1) {
#ifndef HBR
// candidate[i] = (data-0.5)*2;
// elem.y = (data-0.5)*2 ;
candidate[i] = data;
elem.y = (data*3)-1.5;
#else
candidate[i] = data;
elem.y = (data*3)-1.5;
#endif
}
if (i == 2) {
elem.cos_theta = std::cos(data) ;
elem.sin_theta = std::sin(data) ;
}
if (i >= 3){
elem.controller.push_back(data);
}
#ifndef HBR
if (elem.controller.size() == 36) {
archive[candidate] = elem;
}
#else
if (elem.controller.size() == 4) {
archive[candidate] = elem;
}
#endif
}
}
}
}
else {
std::cout << "ERROR: Could not load the archive." << std::endl;
return archive;
}
std::cout << archive.size() << " elements loaded" << std::endl;
return archive;
}
//Calulcating the UCB means for all the maps and returning the posterior for all the maps
void update_ucb_posterior(Eigen::VectorXd desc,Eigen::VectorXd observation){
double k = 5;
double closeness = std::exp(-k * (desc-observation).norm() );
global::prior_map_means[global::prior_map_indices.back()] = ((global::prior_map_trials[global::prior_map_indices.back()]-1)*global::prior_map_means[global::prior_map_indices.back()]+closeness)/(global::prior_map_trials[global::prior_map_indices.back()]);
}
Eigen::VectorXd get_ucb_posterior(){
Eigen::VectorXd ucb(global::prior_map_trials.size()),posteriors(global::prior_map_trials.size());
int total_trials = 0;
total_trials = std::accumulate(global::prior_map_trials.begin(), global::prior_map_trials.end(), 0);
for(int i=0;i<ucb.size();i++){
ucb[i] = 0.01 * std::sqrt((2*std::log(total_trials))/global::prior_map_trials[i]);
}
Eigen::VectorXd means = Eigen::VectorXd::Map(global::prior_map_means.data(), global::prior_map_means.size());
posteriors = (means+ucb)/(means+ucb).sum();
return posteriors;
}
double gaussian_likelihood(Eigen::VectorXd mean, double var,Eigen::VectorXd goal){
double likelihood = 0;
//watch out for the normalization!! mu is usually not normalized
for(int m=0;m<mean.size();m++){
likelihood += -0.5*std::log(2*M_PI*var)-(std::pow(((mean[m]+1.5)/3)-goal[m],2)/(2.0*var + 1e-10));
}
// if((mean*3-1.5) == goal)
// std::cout<<"Likelihood "<<likelihood<<" "<<mean.transpose()<<" "<<var<<std::endl;
return std::exp(likelihood);
}
// typename Params::archiveparams::archive_t choose_prior(const Eigen::VectorXd& desc){
std::tuple<int,const Eigen::VectorXd> choose_prior(const Eigen::VectorXd& goal){
//we can use goal to filter the individuals we want to use but our MCTS algorithm is already doing this.
Eigen::VectorXd mu, desc, posteriors, chosen_desc,full_desc;
std::vector<double>::iterator result;
double sigma;
int best_index = -1;
posteriors = get_ucb_posterior();
std::vector<double> scores,best_likelihoods, index_likelihoods;
std::vector<double> prior_probs(Params::archiveparams::archives.size(),1/Params::archiveparams::archives.size());//prior probability to select one repertoire, uniform in our case
for(int a = 0; a<Params::archiveparams::archives.size();a++){
scores.clear();