441 lines
17 KiB
C
441 lines
17 KiB
C
#include "learn_to_drive.h"
|
|
|
|
char *action_name[8] = {"LEFT", "CENTER", "RIGHT"};
|
|
|
|
float reLU(float x){
|
|
// if(x>10) return 10;
|
|
if(x>0) return x;
|
|
return 0;
|
|
}
|
|
|
|
float d_reLU(float x){
|
|
// if (x>10) return 0;
|
|
if (x>0) return 1;
|
|
return 0;
|
|
}
|
|
|
|
float L2(float t, float o){
|
|
return (o - t) * (o - t)/2;
|
|
}
|
|
|
|
float D_L2(float t, float o){
|
|
return (o - t);
|
|
}
|
|
|
|
void copy_weight_in_networks_from_main_to_target(struct networks_qlearning * networks){
|
|
//copy_weight_in_neurons_TYPE_FLOAT(networks->target_net, networks->main_net);
|
|
COPY_NN_ATTRIBUTE_IN_ALL_LAYERS(TYPE_FLOAT,weight_in, networks->target_net, networks->main_net);
|
|
}
|
|
void copy_weight_in_networks_from_main_to_best(struct networks_qlearning * networks){
|
|
//copy_weight_in_neurons_TYPE_FLOAT(networks->best_net, networks->main_net);
|
|
COPY_NN_ATTRIBUTE_IN_ALL_LAYERS(TYPE_FLOAT,weight_in, networks->best_net, networks->main_net);
|
|
}
|
|
|
|
float id(float x){ return x;}
|
|
|
|
float constOne(float x){return 1;}
|
|
|
|
struct networks_qlearning * create_nework_qlearning(
|
|
struct config_layers * config,
|
|
bool randomize, float minR, float maxR, int randomRange,
|
|
size_t nb_prod_thread,
|
|
size_t nb_calc_thread,
|
|
float learning_rate
|
|
){
|
|
struct networks_qlearning *qnets = malloc(sizeof(struct networks_qlearning));
|
|
qnets->config = config;
|
|
|
|
setup_networks_alloutputs_config_TYPE_FLOAT(&(qnets->main_net), config, random, minR, maxR, randomRange);
|
|
setup_networks_alloutputs_config_TYPE_FLOAT(&(qnets->target_net), config, false, minR, maxR, randomRange);
|
|
copy_weight_in_networks_from_main_to_target(qnets);
|
|
|
|
setup_networks_alloutputs_config_TYPE_FLOAT(&(qnets->best_net), config, false, minR, maxR, randomRange);
|
|
copy_weight_in_networks_from_main_to_best(qnets);
|
|
|
|
setup_all_layers_functions_TYPE_FLOAT(qnets->main_net, tensorContractnProdThread_TYPE_FLOAT, tensorProdThread_TYPE_FLOAT, D_L2, L2, reLU, d_reLU);
|
|
setup_all_layers_params_TYPE_FLOAT(qnets->main_net, nb_prod_thread, nb_calc_thread, learning_rate);
|
|
setup_all_layers_functions_TYPE_FLOAT(qnets->target_net, tensorContractnProdThread_TYPE_FLOAT, tensorProdThread_TYPE_FLOAT, D_L2, L2, reLU, d_reLU);
|
|
setup_all_layers_params_TYPE_FLOAT(qnets->target_net, nb_prod_thread, nb_calc_thread, learning_rate);
|
|
setup_all_layers_functions_TYPE_FLOAT(qnets->best_net, tensorContractnProdThread_TYPE_FLOAT, tensorProdThread_TYPE_FLOAT, D_L2, L2, reLU, d_reLU);
|
|
setup_all_layers_params_TYPE_FLOAT(qnets->best_net, nb_prod_thread, nb_calc_thread, learning_rate);
|
|
|
|
// ne pas mettre fonction d'activation à la sortie , i.e: fonction identité : f(x) = x:
|
|
neurons_TYPE_FLOAT *tmpMain = qnets->main_net;
|
|
neurons_TYPE_FLOAT *tmpTarget = qnets->target_net;
|
|
neurons_TYPE_FLOAT *tmpBest = qnets->best_net;
|
|
while(tmpMain){
|
|
if(tmpMain->next_layer == NULL){
|
|
tmpMain->f_act = id;
|
|
tmpMain->d_f_act = constOne;
|
|
tmpTarget->f_act = id;
|
|
tmpTarget->d_f_act = constOne;
|
|
tmpBest->f_act = id;
|
|
tmpBest->d_f_act = constOne;
|
|
}
|
|
tmpMain = tmpMain->next_layer;
|
|
tmpTarget= tmpTarget->next_layer;
|
|
tmpBest = tmpBest->next_layer;
|
|
}
|
|
|
|
|
|
|
|
return qnets;
|
|
|
|
}
|
|
|
|
struct status_qlearning * create_status_qlearning (){
|
|
struct status_qlearning * status_ql = malloc(sizeof(struct status_qlearning));
|
|
|
|
status_ql->list_main_cumul = create_var_list_TYPE_L_INT();
|
|
status_ql->list_target_cumul = create_var_list_TYPE_L_INT();
|
|
status_ql->progress_best_cumul = create_var_list_TYPE_L_INT();
|
|
|
|
status_ql->ending = 0;
|
|
status_ql->mut_ending=malloc(sizeof(pthread_mutex_t));
|
|
pthread_mutex_init(status_ql->mut_ending, NULL);
|
|
|
|
//push_back_list_TYPE_L_INT(status_ql->list_main_cumul, 0);
|
|
//push_back_list_TYPE_L_INT(status_ql->list_target_cumul, 0);
|
|
push_back_list_TYPE_L_INT(status_ql->progress_best_cumul, -10000);
|
|
|
|
status_ql->nb_training_after_updated_weight_in_target = 0;
|
|
|
|
status_ql->nb_episodes = 0;
|
|
status_ql->index_episode= 0;
|
|
status_ql->action=1;
|
|
|
|
// status_ql->last_action=-1;
|
|
// status_ql->count_last_action=0;
|
|
|
|
return status_ql;
|
|
}
|
|
|
|
struct delay_params * create_delay_params (
|
|
size_t delay_between_episodes,
|
|
size_t delay_between_games
|
|
){
|
|
struct delay_params * delay = malloc(sizeof(struct delay_params));
|
|
delay->delay_between_episodes = delay_between_episodes;
|
|
delay->delay_between_games = delay_between_games;
|
|
|
|
|
|
return delay;
|
|
}
|
|
|
|
struct print_params * create_print_params(float scale_x, float scale_y, struct delay_params * delay){
|
|
struct print_params * pprint = malloc(sizeof(struct print_params));
|
|
pprint->printed = true;
|
|
pprint->scale_x = scale_x;
|
|
pprint->scale_y = scale_y;
|
|
pprint->delay = delay;
|
|
pprint->string_space = malloc(LOG_LENTH+1);
|
|
pthread_mutex_init(&(pprint->mut_printed), NULL);
|
|
|
|
int i;
|
|
for( i=0; i<LOG_LENTH; ++i)
|
|
pprint->string_space[i]=' ';
|
|
pprint->string_space[i]='\0';
|
|
|
|
return pprint;
|
|
}
|
|
|
|
struct qlearning_params * create_qlearning_params (
|
|
float gamma,
|
|
float learning_rate,
|
|
float discount_factor,
|
|
float exploration_factor,
|
|
long int nb_training_before_update_weight_in_target,
|
|
size_t number_episodes
|
|
){
|
|
struct qlearning_params * qparams = malloc(sizeof(struct qlearning_params));
|
|
|
|
qparams->gamma = gamma;
|
|
qparams->learning_rate = learning_rate ;
|
|
qparams->discount_factor = discount_factor ;
|
|
qparams->exploration_factor = exploration_factor ;
|
|
|
|
qparams->nb_training_before_update_weight_in_target = nb_training_before_update_weight_in_target;
|
|
qparams->number_episodes = number_episodes;
|
|
|
|
qparams->factor_update_learning_rate = 0.995;
|
|
qparams->minimum_threshold_learning_rate = 0.0001 ;
|
|
qparams->factor_update_exploration_factor = 0.995;
|
|
qparams->minimum_threshold_exploration_factor = 0.01;
|
|
|
|
// qparams->threshold_number_same_action = 500;
|
|
|
|
return qparams;
|
|
}
|
|
|
|
struct RL_agent * create_RL_agent (
|
|
struct networks_qlearning * networks,
|
|
struct vehicle * car,
|
|
struct status_qlearning * status,
|
|
struct print_params * pprint,
|
|
struct qlearning_params *qlearnParams
|
|
){
|
|
struct RL_agent * rlagent = malloc(sizeof(struct RL_agent));
|
|
|
|
rlagent->networks = networks ;
|
|
rlagent->car = car ;
|
|
rlagent->status = status ;
|
|
rlagent->pprint = pprint ;
|
|
rlagent->qlearnParams = qlearnParams ;
|
|
|
|
return rlagent;
|
|
}
|
|
|
|
void free_networks_qlearning (struct networks_qlearning * networks){
|
|
free_neurons_TYPE_FLOAT(networks->main_net);
|
|
free_neurons_TYPE_FLOAT(networks->target_net);
|
|
free_neurons_TYPE_FLOAT(networks->best_net);
|
|
free_config_layers(networks->config);
|
|
free(networks);
|
|
}
|
|
void free_status_qlearning(struct status_qlearning *status_ql){
|
|
free_all_var_list_TYPE_L_INT(status_ql->list_main_cumul);
|
|
free_all_var_list_TYPE_L_INT(status_ql->list_target_cumul);
|
|
free_all_var_list_TYPE_L_INT(status_ql->progress_best_cumul);
|
|
pthread_mutex_destroy(status_ql->mut_ending);
|
|
free(status_ql->mut_ending);
|
|
free(status_ql);
|
|
}
|
|
void free_delay_params (struct delay_params *dly_p){
|
|
free(dly_p);
|
|
}
|
|
|
|
void free_print_params (struct print_params *pprint){
|
|
free(pprint->string_space);
|
|
pthread_mutex_destroy(&(pprint->mut_printed));
|
|
free_delay_params(pprint->delay);
|
|
free(pprint);
|
|
|
|
}
|
|
|
|
void free_qlearning_params(struct qlearning_params *q_params){
|
|
free(q_params);
|
|
}
|
|
void free_RL_agent(struct RL_agent *rlAgent){
|
|
free(rlAgent->qlearnParams);
|
|
free_print_params(rlAgent->pprint);
|
|
free_status_qlearning(rlAgent->status);
|
|
free_networks_qlearning(rlAgent->networks);
|
|
free_vehicle(rlAgent->car);
|
|
|
|
free(rlAgent);
|
|
}
|
|
|
|
void train_qlearning(struct RL_agent * rlAgent,
|
|
int action //, long reward
|
|
){
|
|
tensor_TYPE_FLOAT * action_value = NULL;
|
|
tensor_TYPE_FLOAT * next_action_value = NULL;
|
|
neurons_TYPE_FLOAT * net_main = rlAgent->networks->main_net;
|
|
neurons_TYPE_FLOAT * net_target = rlAgent->networks->target_net;
|
|
tensor_TYPE_FLOAT * new_state = rlAgent->car->sensor /*input*/;
|
|
tensor_TYPE_FLOAT * state = rlAgent->car->old_sensor /*input*/;
|
|
neurons_TYPE_FLOAT *ttmp = calculate_output_by_network_neurons_TYPE_FLOAT(net_main, state, &action_value);
|
|
calculate_output_by_network_neurons_TYPE_FLOAT(net_target, new_state, &next_action_value);
|
|
tensor_TYPE_FLOAT * experimental_values = CREATE_TENSOR_FROM_CPY_DIM_TYPE_FLOAT(action_value->dim);
|
|
|
|
struct game_status * car_status = rlAgent->car->status;
|
|
struct qlearning_params * qlParams = rlAgent->qlearnParams;
|
|
copy_tensor_TYPE_FLOAT(experimental_values, action_value) ;
|
|
//copy_tensor_TYPE_FLOAT(experimental_values, next_action_value) ;
|
|
// experimental_values === Q-tab learning
|
|
if(car_status->done){
|
|
experimental_values->x[action] = -100;
|
|
}else {
|
|
experimental_values->x[action] = car_status->reward + rlAgent->qlearnParams->gamma * MAX_ARRAY_TYPE_FLOAT(next_action_value->x, next_action_value->dim->rank) ;
|
|
}
|
|
// ***
|
|
copy_tensor_TYPE_FLOAT(ttmp->target, experimental_values);
|
|
while(ttmp != net_main){
|
|
calc_delta_neurons_TYPE_FLOAT(ttmp);
|
|
update_weight_neurons_TYPE_FLOAT(ttmp);
|
|
ttmp = ttmp->prev_layer;
|
|
}
|
|
|
|
// ***
|
|
float new_value = ( (net_main->learning_rate < qlParams->minimum_threshold_learning_rate /*0.0001*/) ? net_main->learning_rate :(net_main->learning_rate ) * qlParams->factor_update_learning_rate /*0.995*/ );
|
|
UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, net_main, learning_rate, new_value);
|
|
|
|
qlParams->exploration_factor = (qlParams->exploration_factor < qlParams->minimum_threshold_exploration_factor) ? qlParams->exploration_factor : qlParams->exploration_factor * qlParams->factor_update_exploration_factor ;
|
|
|
|
// free_tensor_TYPE_FLOAT(action_value);
|
|
// free_tensor_TYPE_FLOAT(next_action_value);
|
|
free_tensor_TYPE_FLOAT(experimental_values);
|
|
|
|
}
|
|
|
|
int select_action(struct RL_agent * rlAgent){
|
|
//static size_t explore = 0;
|
|
int action;
|
|
tensor_TYPE_FLOAT * action_value = NULL;
|
|
//calculate_output_by_network_neurons_TYPE_FLOAT(rlAgent->networks->main_net, rlAgent->car->old_sensor, &action_value);
|
|
calculate_output_by_network_neurons_TYPE_FLOAT(rlAgent->networks->main_net, rlAgent->car->sensor, &action_value);
|
|
//long int NUMBER_EPISODE2 = (rlAgent->qlearnParams->number_episodes)*100;
|
|
//int randRange = 10000;
|
|
//NUMBER_EPISODE2 = NUMBER_EPISODE2 * NUMBER_EPISODE2;
|
|
//static bool init = true ;
|
|
//if(init){
|
|
//srand(time(NULL));
|
|
//init =false;
|
|
//}
|
|
//int random = xrand() % randRange;
|
|
float proba_explor = (float) (xrand() % (1<<17 -1))/ (1<<17 -1); //frand(); //(float)(random ) / randRange;
|
|
if(proba_explor > rlAgent->qlearnParams->exploration_factor ){
|
|
action = ARG_MAX_ARRAY_TYPE_FLOAT( action_value->x, action_value->dim->rank );
|
|
//if(action == ARG_MIN_ARRAY_TYPE_FLOAT( action_value->x, action_value->dim->rank ))
|
|
//action = xrand() % action_value->dim->rank ;
|
|
}
|
|
else{
|
|
action = xrand() % action_value->dim->rank ;
|
|
// explore++;
|
|
//printf(" EXPLORE :%ld, action : %d , factor : %f nb_episodes : %ld \n",explore,action,rlAgent->qlearnParams->exploration_factor, rlAgent->status->nb_episodes);
|
|
}
|
|
/*
|
|
if(rlAgent->status->last_action == action){
|
|
++(rlAgent->status->count_last_action);
|
|
if(rlAgent->status->count_last_action > rlAgent->qlearnParams->threshold_number_same_action ){
|
|
while(rlAgent->status->last_action == action)
|
|
action = xrand() % action_value->dim->rank ;
|
|
|
|
rlAgent->status->last_action = action;
|
|
rlAgent->status->count_last_action = 0;
|
|
}
|
|
}
|
|
else{
|
|
rlAgent->status->last_action = action;
|
|
rlAgent->status->count_last_action = 0;
|
|
}
|
|
*/
|
|
rlAgent->status->action = action;
|
|
return action;
|
|
}
|
|
|
|
int is_ending(struct status_qlearning *qlStatus){
|
|
int ret;
|
|
pthread_mutex_lock(qlStatus->mut_ending);
|
|
ret = qlStatus->ending;
|
|
pthread_mutex_unlock(qlStatus->mut_ending);
|
|
return ret;
|
|
}
|
|
|
|
void* runPrint(void *arg){
|
|
struct RL_agent *rlAgent = (struct RL_agent*)arg;
|
|
struct status_qlearning *qlStatus = rlAgent->status;
|
|
struct print_params * pprint = rlAgent->pprint;
|
|
struct vehicle *car = rlAgent->car;
|
|
size_t count_print = 0;
|
|
while(!is_ending(qlStatus)){
|
|
if(/*(qlStatus->nb_episodes %125 == 0) &&*/ pprint->printed){
|
|
//pthread_mutex_lock(&(pprint->mut_printed));
|
|
pthread_mutex_lock(&(car->mut_coord));
|
|
print_vehicle_n_path(car, pprint->scale_x, pprint->scale_y);
|
|
pthread_mutex_unlock(&(car->mut_coord));
|
|
//pthread_mutex_unlock(&(pprint->mut_printed));
|
|
printf("%s ",pprint->string_space);
|
|
printf("ep: %ld\n",qlStatus->index_episode);
|
|
neurons_TYPE_FLOAT * net_main = rlAgent->networks->main_net;
|
|
neurons_TYPE_FLOAT * net_target = rlAgent->networks->target_net;
|
|
for(size_t i=0; i<net_main->output->dim->rank; ++i) {
|
|
printf("{sensro[%s]:%f "" vs oldsens[%s]: %f}\n",action_name[i%COUNT_ACTION],net_target->output->x[i],
|
|
action_name[i%COUNT_ACTION],net_main->output->x[i]);
|
|
|
|
}
|
|
printf("\n< %5.2f > ( %s ) \n", car->direction, action_name[qlStatus->action % COUNT_ACTION]);
|
|
//print_weight_in_neurons_TYPE_FLOAT(net_main, "net_main_wei");
|
|
//PRINT_ATTRIBUTE_TENS_IN_ALL_LAYERS(TYPE_FLOAT, net_main, weight_in, "net_main_we_in");
|
|
PRINT_ATTRIBUTE_TENS_IN_ALL_LAYERS(TYPE_FLOAT, net_main, output, "net_main_out");
|
|
//PRINT_ATTRIBUTE_TENS_IN_ALL_LAYERS(TYPE_FLOAT, net_target, output, "net_target_out");
|
|
//PRINT_ATTRIBUTE_TENS_IN_ALL_LAYERS(TYPE_FLOAT, net_main, input, "net_main_input");
|
|
printf(" action : %d , factor : %f nb_episodes : %ld \n",qlStatus->action,rlAgent->qlearnParams->exploration_factor, rlAgent->status->nb_episodes);
|
|
|
|
FOR_LIST_FORM_BEGIN(TYPE_L_INT, qlStatus->progress_best_cumul){
|
|
printf(" | %ld |,",(qlStatus->progress_best_cumul)->current_list->value);
|
|
}
|
|
printf("[%ld] %s ", rlAgent->car->status->cumulative_reward, pprint->string_space);
|
|
|
|
}
|
|
Sleep(pprint->delay->delay_between_games);
|
|
++count_print;
|
|
if(count_print > 20){
|
|
count_print = 0;
|
|
clear_screen();
|
|
}
|
|
}
|
|
return NULL;
|
|
}
|
|
|
|
char *fileNameDateScore(char * pre, char* post,size_t score){
|
|
char *filename=malloc(256);
|
|
time_t t = time(NULL);
|
|
struct tm tm = *localtime(&t);
|
|
sprintf(filename,"%s%d%02d%02d_%02dh%02dm%02ds_%ld%s",pre, tm.tm_year + 1900, tm.tm_mon + 1, tm.tm_mday, tm.tm_hour, tm.tm_min, tm.tm_sec,score,post);
|
|
|
|
return filename;
|
|
}
|
|
|
|
|
|
|
|
void learn_to_drive(struct RL_agent * rlAgent){
|
|
int action;
|
|
struct vehicle * car = rlAgent->car;
|
|
struct game_status * car_status = car->status;
|
|
struct qlearning_params * qlParams = rlAgent->qlearnParams;
|
|
struct status_qlearning * qlStatus = rlAgent->status;
|
|
struct print_params * pprint = rlAgent->pprint;
|
|
char msg[100];
|
|
|
|
pthread_t threadPrint;
|
|
pthread_create(&threadPrint, NULL, runPrint, (void*)rlAgent);
|
|
|
|
// while(true){
|
|
for(size_t index_episode = 0; index_episode < qlParams->number_episodes; ++index_episode){
|
|
reset(car);
|
|
qlStatus->nb_training_after_updated_weight_in_target = 0;
|
|
qlStatus->index_episode = index_episode;
|
|
while(true){
|
|
++(qlStatus->nb_episodes);
|
|
++(qlStatus->nb_training_after_updated_weight_in_target);
|
|
action = select_action(rlAgent);
|
|
sprintf(msg," dir:%.0f : %s, ", car->direction ,action_name[action]);
|
|
add_string_log_M(car_status,msg);
|
|
step_vehicle(car, action);
|
|
train_qlearning(rlAgent, action);
|
|
//done in step ... copy_tensor_TYPE_FLOAT(car->old_sensor, car->sensor);
|
|
if( qlStatus->nb_training_after_updated_weight_in_target > qlParams->nb_training_before_update_weight_in_target ){
|
|
qlStatus->nb_training_after_updated_weight_in_target = 0;
|
|
copy_weight_in_networks_from_main_to_target(rlAgent->networks);
|
|
}
|
|
if(car_status->done == true){
|
|
//push_back_list_TYPE_L_INT(qlStatus->list_main_cumul, car_status->cumulative_reward);
|
|
// printf(" cumul : %ld ", car_status->cumulative_reward);
|
|
if(car_status->cumulative_reward > qlStatus->progress_best_cumul->end_list->value){
|
|
|
|
push_back_list_TYPE_L_INT(qlStatus->progress_best_cumul, car_status->cumulative_reward);
|
|
char *file = fileNameDateScore(".ff_main_",".txt",car_status->cumulative_reward);
|
|
EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, rlAgent->networks->main_net ,weight_in, file);
|
|
free(file);
|
|
file = fileNameDateScore(".ff_target_",".txt",car_status->cumulative_reward);
|
|
EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, rlAgent->networks->target_net ,weight_in, file);
|
|
free(file);
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
|
|
//if(pprint->printed){
|
|
// Sleep(pprint->delay->delay_between_episodes);
|
|
//}
|
|
}
|
|
pthread_mutex_lock(qlStatus->mut_ending);
|
|
qlStatus->ending = 1;
|
|
pthread_mutex_unlock(qlStatus->mut_ending);
|
|
// }
|
|
|
|
pthread_join(threadPrint, NULL);
|
|
}
|
|
|