Add some MACRO in neuron_t and debug deepQlearning

deepQlearn_0/src/deepQlearning/learn_to_drive.c

@@ -21,10 +21,12 @@ float D_L2(float t, float o){
 }
 
 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_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_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);
 }
 
 struct networks_qlearning * create_nework_qlearning(
@@ -70,6 +72,8 @@ struct status_qlearning * create_status_qlearning (){
 
   status_ql->nb_training_after_updated_weight_in_target = 0;
 
+  status_ql->nb_episodes = 0;
+  
   return status_ql;
 }
 
@@ -91,6 +95,7 @@ struct print_params * create_print_params(float scale_x, float scale_y,  struct
   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;
@@ -164,6 +169,7 @@ void free_delay_params (struct delay_params *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);
@@ -192,13 +198,14 @@ void train_qlearning(struct RL_agent * rlAgent,
   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*/;
-  calculate_output_by_network_neurons_TYPE_FLOAT(net_main, state, &action_value);
+  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;    
@@ -206,19 +213,12 @@ void train_qlearning(struct RL_agent * rlAgent,
     experimental_values->x[action] = car_status->reward + rlAgent->qlearnParams->gamma * MAX_ARRAY_TYPE_FLOAT(next_action_value->x, next_action_value->dim->rank) ;    
   }
 // ***
-      neurons_TYPE_FLOAT *tmp=NULL, *ttmp=NULL, *base = net_main;
-      init_copy_in_out_networks_from_tensors_TYPE_FLOAT(base,base->output , experimental_values );\
-      tmp=net_main->next_layer;\
-      while(tmp){\
-        calc_out_neurons_TYPE_FLOAT(tmp);\
-        ttmp = tmp;\
-        tmp = tmp->next_layer;\
-      }\
-      while(ttmp != base){\
-        calc_delta_neurons_TYPE_FLOAT(ttmp);\
-        update_weight_neurons_TYPE_FLOAT(ttmp);\
-        ttmp = ttmp->prev_layer;\
-      }\
+      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*/ );
@@ -230,19 +230,28 @@ void train_qlearning(struct RL_agent * rlAgent,
 }
 
 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);
-  long int NUMBER_EPISODE2 = (rlAgent->qlearnParams->number_episodes);
-  NUMBER_EPISODE2 = NUMBER_EPISODE2 * NUMBER_EPISODE2;
-  srand(time(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 NUMBER_EPISODE2 = 3000;
+  //NUMBER_EPISODE2 = NUMBER_EPISODE2 * NUMBER_EPISODE2;
+//  static bool init = true ;
+//  if(init){
+    srand(time(NULL));
+//    init =false;
+//  }
   int random = rand() % NUMBER_EPISODE2;
-  float proba_explor = (float)random / NUMBER_EPISODE2;
-  if(proba_explor <= rlAgent->qlearnParams->exploration_factor ){
-    action = rand() % action_value->dim->rank ; 
+  float proba_explor = (float)(random ) / NUMBER_EPISODE2;
+  if(proba_explor > rlAgent->qlearnParams->exploration_factor ){
+    action = ARG_MAX_ARRAY_TYPE_FLOAT( action_value->x, action_value->dim->rank  );
   }
   else{
-    action = ARG_MAX_ARRAY_TYPE_FLOAT( action_value->x, action_value->dim->rank  );
+    action = rand() % 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);
   }
   return action;
 }
@@ -261,20 +270,34 @@ void learn_to_drive(struct RL_agent * rlAgent){
       reset(car);
       qlStatus->nb_training_after_updated_weight_in_target = 0;
       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);
-        if(pprint->printed){
+        if(/*(qlStatus->nb_episodes %15 == 0)  && */ pprint->printed){
           pthread_mutex_lock(&(pprint->mut_printed));
           print_vehicle_n_path(car, pprint->scale_x, pprint->scale_y);
           pthread_mutex_unlock(&(pprint->mut_printed));
           printf("%s ",pprint->string_space);
-          printf("ep: %ld ",index_episode);
+          printf("ep: %ld\n",index_episode);
           neurons_TYPE_FLOAT * net_main = rlAgent->networks->main_net;
-          for(size_t i=0; i<net_main->output->dim->rank; ++i) printf("{sensro[%s]:%f }",action_name[i%COUNT_ACTION],net_main->output->x[i]);
+          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 %f / VS / %f */" vs oldsens[%s]: %f}\n",action_name[i%COUNT_ACTION],net_target->output->x[i], 
+            /*car->sensor->x[i] ,car->old_sensor->x[i],
+            */action_name[i%COUNT_ACTION],net_main->output->x[i]);
+            
+          }
+          printf("\n< %f > ( %s  ) \n", car->direction, action_name[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",action,rlAgent->qlearnParams->exploration_factor, rlAgent->status->nb_episodes);
           Sleep(pprint->delay->delay_between_games);
         }
         //done in step ... copy_tensor_TYPE_FLOAT(car->old_sensor, car->sensor);

deepQlearn_0/src/deepQlearning/learn_to_drive.h

@@ -45,6 +45,7 @@ struct status_qlearning {
   struct main_list_TYPE_L_INT * list_target_cumul;
   struct main_list_TYPE_L_INT * progress_best_cumul;
   long int nb_training_after_updated_weight_in_target;
+  size_t nb_episodes;
 };
 
 struct delay_params {
@@ -58,7 +59,7 @@ struct print_params {
   float scale_x; 
   float scale_y;
   struct delay_params *delay;
-  char string_space[LOG_LENTH];
+  char *string_space;//[LOG_LENTH];
 };
 
 struct networks_qlearning {

deepQlearn_0/src/deepQlearning/vehicle.c

@@ -7,7 +7,7 @@
 //#define CENTER 1
 //#define RIGHT 2
 
-#define LIMIT_DISTANCE ((float)1)/50
+#define LIMIT_DISTANCE ((float)((SUBDIVISION-1)/10))/SUBDIVISION
 
 #define REWARD_STOP -1000
 #define REWARD_CONTINUE 100
@@ -68,6 +68,12 @@ struct vehicle * create_vehicle(struct blocks *path){
   ret_vehicle->coord = create_coordinate(2);
   ret_vehicle->sensor = create_sensors(NB_SENSORS);
   ret_vehicle->old_sensor = create_sensors(NB_SENSORS);
+
+  for(size_t i=0; i<NB_SENSORS;++i){
+    ret_vehicle->sensor->x[i]=0;
+    ret_vehicle->old_sensor->x[i]=0;
+  }
+
   ret_vehicle->path = path;
   
   ret_vehicle->status = create_game_status();
@@ -387,15 +393,15 @@ void print_vehicle_n_path(struct vehicle *v, float scale_x, float scale_y){
 
 void move_vehicle(struct vehicle *v){
   v->coord->x[0] += v->speed * cos(v->direction * M_PI / 180);
-  v->coord->x[1] += v->speed * sin(v->direction * M_PI / 180);
+  v->coord->x[1] -= v->speed * sin(v->direction * M_PI / 180);
 }
 
 float distance2_coordinate(coordinate *c0, coordinate *c1){
-  if(c0->dim->rank != c1->dim->rank) return 0;
+  if(c0->dim->rank != c1->dim->rank) return -1;
   float d=0, tmp;
   for(size_t i=0; i<c0->dim->rank; ++i){
      tmp = (c0->x[i] - c1->x[i]);
-     d += tmp * tmp;
+     d += (tmp * tmp);
   }
   return sqrt(d);
 }
@@ -404,19 +410,27 @@ float distance2_coordinate(coordinate *c0, coordinate *c1){
   direction_radian = (v->direction + deviation) * M_PI / 180;\
   while( is_in_blocks(v->path, diStep_sensor )){\
     diStep_sensor->x[0] += step_sensor * cos(direction_radian);\
-    diStep_sensor->x[1] += step_sensor * sin(direction_radian);\
+    diStep_sensor->x[1] -= step_sensor * sin(direction_radian);\
   }\
-  v->sensor->x[position] = (MIN(49,(distance2_coordinate(diStep_sensor, v->coord)/5))) ;\
+  dist = (distance2_coordinate(diStep_sensor, v->coord)/5);\
+  printf("| dist :%f | ",dist);\
+  v->sensor->x[position] = (float)(MIN((SUBDIVISION-1),(int)dist))/SUBDIVISION ;\
   
+  
+  
+  
+  
+  //v->sensor->x[position] = (MIN(49,(distance2_coordinate(diStep_sensor, v->coord)/5))) ;\
   //v->sensor->x[position] = (MIN(49,(distance2_coordinate(diStep_sensor, v->coord)))) / 50;\
   //v->sensor->x[position] = (MIN(49,(int)(distance2_coordinate(diStep_sensor, v->coord)/10))) / 50;\
 
 void read_sensor(struct vehicle *v){
   copy_tensor_TYPE_FLOAT(v->old_sensor, v->sensor);
-  float step_sensor = ((float)1)/SUBDIVISION;
+  float step_sensor = STEP; // /SUBDIVISION;
   coordinate * diStep_sensor = create_coordinate(2);
   copy_coordinate(diStep_sensor, v->coord->x);
-  
+  float dist;
+  printf("\n"); 
   // count the number of step until we go out of the path = distance
   // center sensor
   float direction_radian ;
@@ -424,11 +438,11 @@ void read_sensor(struct vehicle *v){
 
   copy_coordinate(diStep_sensor, v->coord->x);
   // right sensor 
-  SENSOR_VALUE_CALCULATE(RIGHT,45);
+  SENSOR_VALUE_CALCULATE(RIGHT,-45);
   
   copy_coordinate(diStep_sensor, v->coord->x);
   // left sensor
-  SENSOR_VALUE_CALCULATE(LEFT, -45);
+  SENSOR_VALUE_CALCULATE(LEFT, 45);
  
   free_coordinate(diStep_sensor);
 
@@ -482,8 +496,8 @@ void add_string_log(struct game_status *status, char *str ){
 void step_vehicle(struct vehicle *v, int action){
   //float action_x[NB_ACTION]={-3,0,3}; // [LEFT, CENTER, RIGHT]
   float action_x[NB_ACTION]={-3,0,3}; // [LEFT, CENTER, RIGHT]
-  v->direction = v->direction + action_x[action % 3];
-  v->speed = ((float)1)/5;
+  v->direction = (float)((int)(v->direction + action_x[action % 3]) % 360) ;
+  v->speed = SPEED; // /5;
   move_vehicle(v);
   read_sensor(v);
   struct game_status *status = v->status;
@@ -495,22 +509,24 @@ void step_vehicle(struct vehicle *v, int action){
   struct blocks * path = v->path;
     //printf(" center : %f vs %f direction: %f\n",v->sensor->x[CENTER], LIMIT_DISTANCE, v->direction);
   if( v->sensor->x[CENTER]<= LIMIT_DISTANCE ){
+  //if( MAX_ARRAY_TYPE_FLOAT(v->sensor->x,v->sensor->dim->rank)<= LIMIT_DISTANCE ){
     status->reward = REWARD_STOP;
     status->done = true;
   }
   else{
     bool broken = false;
-    long prec, next;
+    long pprec, prec, next;
     char msg[48];
     for(long i=0; i< path->nb_blocks; ++i){
         //prec = (i-1)%(path->nb_blocks);
+        pprec = (i + path->nb_blocks - 2 )%(path->nb_blocks);
         prec = (i + path->nb_blocks - 1 )%(path->nb_blocks);
         next = (i + 1)%(path->nb_blocks);
         //printf("i:%ld, prec:%ld, next:%ld: maker %d, prec marker %d\n",i,prec,next, path->marker[i], path->marker[prec]);
       if(is_in_block_index(path, i, v->coord)){
         if(path->marker[i] == false && path->marker[prec] == true){
           path->marker[i]=true;
-          path->marker[prec]=false;
+          path->marker[pprec]=false;
           status->reward = REWARD_CONTINUE;
           status->done = false;
           sprintf(msg," %ld,",i);
@@ -536,9 +552,10 @@ void step_vehicle(struct vehicle *v, int action){
 
 }
 
-
+#define RANDOM 1
 
 void reset(struct vehicle *v){
+  //static bool init = true;
   struct blocks * path = v->path;
   long int i;
   for(i=0; i<(path->nb_blocks -1); ++i)
@@ -547,19 +564,32 @@ void reset(struct vehicle *v){
   v->status->cumulative_reward = 0;
   sprintf(v->status->log,"\n");
   v->status->cur_log = 0;
-  srand(time(NULL));
+  //if(init){
+      srand(time(NULL));
+  //  init = false;
+  //}
   int random;
   int diff;
   diff = path->upper_bound_block[0]->x[0] - path->lower_bound_block[0]->x[0];
-  random = rand() % diff;
-  //v->coord->x[0] = path->lower_bound_block[0]->x[0] + random;
-  v->coord->x[0] = path->lower_bound_block[0]->x[0] + diff/2;
+  random = rand() % (diff/2) ;
+  #if RANDOM
+    v->coord->x[0] = path->lower_bound_block[0]->x[0] + random;
+  #else
+    v->coord->x[0] = path->lower_bound_block[0]->x[0] + diff/2;
+  #endif
   diff = path->upper_bound_block[0]->x[1] - path->lower_bound_block[0]->x[1];
-  random = rand() % diff;
-  //v->coord->x[1] = path->lower_bound_block[0]->x[1] + random;
-  v->coord->x[1] = path->lower_bound_block[0]->x[1] + diff/2;
+  random = rand() % (diff/2);
+  #if RANDOM
+    v->coord->x[1] = path->lower_bound_block[0]->x[1] + random;
+  #else
+    v->coord->x[1] = path->lower_bound_block[0]->x[1] + diff/2;
+  #endif
   random = rand() % 50;
-  //v->direction = random - 25;
-  v->direction = -90;
-  v->speed = 1;
+  #if RANDOM
+    v->direction = random - 25;
+  #else
+    v->direction = -90;
+  #endif
+  v->speed = SPEED;
+  read_sensor(v);
 }

deepQlearn_0/src/deepQlearning/vehicle.h

@@ -26,7 +26,10 @@
 
 #define COUNT_ACTION 3
 
-#define SUBDIVISION 5 //10
+#define SUBDIVISION 50
+
+#define STEP 1
+#define SPEED 1
 
 
 struct game_status {

deepQlearn_0/test/is_good.c

@@ -253,6 +253,137 @@ TEST(circle_path_vehicle){
   free_vehicle(vhcl);
 
 
+}
+
+TEST(circle_path_vehicle_00){
+  size_t nb_block = 7;
+  size_t dim= 2;
+  struct blocks * path = create_blocks(nb_block, dim);
+
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,300});
+  copy_coordinate(path->upper_bound_block[0], (float[]){400,700});
+  copy_coordinate(path->lower_bound_block[1], (float[]){100,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){1000,300});
+  copy_coordinate(path->lower_bound_block[2], (float[]){1000,50});
+  copy_coordinate(path->upper_bound_block[2], (float[]){1400,500});
+  copy_coordinate(path->lower_bound_block[3], (float[]){1400,200});
+  copy_coordinate(path->upper_bound_block[3], (float[]){1800,700});
+  copy_coordinate(path->lower_bound_block[4], (float[]){1100,700});
+  copy_coordinate(path->upper_bound_block[4], (float[]){1700,1000});
+  copy_coordinate(path->lower_bound_block[5], (float[]){800,600});
+  copy_coordinate(path->upper_bound_block[5], (float[]){1100,975});
+  copy_coordinate(path->lower_bound_block[6], (float[]){100,700});
+  copy_coordinate(path->upper_bound_block[6], (float[]){800,975});
+ 
+  update_bounds_limits_blocks(path);
+
+  struct vehicle *vhcl = create_vehicle(path);
+
+  print_vehicle_n_path(vhcl, 20,40);
+  
+  step_vehicle(vhcl, CENTER);
+  Sleep(200);
+/*  print_vehicle_n_path(vhcl, 0.2,0.4);
+  
+  step_vehicle(vhcl, CENTER);
+  Sleep(200);
+  print_vehicle_n_path(vhcl, 0.2,0.4);
+
+  step_vehicle(vhcl, CENTER);
+  Sleep(200);
+  print_vehicle_n_path(vhcl, 0.2,0.4);
+  
+  step_vehicle(vhcl, CENTER);
+  Sleep(200);
+  print_vehicle_n_path(vhcl, 0.2,0.4);
+*/
+  free_vehicle(vhcl);
+
+
+}
+
+
+
+TEST(circle_path_vehicle_50){
+  size_t nb_block = 7;
+  size_t dim= 2;
+  struct blocks * path = create_blocks(nb_block, dim);
+#if 1
+  copy_coordinate(path->lower_bound_block[6], (float[]){0,30});
+  copy_coordinate(path->upper_bound_block[6], (float[]){150,250});
+  copy_coordinate(path->lower_bound_block[5], (float[]){150,0});
+  copy_coordinate(path->upper_bound_block[5], (float[]){250,80});
+  copy_coordinate(path->lower_bound_block[4], (float[]){250,20});
+  copy_coordinate(path->upper_bound_block[4], (float[]){360,120});
+  copy_coordinate(path->lower_bound_block[3], (float[]){360,80});
+  copy_coordinate(path->upper_bound_block[3], (float[]){600,150});
+  copy_coordinate(path->lower_bound_block[2], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[2], (float[]){760,300});
+  copy_coordinate(path->lower_bound_block[1], (float[]){300,300});
+  copy_coordinate(path->upper_bound_block[1], (float[]){760,350});
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[0], (float[]){410,300});
+
+
+#else 
+    copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
+  copy_coordinate(path->upper_bound_block[0], (float[]){150,250});
+  copy_coordinate(path->lower_bound_block[1], (float[]){150,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){250,150});
+  copy_coordinate(path->lower_bound_block[2], (float[]){250,80});
+  copy_coordinate(path->upper_bound_block[2], (float[]){360,200});
+  copy_coordinate(path->lower_bound_block[3], (float[]){360,70});
+  copy_coordinate(path->upper_bound_block[3], (float[]){600,170});
+  copy_coordinate(path->lower_bound_block[4], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[4], (float[]){760,300});
+  copy_coordinate(path->lower_bound_block[5], (float[]){300,300});
+  copy_coordinate(path->upper_bound_block[5], (float[]){760,350});
+  copy_coordinate(path->lower_bound_block[6], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[6], (float[]){410,300});
+
+
+copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
+  copy_coordinate(path->upper_bound_block[0], (float[]){100,250});
+  copy_coordinate(path->lower_bound_block[1], (float[]){100,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){250,80});
+  copy_coordinate(path->lower_bound_block[2], (float[]){250,0});
+  copy_coordinate(path->upper_bound_block[2], (float[]){360,140});
+  copy_coordinate(path->lower_bound_block[3], (float[]){360,70});
+  copy_coordinate(path->upper_bound_block[3], (float[]){600,140});
+  copy_coordinate(path->lower_bound_block[4], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[4], (float[]){720,300});
+  copy_coordinate(path->lower_bound_block[5], (float[]){300,300});
+  copy_coordinate(path->upper_bound_block[5], (float[]){720,350});
+  copy_coordinate(path->lower_bound_block[6], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[6], (float[]){410,300});
+ 
+#endif
+
+  update_bounds_limits_blocks(path);
+
+  struct vehicle *vhcl = create_vehicle(path);
+
+  print_vehicle_n_path(vhcl, 10,10);
+  
+  step_vehicle(vhcl, CENTER);
+  Sleep(200);
+/*  print_vehicle_n_path(vhcl, 0.2,0.4);
+  
+  step_vehicle(vhcl, CENTER);
+  Sleep(200);
+  print_vehicle_n_path(vhcl, 0.2,0.4);
+
+  step_vehicle(vhcl, CENTER);
+  Sleep(200);
+  print_vehicle_n_path(vhcl, 0.2,0.4);
+  
+  step_vehicle(vhcl, CENTER);
+  Sleep(200);
+  print_vehicle_n_path(vhcl, 0.2,0.4);
+*/
+  free_vehicle(vhcl);
+
+
 }
 
 TEST(reward_list){
@@ -261,8 +392,125 @@ TEST(reward_list){
   free_status_qlearning(l_reward);
 }
 
+
+float f(float x){
+  return 1/(1+exp((double)(-x)));
+}
+
+float df(float x){
+  return exp(-x)/ ((1+exp(-x)) * (1+exp(-x)));
+}
 #if 1
-TEST(first_learn_vehicle){
+TEST(first_learn_vehicle_rev50){
+  size_t nb_block = 7;
+  size_t dim= 2;
+  struct blocks * path = create_blocks(nb_block, dim);
+
+
+
+#if 0
+  copy_coordinate(path->lower_bound_block[6], (float[]){0,30});
+  copy_coordinate(path->upper_bound_block[6], (float[]){150,250});
+  copy_coordinate(path->lower_bound_block[5], (float[]){150,0});
+  copy_coordinate(path->upper_bound_block[5], (float[]){250,80});
+  copy_coordinate(path->lower_bound_block[4], (float[]){250,20});
+  copy_coordinate(path->upper_bound_block[4], (float[]){360,120});
+  copy_coordinate(path->lower_bound_block[3], (float[]){360,80});
+  copy_coordinate(path->upper_bound_block[3], (float[]){600,150});
+  copy_coordinate(path->lower_bound_block[2], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[2], (float[]){760,300});
+  copy_coordinate(path->lower_bound_block[1], (float[]){300,300});
+  copy_coordinate(path->upper_bound_block[1], (float[]){760,350});
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[0], (float[]){410,300});
+
+#else
+
+  copy_coordinate(path->lower_bound_block[4], (float[]){0,0});
+  copy_coordinate(path->upper_bound_block[4], (float[]){150,250});
+  copy_coordinate(path->lower_bound_block[5], (float[]){150,40});
+  copy_coordinate(path->upper_bound_block[5], (float[]){250,150});
+  copy_coordinate(path->lower_bound_block[6], (float[]){250,80});
+  copy_coordinate(path->upper_bound_block[6], (float[]){360,200});
+  copy_coordinate(path->lower_bound_block[0], (float[]){360,70});
+  copy_coordinate(path->upper_bound_block[0], (float[]){600,150});
+  copy_coordinate(path->lower_bound_block[1], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[1], (float[]){760,300});
+  copy_coordinate(path->lower_bound_block[2], (float[]){260,300});
+  copy_coordinate(path->upper_bound_block[2], (float[]){760,360});
+  copy_coordinate(path->lower_bound_block[3], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[3], (float[]){410,300});
+
+
+ 
+#endif
+
+  update_bounds_limits_blocks(path);
+
+  struct vehicle *car = create_vehicle(path);
+
+  config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,24,24,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
+  //config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,14,14,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
+  
+  bool randomize=true; 
+  float minR = -0.5, maxR = 0.5;  
+  int randomRange = 500;
+  size_t nb_prod_thread = 2;
+  size_t nb_calc_thread = 4;
+  float learning_rate = 0.001;  
+  struct networks_qlearning *nnetworks = create_nework_qlearning(
+    pconf,
+    randomize, minR, maxR,  randomRange,
+    nb_prod_thread, nb_calc_thread,
+    learning_rate
+  );
+
+  struct status_qlearning *qlstatus = create_status_qlearning ();
+  struct delay_params *dly = create_delay_params (
+    100/*size_t delay_between_episodes*/,
+    10/*size_t delay_between_games*/
+  );
+  
+  struct qlearning_params *qlparams = create_qlearning_params (
+    0.95/*float gamma*/,
+    learning_rate,
+    0 /* (not used!)float discount_factor*/,
+    0.99  /*float exploration_factor*/,
+    20/*long int nb_training_before_update_weight_in_target*/,
+    10000/*size_t number_episodes*/
+  );
+/*   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, d_f_act , df );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, f_act, f );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, d_f_act , df );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, f_act , f );
+*/
+  struct print_params *pprint = create_print_params(
+    12/*float scale_x*/,12 /*float scale_y*/,  
+    dly/*struct delay_params * dly_p*/
+  );
+
+  struct RL_agent *rlAgent = create_RL_agent (
+    nnetworks /*struct networks_qlearning * networks*/,
+    car /*struct vehicle *  car*/,
+    qlstatus /*struct status_qlearning * status*/,
+    pprint /*struct print_params * pprint*/,
+    qlparams/*struct qlearning_params *qlearnParams*/
+  );
+
+  learn_to_drive(rlAgent);
+
+  free_RL_agent(rlAgent);
+
+
+
+
+}
+#endif
+
+
+
+#if 1
+TEST(first_learn_vehicle_50){
   size_t nb_block = 7;
   size_t dim= 2;
   struct blocks * path = create_blocks(nb_block, dim);
@@ -270,6 +518,59 @@ TEST(first_learn_vehicle){
 
 
 #if 1
+
+    copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
+  copy_coordinate(path->upper_bound_block[0], (float[]){150,250});
+  copy_coordinate(path->lower_bound_block[1], (float[]){150,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){250,150});
+  copy_coordinate(path->lower_bound_block[2], (float[]){250,80});
+  copy_coordinate(path->upper_bound_block[2], (float[]){360,200});
+  copy_coordinate(path->lower_bound_block[3], (float[]){360,70});
+  copy_coordinate(path->upper_bound_block[3], (float[]){600,170});
+  copy_coordinate(path->lower_bound_block[4], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[4], (float[]){760,300});
+  copy_coordinate(path->lower_bound_block[5], (float[]){300,300});
+  copy_coordinate(path->upper_bound_block[5], (float[]){760,350});
+  copy_coordinate(path->lower_bound_block[6], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[6], (float[]){410,300});
+
+/*
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
+  copy_coordinate(path->upper_bound_block[0], (float[]){100,250});
+  copy_coordinate(path->lower_bound_block[1], (float[]){100,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){250,80});
+  copy_coordinate(path->lower_bound_block[2], (float[]){250,0});
+  copy_coordinate(path->upper_bound_block[2], (float[]){360,140});
+  copy_coordinate(path->lower_bound_block[3], (float[]){360,70});
+  copy_coordinate(path->upper_bound_block[3], (float[]){600,140});
+  copy_coordinate(path->lower_bound_block[4], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[4], (float[]){720,300});
+  copy_coordinate(path->lower_bound_block[5], (float[]){300,300});
+  copy_coordinate(path->upper_bound_block[5], (float[]){720,350});
+  copy_coordinate(path->lower_bound_block[6], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[6], (float[]){410,300});
+ 
+
+
+
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,300});
+  copy_coordinate(path->upper_bound_block[0], (float[]){400,700});
+  copy_coordinate(path->lower_bound_block[1], (float[]){100,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){1000,300});
+  copy_coordinate(path->lower_bound_block[2], (float[]){1000,50});
+  copy_coordinate(path->upper_bound_block[2], (float[]){1400,500});
+  copy_coordinate(path->lower_bound_block[3], (float[]){1400,200});
+  copy_coordinate(path->upper_bound_block[3], (float[]){1800,700});
+  copy_coordinate(path->lower_bound_block[4], (float[]){1100,700});
+  copy_coordinate(path->upper_bound_block[4], (float[]){1700,1000});
+  copy_coordinate(path->lower_bound_block[5], (float[]){800,600});
+  copy_coordinate(path->upper_bound_block[5], (float[]){1100,975});
+  copy_coordinate(path->lower_bound_block[6], (float[]){100,700});
+  copy_coordinate(path->upper_bound_block[6], (float[]){800,975});
+ */
+
+#else 
+
   copy_coordinate(path->lower_bound_block[0], (float[]){0,3});
   copy_coordinate(path->upper_bound_block[0], (float[]){4,7});
   copy_coordinate(path->lower_bound_block[1], (float[]){1,0});
@@ -285,23 +586,7 @@ TEST(first_learn_vehicle){
   copy_coordinate(path->lower_bound_block[6], (float[]){1,7});
   copy_coordinate(path->upper_bound_block[6], (float[]){8,9.75});
  
-#else 
-
-
-  copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
-  copy_coordinate(path->upper_bound_block[0], (float[]){2,7});
-  copy_coordinate(path->lower_bound_block[1], (float[]){2,0});
-  copy_coordinate(path->upper_bound_block[1], (float[]){4,2});
-  copy_coordinate(path->lower_bound_block[2], (float[]){4,0.5});
-  copy_coordinate(path->upper_bound_block[2], (float[]){8,3});
-  copy_coordinate(path->lower_bound_block[3], (float[]){8,0});
-  copy_coordinate(path->upper_bound_block[3], (float[]){16,2});
-  copy_coordinate(path->lower_bound_block[4], (float[]){16,0});
-  copy_coordinate(path->upper_bound_block[4], (float[]){18,7});
-  copy_coordinate(path->lower_bound_block[5], (float[]){6,7});
-  copy_coordinate(path->upper_bound_block[5], (float[]){18,9});
-  copy_coordinate(path->lower_bound_block[6], (float[]){2,6});
-  copy_coordinate(path->upper_bound_block[6], (float[]){6,8});
+ 
 #endif
 
   update_bounds_limits_blocks(path);
@@ -309,9 +594,10 @@ TEST(first_learn_vehicle){
   struct vehicle *car = create_vehicle(path);
 
   config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,24,24,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
+  //config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,14,14,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
   
   bool randomize=true; 
-  float minR = 0, maxR = 1;  
+  float minR = -0.5, maxR = 0.5;  
   int randomRange = 500;
   size_t nb_prod_thread = 2;
   size_t nb_calc_thread = 4;
@@ -325,20 +611,356 @@ TEST(first_learn_vehicle){
 
   struct status_qlearning *qlstatus = create_status_qlearning ();
   struct delay_params *dly = create_delay_params (
-    200/*size_t delay_between_episodes*/,
-    20/*size_t delay_between_games*/
+    100/*size_t delay_between_episodes*/,
+    10/*size_t delay_between_games*/
   );
   
   struct qlearning_params *qlparams = create_qlearning_params (
     0.95/*float gamma*/,
     learning_rate,
     0 /* (not used!)float discount_factor*/,
-    0.99/*float exploration_factor*/,
+    0.99  /*float exploration_factor*/,
     20/*long int nb_training_before_update_weight_in_target*/,
     10000/*size_t number_episodes*/
   );
+/*   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, d_f_act , df );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, f_act, f );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, d_f_act , df );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, f_act , f );
+*/
   struct print_params *pprint = create_print_params(
-    0.2/*float scale_x*/,0.4 /*float scale_y*/,  
+    12/*float scale_x*/,12 /*float scale_y*/,  
+    dly/*struct delay_params * dly_p*/
+  );
+
+  struct RL_agent *rlAgent = create_RL_agent (
+    nnetworks /*struct networks_qlearning * networks*/,
+    car /*struct vehicle *  car*/,
+    qlstatus /*struct status_qlearning * status*/,
+    pprint /*struct print_params * pprint*/,
+    qlparams/*struct qlearning_params *qlearnParams*/
+  );
+
+  learn_to_drive(rlAgent);
+
+  free_RL_agent(rlAgent);
+
+
+
+
+}
+#endif
+
+
+
+#if 0
+TEST(first_learn_vehicle){
+  size_t nb_block = 7;
+  size_t dim= 2;
+  struct blocks * path = create_blocks(nb_block, dim);
+
+
+
+#if 1
+
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,300});
+  copy_coordinate(path->upper_bound_block[0], (float[]){400,700});
+  copy_coordinate(path->lower_bound_block[1], (float[]){100,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){1000,300});
+  copy_coordinate(path->lower_bound_block[2], (float[]){1000,50});
+  copy_coordinate(path->upper_bound_block[2], (float[]){1400,500});
+  copy_coordinate(path->lower_bound_block[3], (float[]){1400,200});
+  copy_coordinate(path->upper_bound_block[3], (float[]){1800,700});
+  copy_coordinate(path->lower_bound_block[4], (float[]){1100,700});
+  copy_coordinate(path->upper_bound_block[4], (float[]){1700,1000});
+  copy_coordinate(path->lower_bound_block[5], (float[]){800,600});
+  copy_coordinate(path->upper_bound_block[5], (float[]){1100,975});
+  copy_coordinate(path->lower_bound_block[6], (float[]){100,700});
+  copy_coordinate(path->upper_bound_block[6], (float[]){800,975});
+ 
+
+#else 
+
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,3});
+  copy_coordinate(path->upper_bound_block[0], (float[]){4,7});
+  copy_coordinate(path->lower_bound_block[1], (float[]){1,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){10,3});
+  copy_coordinate(path->lower_bound_block[2], (float[]){10,0.5});
+  copy_coordinate(path->upper_bound_block[2], (float[]){14,5});
+  copy_coordinate(path->lower_bound_block[3], (float[]){14,2});
+  copy_coordinate(path->upper_bound_block[3], (float[]){18,7});
+  copy_coordinate(path->lower_bound_block[4], (float[]){11,7});
+  copy_coordinate(path->upper_bound_block[4], (float[]){17,10});
+  copy_coordinate(path->lower_bound_block[5], (float[]){8,6});
+  copy_coordinate(path->upper_bound_block[5], (float[]){11,9.75});
+  copy_coordinate(path->lower_bound_block[6], (float[]){1,7});
+  copy_coordinate(path->upper_bound_block[6], (float[]){8,9.75});
+ 
+ 
+#endif
+
+  update_bounds_limits_blocks(path);
+
+  struct vehicle *car = create_vehicle(path);
+
+  config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,24,24,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
+  //config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,14,14,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
+  
+  bool randomize=true; 
+  float minR = -0.5, maxR = 0.5;  
+  int randomRange = 5000;
+  size_t nb_prod_thread = 2;
+  size_t nb_calc_thread = 4;
+  float learning_rate = 0.1;  
+  struct networks_qlearning *nnetworks = create_nework_qlearning(
+    pconf,
+    randomize, minR, maxR,  randomRange,
+    nb_prod_thread, nb_calc_thread,
+    learning_rate
+  );
+
+  struct status_qlearning *qlstatus = create_status_qlearning ();
+  struct delay_params *dly = create_delay_params (
+    100/*size_t delay_between_episodes*/,
+    10/*size_t delay_between_games*/
+  );
+  
+  struct qlearning_params *qlparams = create_qlearning_params (
+    0.95/*float gamma*/,
+    learning_rate,
+    0 /* (not used!)float discount_factor*/,
+    0.85  /*float exploration_factor*/,
+    20/*long int nb_training_before_update_weight_in_target*/,
+    10000/*size_t number_episodes*/
+  );
+/*   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, d_f_act , df );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, f_act, f );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, d_f_act , df );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, f_act , f );
+*/
+  struct print_params *pprint = create_print_params(
+    20/*float scale_x*/,40 /*float scale_y*/,  
+    dly/*struct delay_params * dly_p*/
+  );
+
+  struct RL_agent *rlAgent = create_RL_agent (
+    nnetworks /*struct networks_qlearning * networks*/,
+    car /*struct vehicle *  car*/,
+    qlstatus /*struct status_qlearning * status*/,
+    pprint /*struct print_params * pprint*/,
+    qlparams/*struct qlearning_params *qlearnParams*/
+  );
+
+  learn_to_drive(rlAgent);
+
+  free_RL_agent(rlAgent);
+
+
+
+
+}
+#endif
+
+
+
+#if 0
+TEST(first_learn_vehicle){
+  size_t nb_block = 7;
+  size_t dim= 2;
+  struct blocks * path = create_blocks(nb_block, dim);
+
+
+
+#if 1
+
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,300});
+  copy_coordinate(path->upper_bound_block[0], (float[]){400,700});
+  copy_coordinate(path->lower_bound_block[1], (float[]){100,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){1000,300});
+  copy_coordinate(path->lower_bound_block[2], (float[]){1000,50});
+  copy_coordinate(path->upper_bound_block[2], (float[]){1400,500});
+  copy_coordinate(path->lower_bound_block[3], (float[]){1400,200});
+  copy_coordinate(path->upper_bound_block[3], (float[]){1800,700});
+  copy_coordinate(path->lower_bound_block[4], (float[]){1100,700});
+  copy_coordinate(path->upper_bound_block[4], (float[]){1700,1000});
+  copy_coordinate(path->lower_bound_block[5], (float[]){800,600});
+  copy_coordinate(path->upper_bound_block[5], (float[]){1100,975});
+  copy_coordinate(path->lower_bound_block[6], (float[]){100,700});
+  copy_coordinate(path->upper_bound_block[6], (float[]){800,975});
+ 
+
+#else 
+
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,3});
+  copy_coordinate(path->upper_bound_block[0], (float[]){4,7});
+  copy_coordinate(path->lower_bound_block[1], (float[]){1,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){10,3});
+  copy_coordinate(path->lower_bound_block[2], (float[]){10,0.5});
+  copy_coordinate(path->upper_bound_block[2], (float[]){14,5});
+  copy_coordinate(path->lower_bound_block[3], (float[]){14,2});
+  copy_coordinate(path->upper_bound_block[3], (float[]){18,7});
+  copy_coordinate(path->lower_bound_block[4], (float[]){11,7});
+  copy_coordinate(path->upper_bound_block[4], (float[]){17,10});
+  copy_coordinate(path->lower_bound_block[5], (float[]){8,6});
+  copy_coordinate(path->upper_bound_block[5], (float[]){11,9.75});
+  copy_coordinate(path->lower_bound_block[6], (float[]){1,7});
+  copy_coordinate(path->upper_bound_block[6], (float[]){8,9.75});
+ 
+ 
+#endif
+
+  update_bounds_limits_blocks(path);
+
+  struct vehicle *car = create_vehicle(path);
+
+  config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,24,24,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
+  //config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,14,14,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
+  
+  bool randomize=true; 
+  float minR = -0.5, maxR = 0.5;  
+  int randomRange = 5000;
+  size_t nb_prod_thread = 2;
+  size_t nb_calc_thread = 4;
+  float learning_rate = 0.1;  
+  struct networks_qlearning *nnetworks = create_nework_qlearning(
+    pconf,
+    randomize, minR, maxR,  randomRange,
+    nb_prod_thread, nb_calc_thread,
+    learning_rate
+  );
+
+  struct status_qlearning *qlstatus = create_status_qlearning ();
+  struct delay_params *dly = create_delay_params (
+    100/*size_t delay_between_episodes*/,
+    10/*size_t delay_between_games*/
+  );
+  
+  struct qlearning_params *qlparams = create_qlearning_params (
+    0.95/*float gamma*/,
+    learning_rate,
+    0 /* (not used!)float discount_factor*/,
+    0.85  /*float exploration_factor*/,
+    20/*long int nb_training_before_update_weight_in_target*/,
+    10000/*size_t number_episodes*/
+  );
+/*   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, d_f_act , df );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, f_act, f );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, d_f_act , df );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, f_act , f );
+*/
+  struct print_params *pprint = create_print_params(
+    20/*float scale_x*/,40 /*float scale_y*/,  
+    dly/*struct delay_params * dly_p*/
+  );
+
+  struct RL_agent *rlAgent = create_RL_agent (
+    nnetworks /*struct networks_qlearning * networks*/,
+    car /*struct vehicle *  car*/,
+    qlstatus /*struct status_qlearning * status*/,
+    pprint /*struct print_params * pprint*/,
+    qlparams/*struct qlearning_params *qlearnParams*/
+  );
+
+  learn_to_drive(rlAgent);
+
+  free_RL_agent(rlAgent);
+
+
+
+
+}
+#endif
+
+
+
+
+#if 0
+TEST(first_learn_vehicle){
+  size_t nb_block = 7;
+  size_t dim= 2;
+  struct blocks * path = create_blocks(nb_block, dim);
+
+
+
+#if 1
+
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,300});
+  copy_coordinate(path->upper_bound_block[0], (float[]){400,700});
+  copy_coordinate(path->lower_bound_block[1], (float[]){100,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){1000,300});
+  copy_coordinate(path->lower_bound_block[2], (float[]){1000,50});
+  copy_coordinate(path->upper_bound_block[2], (float[]){1400,500});
+  copy_coordinate(path->lower_bound_block[3], (float[]){1400,200});
+  copy_coordinate(path->upper_bound_block[3], (float[]){1800,700});
+  copy_coordinate(path->lower_bound_block[4], (float[]){1100,700});
+  copy_coordinate(path->upper_bound_block[4], (float[]){1700,1000});
+  copy_coordinate(path->lower_bound_block[5], (float[]){800,600});
+  copy_coordinate(path->upper_bound_block[5], (float[]){1100,975});
+  copy_coordinate(path->lower_bound_block[6], (float[]){100,700});
+  copy_coordinate(path->upper_bound_block[6], (float[]){800,975});
+ 
+
+#else 
+
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,3});
+  copy_coordinate(path->upper_bound_block[0], (float[]){4,7});
+  copy_coordinate(path->lower_bound_block[1], (float[]){1,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){10,3});
+  copy_coordinate(path->lower_bound_block[2], (float[]){10,0.5});
+  copy_coordinate(path->upper_bound_block[2], (float[]){14,5});
+  copy_coordinate(path->lower_bound_block[3], (float[]){14,2});
+  copy_coordinate(path->upper_bound_block[3], (float[]){18,7});
+  copy_coordinate(path->lower_bound_block[4], (float[]){11,7});
+  copy_coordinate(path->upper_bound_block[4], (float[]){17,10});
+  copy_coordinate(path->lower_bound_block[5], (float[]){8,6});
+  copy_coordinate(path->upper_bound_block[5], (float[]){11,9.75});
+  copy_coordinate(path->lower_bound_block[6], (float[]){1,7});
+  copy_coordinate(path->upper_bound_block[6], (float[]){8,9.75});
+ 
+ 
+#endif
+
+  update_bounds_limits_blocks(path);
+
+  struct vehicle *car = create_vehicle(path);
+
+  config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,24,24,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
+  //config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,14,14,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
+  
+  bool randomize=true; 
+  float minR = -0.5, maxR = 0.5;  
+  int randomRange = 5000;
+  size_t nb_prod_thread = 2;
+  size_t nb_calc_thread = 4;
+  float learning_rate = 0.1;  
+  struct networks_qlearning *nnetworks = create_nework_qlearning(
+    pconf,
+    randomize, minR, maxR,  randomRange,
+    nb_prod_thread, nb_calc_thread,
+    learning_rate
+  );
+
+  struct status_qlearning *qlstatus = create_status_qlearning ();
+  struct delay_params *dly = create_delay_params (
+    100/*size_t delay_between_episodes*/,
+    10/*size_t delay_between_games*/
+  );
+  
+  struct qlearning_params *qlparams = create_qlearning_params (
+    0.95/*float gamma*/,
+    learning_rate,
+    0 /* (not used!)float discount_factor*/,
+    0.85  /*float exploration_factor*/,
+    20/*long int nb_training_before_update_weight_in_target*/,
+    10000/*size_t number_episodes*/
+  );
+/*   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, d_f_act , df );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, f_act, f );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, d_f_act , df );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, f_act , f );
+*/
+  struct print_params *pprint = create_print_params(
+    20/*float scale_x*/,40 /*float scale_y*/,  
     dly/*struct delay_params * dly_p*/
   );