add some test deep reinforcement learning

deepQlearn_0/src/deepQlearning/learn_to_drive.c

@@ -352,6 +352,16 @@ if(/*(qlStatus->nb_episodes %125 == 0)  &&*/  pprint->printed){
   }
 }
 
+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;
@@ -387,7 +397,14 @@ void learn_to_drive(struct RL_agent * rlAgent){
           //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;
         }

deepQlearn_0/src/deepQlearning/learn_to_drive.h

@@ -14,6 +14,7 @@
 
 
 #include "neuron_t/neuron_t.h"
+#include "neuron_t/nneuron_t_file.h"
 
 #include "list_t/list_t.h"
 

deepQlearn_0/src/deepQlearning/vehicle.c

@@ -342,9 +342,12 @@ void print2D_blocks_indexOne_withPoint(struct blocks *blk, float scale_x, float
         if(is_in_blocks(blk_point, coord))
           printf("\033[0;31m"); // red
         int in = is_in_blocks(blk,coord);
-        if(in)
+        if(in){
-          printf("%d",in);
+          if(in>9){
-        else 
+            int div=in%10;
+            printf("%d",div);
+          }else printf("%d",in);
+        }else 
           printf("."); //printf(" ");
         printf("\033[0;37m"); // white
       }

deepQlearn_0/test/is_good.c

@@ -457,7 +457,7 @@ TEST(first_learn_vehicle_rev50_8){
   int randomRange = 500;
   size_t nb_prod_thread = 2;
   size_t nb_calc_thread = 4;
-  float learning_rate = 0.001;  
+  float learning_rate =0.00001 /*0.001*/;  
   struct networks_qlearning *nnetworks = create_nework_qlearning(
     pconf,
     randomize, minR, maxR,  randomRange,
@@ -465,7 +465,10 @@ TEST(first_learn_vehicle_rev50_8){
     learning_rate
   );
 
-  struct status_qlearning *qlstatus = create_status_qlearning ();
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20240717_01h42m16s_5300.txt");
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20240717_01h42m16s_5300.txt");
+
+struct status_qlearning *qlstatus = create_status_qlearning ();
   struct delay_params *dly = create_delay_params (
     500/*size_t delay_between_episodes*/,
     50/*size_t delay_between_games*/
@@ -475,7 +478,7 @@ TEST(first_learn_vehicle_rev50_8){
     0.95/*float gamma*/,
     learning_rate,
     0 /* (not used!)float discount_factor*/,
-    0.99  /*float exploration_factor*/,
+    0.0001 /* 0.99*/  /*float exploration_factor*/,
     20/*long int nb_training_before_update_weight_in_target*/,
     10000/*size_t number_episodes*/
   );
@@ -496,7 +499,8 @@ TEST(first_learn_vehicle_rev50_8){
     pprint /*struct print_params * pprint*/,
     qlparams/*struct qlearning_params *qlearnParams*/
   );
-
+char c;
+scanf("%c",&c);
   learn_to_drive(rlAgent);
 
   free_RL_agent(rlAgent);
@@ -515,9 +519,26 @@ TEST(first_learn_vehicle_50__9){
   size_t dim= 2;
   struct blocks * path = create_blocks(nb_block, dim);
 
-
-
 #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[4], (float[]){0,0});
   copy_coordinate(path->upper_bound_block[4], (float[]){150,250});
   copy_coordinate(path->lower_bound_block[3], (float[]){150,40});
@@ -534,22 +555,6 @@ TEST(first_learn_vehicle_50__9){
   copy_coordinate(path->upper_bound_block[5], (float[]){410,300});
 
 
-/*
-    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});
@@ -616,15 +621,22 @@ TEST(first_learn_vehicle_50__9){
   int randomRange = 500;
   size_t nb_prod_thread = 2;
   size_t nb_calc_thread = 4;
-  float learning_rate = 0.001;  
+  float learning_rate = 0.00001 /* 0.001*/;  
   struct networks_qlearning *nnetworks = create_nework_qlearning(
     pconf,
     randomize, minR, maxR,  randomRange,
     nb_prod_thread, nb_calc_thread,
     learning_rate
   );
+/*
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20240717_01h42m16s_5300.txt");
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20240717_01h42m16s_5300.txt");
+*/
 
-  struct status_qlearning *qlstatus = create_status_qlearning ();
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20240717_09h11m09s_1700.txt");
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20240717_09h11m09s_1700.txt");
+
+struct status_qlearning *qlstatus = create_status_qlearning ();
   struct delay_params *dly = create_delay_params (
     500/*size_t delay_between_episodes*/,
     50/*size_t delay_between_games*/
@@ -634,7 +646,7 @@ TEST(first_learn_vehicle_50__9){
     0.95/*float gamma*/,
     learning_rate,
     0 /* (not used!)float discount_factor*/,
-    0.99  /*float exploration_factor*/,
+    0.0001/*0.99*/  /*float exploration_factor*/,
     20/*long int nb_training_before_update_weight_in_target*/,
     10000/*size_t number_episodes*/
   );
@@ -656,6 +668,7 @@ TEST(first_learn_vehicle_50__9){
     qlparams/*struct qlearning_params *qlearnParams*/
   );
 
+
   learn_to_drive(rlAgent);
 
   free_RL_agent(rlAgent);
@@ -668,6 +681,353 @@ TEST(first_learn_vehicle_50__9){
 
 
 
+#if 1
+TEST(first_learn_vehicle_50__10){
+  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[]){0,0});
+  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[4], (float[]){0,0});
+  copy_coordinate(path->upper_bound_block[4], (float[]){150,250});
+  copy_coordinate(path->lower_bound_block[3], (float[]){150,40});
+  copy_coordinate(path->upper_bound_block[3], (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[1], (float[]){360,70});
+  copy_coordinate(path->upper_bound_block[1], (float[]){600,150});
+  copy_coordinate(path->lower_bound_block[0], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[0], (float[]){760,300});
+  copy_coordinate(path->lower_bound_block[6], (float[]){260,300});
+  copy_coordinate(path->upper_bound_block[6], (float[]){760,360});
+  copy_coordinate(path->lower_bound_block[5], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[5], (float[]){410,300});
+
+
+
+
+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[]){0,300});
+  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 = 500;
+  size_t nb_prod_thread = 2;
+  size_t nb_calc_thread = 4;
+  float learning_rate = 0.00001 /* 0.001*/;  
+  struct networks_qlearning *nnetworks = create_nework_qlearning(
+    pconf,
+    randomize, minR, maxR,  randomRange,
+    nb_prod_thread, nb_calc_thread,
+    learning_rate
+  );
+/*
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20240717_01h42m16s_5300.txt");
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20240717_01h42m16s_5300.txt");
+*/
+
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20240717_09h11m09s_1700.txt");
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20240717_09h11m09s_1700.txt");
+
+struct status_qlearning *qlstatus = create_status_qlearning ();
+  struct delay_params *dly = create_delay_params (
+    500/*size_t delay_between_episodes*/,
+    50/*size_t delay_between_games*/
+  );
+  
+  struct qlearning_params *qlparams = create_qlearning_params (
+    0.95/*float gamma*/,
+    learning_rate,
+    0 /* (not used!)float discount_factor*/,
+    0.0001/*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__11){
+  size_t nb_block = 10;
+  size_t dim= 2;
+  struct blocks * path = create_blocks(nb_block, dim);
+
+#if 1
+
+  copy_coordinate(path->lower_bound_block[9], (float[]){0,0});
+  copy_coordinate(path->upper_bound_block[9], (float[]){100,250});
+  copy_coordinate(path->lower_bound_block[0], (float[]){100,0});
+  copy_coordinate(path->upper_bound_block[0], (float[]){250,80});
+  copy_coordinate(path->lower_bound_block[1], (float[]){250,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){360,140});
+  copy_coordinate(path->lower_bound_block[2], (float[]){360,70});
+  copy_coordinate(path->upper_bound_block[2], (float[]){600,140});
+  copy_coordinate(path->lower_bound_block[3], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[3], (float[]){720,300});
+  copy_coordinate(path->lower_bound_block[4], (float[]){300,300});
+  copy_coordinate(path->upper_bound_block[4], (float[]){720,350});
+  copy_coordinate(path->lower_bound_block[5], (float[]){300,150});
+  copy_coordinate(path->upper_bound_block[5], (float[]){410,300});
+  copy_coordinate(path->lower_bound_block[6], (float[]){120,150});
+  copy_coordinate(path->upper_bound_block[6], (float[]){300,210});
+  copy_coordinate(path->lower_bound_block[7], (float[]){120,210});
+  copy_coordinate(path->upper_bound_block[7], (float[]){270,350});
+  copy_coordinate(path->lower_bound_block[8], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[8], (float[]){120,350});
+ 
+
+
+/*
+
+  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[3], (float[]){150,40});
+  copy_coordinate(path->upper_bound_block[3], (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[1], (float[]){360,70});
+  copy_coordinate(path->upper_bound_block[1], (float[]){600,150});
+  copy_coordinate(path->lower_bound_block[0], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[0], (float[]){760,300});
+  copy_coordinate(path->lower_bound_block[6], (float[]){260,300});
+  copy_coordinate(path->upper_bound_block[6], (float[]){760,360});
+  copy_coordinate(path->lower_bound_block[5], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[5], (float[]){410,300});
+
+
+
+
+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[]){0,300});
+  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 = 500;
+  size_t nb_prod_thread = 2;
+  size_t nb_calc_thread = 4;
+  float learning_rate = 0.00001 /* 0.001*/;  
+  struct networks_qlearning *nnetworks = create_nework_qlearning(
+    pconf,
+    randomize, minR, maxR,  randomRange,
+    nb_prod_thread, nb_calc_thread,
+    learning_rate
+  );
+
+//print_vehicle_n_path(car, 12, 12);
+
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20240717_09h11m09s_1700.txt");
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20240717_09h11m09s_1700.txt");
+
+struct status_qlearning *qlstatus = create_status_qlearning ();
+  struct delay_params *dly = create_delay_params (
+    500/*size_t delay_between_episodes*/,
+    50/*size_t delay_between_games*/
+  );
+  
+  struct qlearning_params *qlparams = create_qlearning_params (
+    0.95/*float gamma*/,
+    learning_rate,
+    0 /* (not used!)float discount_factor*/,
+    0.0001/*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){
   size_t nb_block = 7;

neuron_t/src/neuron_t/nneuron_t_file.h

@@ -193,7 +193,7 @@ do{\
       \
       ppEnd=ttmp;\
       if( !bracketsDown){\
-        while(*ttmp!=0 && *ppEnd!=']' ){\
+        while(*ttmp!='\0' && *ppEnd!=']' ){\
           ss = strtoul(ttmp, &ppEnd, 10);\
           while(ttmp == ppEnd && *ttmp!='\0'  && ppEnd[0] !=']'){\
             ttmp++;\
@@ -207,11 +207,11 @@ do{\
         if( *ttmp ==']'){\
           dim=create_dim_from_list_perm(l_p);\
           bracketsDown = true;\
-          ttmp++; ppEnd++;\
+          /*ttmp++; ppEnd++;*/\
         }\
       \
       }\
-      else{/*if(bracketsDown)*/\
+      if(bracketsDown){\
  \
         if(T->dim->rank == dim->rank){\
           \
@@ -235,7 +235,6 @@ do{\
         }else {\
           tensorNotMatched = true;\
           Done = true;\
-          printf(" T->%s doesn't have the same rank as the input ! extract failed\n",#attribute);\
           break;\
         }\
       }\

neuron_t/test/is_good.c

@@ -399,7 +399,6 @@ TEST(copy_weight_in_neurons){
 }
 
 
-
 TEST(Extract_weight_in_neurons){
    bool rec_randomizeInitWeight = randomizeInitWeight;
    randomizeInitWeight =false;
@@ -424,7 +423,7 @@ TEST(Extract_weight_in_neurons){
   setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 ,  0.1);
 
 
-  size_t reps = learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
+  size_t reps = 1;// learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
   EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, bn, weight_in, ".ff_bn_weight_in__toExtract.txt")
   
   setup_all_layers_functions_TYPE_FLOAT(cpyn,
@@ -468,6 +467,77 @@ TEST(Extract_weight_in_neurons){
 
 
 
+TEST(Extract_EXPORT_weight_in_neurons){
+   bool rec_randomizeInitWeight = randomizeInitWeight;
+   randomizeInitWeight =false;
+  
+  data_set_TYPE_FLOAT *ds= fill_data_set_from_file_TYPE_FLOAT("xor.txt",1);
+//  print_data_set_msg_TYPE_FLOAT(ds,"data");
+  //config_layers *pconf = create_config_layers_from_OneD(3,(size_t[]){2,4,1}); /* 2 input , 1 target; 1 hidden layer with 5 neurons */
+  config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,24,24,3});
+  neurons_TYPE_FLOAT *bn=NULL, *tmp ;
+  neurons_TYPE_FLOAT *cpyn=NULL;
+  //setup_networks_alloutputs_config_GLOBAL_rdm01_TYPE_FLOAT(setup_networks_alloutputs_config_TYPE_FLOAT(&bn,pconf);bn,pconf);
+     setup_networks_alloutputs_config_TYPE_FLOAT(&bn,pconf,false,0,1,5000);
+     setup_networks_alloutputs_config_TYPE_FLOAT(&cpyn, pconf,false,0,1,5000);
+  
+  EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, bn, weight_in, ".ff_target_20240717_01h43m41s_13300.txt")
+
+  setup_all_layers_functions_TYPE_FLOAT(bn,
+    tensorContractnProdThread_TYPE_FLOAT,
+    tensorProdThread_TYPE_FLOAT,
+    DL,
+    L,
+    f,
+    df);
+
+  setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 ,  0.1);
+
+
+  size_t reps = 1;// learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
+  EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, bn, weight_in, ".ff_bn_weight_in__toCMP.txt")
+  
+  setup_all_layers_functions_TYPE_FLOAT(cpyn,
+    tensorContractnProdThread_TYPE_FLOAT,
+    tensorProdThread_TYPE_FLOAT,
+    DL,
+    L,
+    f,
+    df);
+
+  setup_all_layers_params_TYPE_FLOAT(cpyn, 5, 1 ,  0.1);
+
+//  EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, cpyn, weight_in, ".ff_bn_weight_in__toExtract.txt")
+//  copy_weight_in_neurons_TYPE_FLOAT(cpyn, bn);
+ 
+  char msg[256];
+  tensor_TYPE_FLOAT * linked_tens = NULL;
+  for(size_t i=0; i<ds->size; ++i){
+//    print_predict_by_network_with_error_neurons_TYPE_FLOAT(bn,ds->input[i],ds->target[i]);
+ //   print_predict_by_network_with_error_neurons_TYPE_FLOAT(cpyn,ds->input[i],ds->target[i]);
+    calculate_output_by_network_neurons_TYPE_FLOAT(bn,ds->input[i],&linked_tens);
+    sprintf(msg," output base %ld ",i);
+    print_tensor_msg_TYPE_FLOAT(linked_tens,msg);
+    calculate_output_by_network_neurons_TYPE_FLOAT(cpyn,ds->input[i],&linked_tens);
+    sprintf(msg," output copy %ld ",i);
+    print_tensor_msg_TYPE_FLOAT(linked_tens,msg);
+  }
+
+ 
+//  EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, cpyn, weight_in, ".ff_bn_weight_in__exportedCPYfromExtract.txt")
+
+
+  free_data_set_TYPE_FLOAT(ds);
+  free_neurons_TYPE_FLOAT(bn); 
+  free_neurons_TYPE_FLOAT(cpyn); 
+
+  LOG("reps = %ld\n",reps);
+  randomizeInitWeight = rec_randomizeInitWeight;
+}
+
+
+
+
 
 
 int main(int argc, char **argv){