update: [tensor] add transpose optimized function and tests

tranfert learning find quickly a path (less crash) but from scratch learning find optimal path (shorter and faster)

deepQlearn_0/src/deepQlearning/learn_to_drive.c

@@ -74,13 +74,14 @@ struct networks_qlearning * create_network_qlearning(
   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_functions_TYPE_FLOAT(qnets->main_net, tensorContractnProdThreadOpt0_TYPE_FLOAT, tensorProdThread_TYPE_FLOAT, D_L2, L2, reLU, d_reLU);
+  //setup_all_layers_functions_TYPE_FLOAT(qnets->main_net, tensorContractnProdThread_TYPE_FLOAT, tensorProdThread_TYPE_FLOAT, D_L2, L2, reLU, d_reLU);
   //setup_all_layers_functions_TYPE_FLOAT(qnets->main_net, tensorContractnProdTHR_TYPE_FLOAT, tensorProdTHR_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_functions_TYPE_FLOAT(qnets->target_net, tensorContractnProdThreadOpt0_TYPE_FLOAT, tensorProdThread_TYPE_FLOAT, D_L2, L2, reLU, d_reLU);
   //setup_all_layers_functions_TYPE_FLOAT(qnets->target_net, tensorContractnProdTHR_TYPE_FLOAT, tensorProdTHR_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_functions_TYPE_FLOAT(qnets->best_net, tensorContractnProdThreadOpt0_TYPE_FLOAT, tensorProdThread_TYPE_FLOAT, D_L2, L2, reLU, d_reLU);
   //setup_all_layers_functions_TYPE_FLOAT(qnets->best_net, tensorContractnProdTHR_TYPE_FLOAT, tensorProdTHR_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);
 
@@ -276,6 +277,7 @@ void free_RL_agent(struct RL_agent *rlAgent){
 }
 
 #define ACCEPTABLE_REWARD 1000
+#define VERY_GOOD_REWARD 10000
 
 #define UPDATE_PARAMS 1
 #define UPDATE_EXPLOR_FAC 1
@@ -322,6 +324,7 @@ void train_qlearning(struct RL_agent * rlAgent,
 #if UPDATE_PARAMS
 	if((car_status->cumulative_reward > ACCEPTABLE_REWARD) || (rlAgent->status->nb_episodes % 100 == 0) ){ 
   	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*/ );
+	  if(car_status->cumulative_reward > VERY_GOOD_REWARD) new_value = (net_main->learning_rate ) * qlParams->factor_update_learning_rate ;
     UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, net_main, learning_rate, new_value);
     qlParams->learning_rate = new_value;
 #if UPDATE_EXPLOR_FAC

y_network_neural_network_/test/is_good.c

@@ -773,14 +773,14 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
 
 }
 
-HIDE_TEST(extract_with_pconf){
+TEST(extract_with_pconf){
   size_t nb_block = 7;
   size_t dim= 2;
   struct blocks * path = create_blocks(nb_block, dim);
 
   LOG("debug: f_name = %s\n", __func__);
 
-#if 1
+#if 0
 
   copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
   copy_coordinate(path->upper_bound_block[0], (float[]){100,250});
@@ -798,8 +798,9 @@ HIDE_TEST(extract_with_pconf){
   copy_coordinate(path->upper_bound_block[6], (float[]){410,300});
  
 
+#else 
+#if 0
 
-/*
 
   copy_coordinate(path->lower_bound_block[4], (float[]){0,0});
   copy_coordinate(path->upper_bound_block[4], (float[]){150,250});
@@ -816,10 +817,10 @@ HIDE_TEST(extract_with_pconf){
   copy_coordinate(path->lower_bound_block[5], (float[]){0,250});
   copy_coordinate(path->upper_bound_block[5], (float[]){410,300});
 
+#else
+#if 0
 
-
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
-
-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});
@@ -834,7 +835,28 @@ copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
   copy_coordinate(path->lower_bound_block[6], (float[]){0,250});
   copy_coordinate(path->upper_bound_block[6], (float[]){410,300});
 
+#else
+#if 1
 
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
+  copy_coordinate(path->upper_bound_block[0], (float[]){60,250});
+  copy_coordinate(path->lower_bound_block[1], (float[]){60,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){250,50});
+  copy_coordinate(path->lower_bound_block[2], (float[]){250,20});
+  copy_coordinate(path->upper_bound_block[2], (float[]){310,80});
+  copy_coordinate(path->lower_bound_block[3], (float[]){310,40});
+  copy_coordinate(path->upper_bound_block[3], (float[]){450,90});
+  copy_coordinate(path->lower_bound_block[4], (float[]){450,60});
+  copy_coordinate(path->upper_bound_block[4], (float[]){560,220});
+  copy_coordinate(path->lower_bound_block[5], (float[]){430,220});
+  copy_coordinate(path->upper_bound_block[5], (float[]){560,280});
+  copy_coordinate(path->lower_bound_block[6], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[6], (float[]){430,300});
+
+
+
+#else
+#if 0
 
   copy_coordinate(path->lower_bound_block[0], (float[]){0,300});
   copy_coordinate(path->upper_bound_block[0], (float[]){400,700});
@@ -850,7 +872,6 @@ copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
   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
 
@@ -870,6 +891,10 @@ copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
   copy_coordinate(path->upper_bound_block[6], (float[]){8,9.75});
  
  
+#endif
+#endif
+#endif
+#endif
 #endif
 
   update_bounds_limits_blocks(path);
@@ -885,7 +910,7 @@ copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
   int randomRange = 500;
   size_t nb_prod_thread = 2;
   size_t nb_calc_thread = 4;
-  float learning_rate = 0.0007 /*0.001*//* 0.0001*/; /* 0.000001*/ /* 0.001*/;  
+  float learning_rate = 0.007 /*0.001*//* 0.0001*/; /* 0.000001*/ /* 0.001*/;  
   struct networks_qlearning *nnetworks = create_network_qlearning(
     pconf,
     randomize, minR, maxR,  randomRange,
@@ -979,7 +1004,7 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
   struct arg_run_qlearn_bprint *argQL_BP =   create_arg_run_qlearn_bprint(bash_arg, rlAgent);
 
   struct arg_var_ * var = create_arg_var_(y_nnn_manager_handle_input, argQL_BP);
-  struct y_socket_t *argS = y_socket_create("1600", 2, 3, var);
+  struct y_socket_t *argS = y_socket_create("1609", 2, 3, var);
  
 
   pthread_t pollTh;
@@ -1006,7 +1031,7 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
 
 
 
-TEST(Transfert_learn_mini_learn){
+HIDE_TEST(Transfert_learn_mini_learn){
   size_t nb_block = 7;
   size_t dim= 2;
   struct blocks * path = create_blocks(nb_block, dim);
@@ -1031,7 +1056,7 @@ TEST(Transfert_learn_mini_learn){
   copy_coordinate(path->upper_bound_block[6], (float[]){410,300});
  
 #else
-#if 1
+#if 0
 
 
   copy_coordinate(path->lower_bound_block[4], (float[]){0,0});
@@ -1051,7 +1076,7 @@ TEST(Transfert_learn_mini_learn){
 
 
 #else
-#if 0
+#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});
@@ -1122,7 +1147,7 @@ copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
   int randomRange = 500;
   size_t nb_prod_thread = 2;
   size_t nb_calc_thread = 4;
-  float learning_rate = 0.000001 /*0.001*//* 0.0001*/; /* 0.000001*/ /* 0.001*/;  
+  float learning_rate = 0.00001 /*0.001*//* 0.0001*/; /* 0.000001*/ /* 0.001*/;  
   struct networks_qlearning *nnetworks = create_network_qlearning(
     pconf,
     randomize, minR, maxR,  randomRange,
@@ -1136,8 +1161,8 @@ EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, wei
   struct main_list_ptr_DIMENSION *m_l_dim=create_var_list_ptr_DIMENSION();
   //struct main_list_dimension *m_l_dim=create_var_list_dimension();
 
-EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS_PCONF(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_TEST_extract_with_pconf____9.symlink",m_l_dim);
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS_PCONF(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_learnDir/.ff_main_TEST_extract_with_pconf____9;1770646800;2400;",m_l_dim);
-EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS_PCONF(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_TEST_extract_with_pconf____9.symlink",m_l_dim);
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS_PCONF(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_learnDir/.ff_target_TEST_extract_with_pconf____9;1770646800;2400;",m_l_dim);
  
  for(struct list_ptr_DIMENSION *local_l_dim=m_l_dim->begin_list; local_l_dim; local_l_dim=local_l_dim->next){
    size_t i=local_l_dim->index;
@@ -1182,7 +1207,7 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
     0.95/*float gamma*/,
     learning_rate,
     0 /* (not used!)float discount_factor*/,
-    0.000001/*1.0*//*0.99*//*0.0001*//*0.99*/  /*float exploration_factor*/,
+    0.0001/*1.0*//*0.99*//*0.0001*//*0.99*/  /*float exploration_factor*/,
     20/*long int nb_training_before_update_weight_in_target*/,
     10000/*size_t number_episodes*/
   );
@@ -1217,7 +1242,269 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
   struct arg_run_qlearn_bprint *argQL_BP =   create_arg_run_qlearn_bprint(bash_arg, rlAgent);
 
   struct arg_var_ * var = create_arg_var_(y_nnn_manager_handle_input, argQL_BP);
-  struct y_socket_t *argS = y_socket_create("1600", 2, 3, var);
+  struct y_socket_t *argS = y_socket_create("1613", 2, 3, var);
+ 
+
+  pthread_t pollTh;
+  pthread_create(&pollTh, NULL, y_socket_poll_fds, (void*)argS);
+
+
+
+
+
+
+
+  pthread_join(pollTh, NULL);
+	//pthread_join(thread_learn, NULL);
+  
+  y_socket_free(argS);
+  free_arg_var_(var);
+  free_arg_run_qlearn_bprint(argQL_BP);
+  //free_RL_agent(rlAgent);
+
+
+
+
+}
+
+
+
+TEST(transfertlearning_extract_with_pconf){
+  size_t nb_block = 7;
+  size_t dim= 2;
+  struct blocks * path = create_blocks(nb_block, dim);
+
+  LOG("debug: f_name = %s\n", __func__);
+
+#if 0
+
+  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});
+ 
+
+#else 
+#if 0
+
+
+  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});
+
+#else
+#if 0
+
+  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});
+
+#else
+#if 1
+
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
+  copy_coordinate(path->upper_bound_block[0], (float[]){60,250});
+  copy_coordinate(path->lower_bound_block[1], (float[]){60,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){250,50});
+  copy_coordinate(path->lower_bound_block[2], (float[]){250,20});
+  copy_coordinate(path->upper_bound_block[2], (float[]){310,80});
+  copy_coordinate(path->lower_bound_block[3], (float[]){310,40});
+  copy_coordinate(path->upper_bound_block[3], (float[]){450,90});
+  copy_coordinate(path->lower_bound_block[4], (float[]){450,60});
+  copy_coordinate(path->upper_bound_block[4], (float[]){560,220});
+  copy_coordinate(path->lower_bound_block[5], (float[]){430,220});
+  copy_coordinate(path->upper_bound_block[5], (float[]){560,280});
+  copy_coordinate(path->lower_bound_block[6], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[6], (float[]){430,300});
+
+
+
+
+#else
+#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
+#endif
+#endif
+#endif
+#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(3,(size_t[]){3,24,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
+  
+  bool randomize=true; 
+  float minR = -0.5, maxR = 0.5;  
+  //float minR = 0, maxR = 1;  
+  int randomRange = 500;
+  size_t nb_prod_thread = 2;
+  size_t nb_calc_thread = 4;
+  float learning_rate = 0.0001;  // 0.0007 /*0.001*//* 0.0001*/; /* 0.000001*/ /* 0.001*/;  
+  struct networks_qlearning *nnetworks = create_network_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 main_list_ptr_DIMENSION *m_l_dim=create_var_list_ptr_DIMENSION();
+  //struct main_list_dimension *m_l_dim=create_var_list_dimension();
+
+//EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS_PCONF(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_learnDir/.ff_main_TEST_extract_with_pconf____9;1770646800;2400;",m_l_dim);
+//EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS_PCONF(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_learnDir/.ff_target_TEST_extract_with_pconf____9;1770646800;2400;",m_l_dim);
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS_PCONF(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_learnDir/.ff_main_TEST_extract_with_pconf____9;1770675296;1044700;",m_l_dim);
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS_PCONF(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_learnDir/.ff_target_TEST_extract_with_pconf____9;1770675296;1044700;",m_l_dim);
+ 
+ for(struct list_ptr_DIMENSION *local_l_dim=m_l_dim->begin_list; local_l_dim; local_l_dim=local_l_dim->next){
+   size_t i=local_l_dim->index;
+   char msg[50]; sprintf(msg, " DIM[%ld] ",i);
+   printDebug_dimension((local_l_dim->value), msg);
+ }
+ LOG("%s","=========================================="); 
+  config_layers *base_conf=create_config_layers_from_weight_in_neurons_TYPE_FLOAT(nnetworks->main_net);
+  config_layers *p_conf=create_config_layers_from_m_list_ptr_DIMENSION(m_l_dim);
+  //config_layers *p_conf=create_config_layers_from_m_list_dimension(m_l_dim);
+
+  if(cmp_config_layers(p_conf, base_conf)==0){
+    LOG("base_conf == %s\n","p_conf");
+    
+  }else{
+    LOG("base_conf != %s\n","p_conf");
+
+  }
+LOG("%s ", "base_conf"); print_config_layers(base_conf);
+LOG("%s ", "p_conf"); print_config_layers(p_conf);
+free_config_layers(base_conf);
+free_config_layers(p_conf);
+//free_all_var_list_dimension(m_l_dim);
+///free_all_var_list_ptr_DIMENSION(m_l_dim);
+//remove_all_ptr_type_list_ptr_DIMENSION(m_l_dim);
+purge_ptr_type_list_ptr_DIMENSION(m_l_dim);
+
+//EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20250508_17h50m56s_26300.txt");
+///EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_.symlink");
+///EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_.symlink");
+/*
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20250508_23h02m40s_29000.txt");
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20250508_23h02m40s_29000.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*/,
+    0.01/*1.0*//*0.99*//*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 );
+*/
+  qlparams->caller_func_name=malloc(strlen(__func__)+1);
+  strcpy(qlparams->caller_func_name, __func__);
+
+
+  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);
+ 	//learn_to_drive(rlAgent);
+  
+	struct arg_bash *bash_arg= create_arg_bash();
+
+  struct arg_run_qlearn_bprint *argQL_BP =   create_arg_run_qlearn_bprint(bash_arg, rlAgent);
+
+  struct arg_var_ * var = create_arg_var_(y_nnn_manager_handle_input, argQL_BP);
+  struct y_socket_t *argS = y_socket_create("1621", 2, 3, var);
  
 
   pthread_t pollTh;

y_socket_t/src/y_socket_t/y_list_var_tool.c

@@ -193,6 +193,6 @@ void usage_cmdl(){
   "\t\t\tNeed to add \"seq\" and \"tm\" keys to have good handling, the payload is after the header {}\n"
   "\t\t\tSee y_socket_send_file_for_node function.\n"
   "\t\tpost ok [filenameid]: to acknowledge receipt [filename].\n"
-  "\t\t\t[filenameid] is to precise witch [filename] (file name from whom and when)\n"
+  "\t\t\t[filenameid] is to precise wich [filename] (file name from whom and when)\n"
   );
 }