debug : nan, it's the learning rate too high,s try to find optimum: 0.001
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fanasina committed
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@@ -61,6 +61,7 @@ struct arg_bash{
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pthread_mutex_t *mut_bash_var;
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pthread_cond_t *cond_bash_var;
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int go_on;
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int weight_net_print;
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};
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struct arg_bash *create_arg_bash();
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@@ -129,10 +129,11 @@ void* runBashPrint(void *arg){
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while( check_go_on_bash(bash_arg) && (new_bash_exist(bash_arg)) && check_go_on_print_params(pprint) && !is_ending(qlStatus)){
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if(/*(qlStatus->nb_episodes %125 == 0) &&*/ pprint->printed){
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//pthread_mutex_lock(&(pprint->mut_printed));
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pthread_mutex_lock(&(car->mut_coord));
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bash_print_vehicle_n_path(car, pprint->scale_x, pprint->scale_y,bash_arg);
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pthread_mutex_unlock(&(car->mut_coord));
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if(bash_arg->weight_net_print == 0){
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pthread_mutex_lock(&(car->mut_coord));
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bash_print_vehicle_n_path(car, pprint->scale_x, pprint->scale_y,bash_arg);
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pthread_mutex_unlock(&(car->mut_coord));
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}
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//pthread_mutex_unlock(&(pprint->mut_printed));
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////printf("%s ",pprint->string_space);
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len_buf=sprintf(buf,"%s ",pprint->string_space);
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@@ -150,7 +151,7 @@ void* runBashPrint(void *arg){
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}
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////printf("\n< %5.2f > ( %s ) \n", car->direction, action_name[qlStatus->action % COUNT_ACTION]);
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len_buf=sprintf(buf,"\n< %5.2f > ( %s ) \n", car->direction, action_name[qlStatus->action % COUNT_ACTION]);
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len_buf=sprintf(buf,"\n< %5.2f ( %s ) \n", car->direction, action_name[qlStatus->action % COUNT_ACTION]);
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BASH_WRITE_IF_EXIST(bash_arg, buf, len_buf)
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//print_weight_in_neurons_TYPE_FLOAT(net_main, "net_main_wei");
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//PRINT_ATTRIBUTE_TENS_IN_ALL_LAYERS(TYPE_FLOAT, net_main, weight_in, "net_main_we_in");
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@@ -158,9 +159,15 @@ void* runBashPrint(void *arg){
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//PRINT_ATTRIBUTE_TENS_IN_ALL_LAYERS(TYPE_FLOAT, net_target, output, "net_target_out");
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//PRINT_ATTRIBUTE_TENS_IN_ALL_LAYERS(TYPE_FLOAT, net_main, input, "net_main_input");
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////printf(" action : %d , factor : %f nb_episodes : %ld \n",qlStatus->action,rlAgent->qlearnParams->exploration_factor, rlAgent->status->nb_episodes);
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BASH_PRINT_ATTRIBUTE_TENS_IN_ALL_LAYERS(TYPE_FLOAT, bash_arg, net_main, weight_in, "net_main_we_in");
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len_buf=sprintf(buf," action : %d , factor : %f nb_episodes : %ld \n",qlStatus->action,rlAgent->qlearnParams->exploration_factor, rlAgent->status->nb_episodes);
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if(bash_arg->weight_net_print){
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BASH_PRINT_ATTRIBUTE_TENS_IN_ALL_LAYERS(TYPE_FLOAT, bash_arg, net_target, weight_in, "net_main_we_in", false);
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}
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len_buf=sprintf(buf," action : %d , learning_rate: %f, factor : %f nb_episodes : %ld \n",
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qlStatus->action,
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rlAgent->qlearnParams->learning_rate,
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rlAgent->qlearnParams->exploration_factor,
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rlAgent->status->nb_episodes);
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BASH_WRITE_IF_EXIST(bash_arg, buf, len_buf)
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FOR_LIST_FORM_BEGIN(TYPE_L_INT, qlStatus->progress_best_cumul){
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@@ -116,6 +116,7 @@ struct arg_bash *create_arg_bash(){
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pthread_cond_init(b_arg->cond_bash_var,NULL);
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b_arg->go_on=1;
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b_arg->weight_net_print=0;
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b_arg->thread_launch=NULL;
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b_arg->thread_run_newbash=NULL;
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@@ -336,7 +336,7 @@ HIDE_TEST(_first_learn_vehicle_50__9){
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0.001 /*0.01*/ /* 0.001*/;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -364,12 +364,12 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
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20/*long int nb_training_before_update_weight_in_target*/,
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10000/*size_t number_episodes*/
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);
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#if 0
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UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, d_f_act , df );
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UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, f_act, f );
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UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, d_f_act , df );
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UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, f_act , f );
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#endif
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struct print_params *pprint = create_print_params(
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12/*float scale_x*/,12 /*float scale_y*/,
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dly/*struct delay_params * dly_p*/
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@@ -529,7 +529,7 @@ HIDE_TEST(_first_learn_vehicle_50__10){
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0.00001 /* 0.001*/;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -614,33 +614,13 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
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}
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#endif
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TEST(_first_learn_vehicle_50__11_9){
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HIDE_TEST(_first_learn_vehicle_50__11_9){
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size_t nb_block = 7;
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size_t dim= 2;
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struct blocks * path = create_blocks(nb_block, dim);
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#if 1
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copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
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copy_coordinate(path->upper_bound_block[0], (float[]){100,250});
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copy_coordinate(path->lower_bound_block[1], (float[]){100,0});
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copy_coordinate(path->upper_bound_block[1], (float[]){250,80});
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copy_coordinate(path->lower_bound_block[2], (float[]){250,0});
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copy_coordinate(path->upper_bound_block[2], (float[]){360,140});
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copy_coordinate(path->lower_bound_block[3], (float[]){360,70});
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copy_coordinate(path->upper_bound_block[3], (float[]){600,140});
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copy_coordinate(path->lower_bound_block[4], (float[]){600,90});
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copy_coordinate(path->upper_bound_block[4], (float[]){720,300});
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copy_coordinate(path->lower_bound_block[5], (float[]){300,300});
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copy_coordinate(path->upper_bound_block[5], (float[]){720,350});
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copy_coordinate(path->lower_bound_block[6], (float[]){0,250});
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copy_coordinate(path->upper_bound_block[6], (float[]){410,300});
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#else
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copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
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copy_coordinate(path->upper_bound_block[0], (float[]){150,250});
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copy_coordinate(path->lower_bound_block[1], (float[]){150,0});
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@@ -657,9 +637,30 @@ TEST(_first_learn_vehicle_50__11_9){
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copy_coordinate(path->upper_bound_block[6], (float[]){410,300});
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#else
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copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
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copy_coordinate(path->upper_bound_block[0], (float[]){100,250});
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copy_coordinate(path->lower_bound_block[1], (float[]){100,0});
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copy_coordinate(path->upper_bound_block[1], (float[]){250,80});
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copy_coordinate(path->lower_bound_block[2], (float[]){250,0});
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copy_coordinate(path->upper_bound_block[2], (float[]){360,140});
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copy_coordinate(path->lower_bound_block[3], (float[]){360,70});
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copy_coordinate(path->upper_bound_block[3], (float[]){600,140});
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copy_coordinate(path->lower_bound_block[4], (float[]){600,90});
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copy_coordinate(path->upper_bound_block[4], (float[]){720,300});
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copy_coordinate(path->lower_bound_block[5], (float[]){300,300});
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copy_coordinate(path->upper_bound_block[5], (float[]){720,350});
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copy_coordinate(path->lower_bound_block[6], (float[]){0,250});
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copy_coordinate(path->upper_bound_block[6], (float[]){410,300});
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///////////////////////////////////////
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#if 0
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copy_coordinate(path->lower_bound_block[0], (float[]){0,3});
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copy_coordinate(path->upper_bound_block[0], (float[]){4,7});
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copy_coordinate(path->lower_bound_block[1], (float[]){1,0});
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@@ -675,6 +676,7 @@ TEST(_first_learn_vehicle_50__11_9){
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copy_coordinate(path->lower_bound_block[6], (float[]){1,7});
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copy_coordinate(path->upper_bound_block[6], (float[]){8,9.75});
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#endif
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#endif
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@@ -683,15 +685,15 @@ TEST(_first_learn_vehicle_50__11_9){
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struct vehicle *car = create_vehicle(path);
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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 */
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//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 */
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//config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,4,4,3}); /* 3 input , 3 target; 2 hidden layer with 14 neurons each */
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bool randomize=true;
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float minR = -0.5, maxR = 0.5;
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int randomRange = 500;
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0.001; /* 0.00001*/ /* 0.001*/;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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float learning_rate = 0.01; /* 0.00001*/ /* 0.001*/;
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -771,7 +773,7 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
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}
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#if 1
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HIDE_TEST(_first_learn_vehicle_50__11){
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TEST(_first_learn_vehicle_50__11){
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size_t nb_block = 7;
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size_t dim= 2;
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struct blocks * path = create_blocks(nb_block, dim);
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@@ -873,15 +875,16 @@ copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
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struct vehicle *car = create_vehicle(path);
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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 */
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//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 */
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//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 */
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bool randomize=true;
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float minR = -0.5, maxR = 0.5;
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//float minR = 0, maxR = 1;
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int randomRange = 500;
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0; /* 0.000001*/ /* 0.001*/;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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float learning_rate = 0.0007 /*0.001*//* 0.0001*/; /* 0.000001*/ /* 0.001*/;
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -894,8 +897,8 @@ EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, wei
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//EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20250508_17h50m56s_26300.txt");
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//EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20250508_17h50m56s_26300.txt");
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EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_.symlink");
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EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_.symlink");
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///EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_.symlink");
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///EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_.symlink");
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/*
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EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20250508_23h02m40s_29000.txt");
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EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20250508_23h02m40s_29000.txt");
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@@ -910,7 +913,7 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
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0.95/*float gamma*/,
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learning_rate,
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0 /* (not used!)float discount_factor*/,
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0.0001/*0.99*/ /*float exploration_factor*/,
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1.0/*0.99*//*0.0001*//*0.99*/ /*float exploration_factor*/,
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20/*long int nb_training_before_update_weight_in_target*/,
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10000/*size_t number_episodes*/
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);
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@@ -1086,7 +1089,7 @@ copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0.0000001 /* 0.001*/;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -1233,7 +1236,7 @@ HIDE_TEST(__first_learn_vehicle13){
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0.1;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -1371,7 +1374,7 @@ HIDE_TEST(first_learn_vehicle){
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0.1;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -1482,7 +1485,7 @@ HIDE_TEST(first_learn_vehicle){
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0.1;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -2006,7 +2009,7 @@ HIDE_TEST(first_learn_vehicle_rev50_8){
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0.001; // 0.00001 /*0.001*/;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -2170,7 +2173,7 @@ HIDE_TEST(first_learn_vehicle_50__9){
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0.00001 /* 0.001*/;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -2340,7 +2343,7 @@ HIDE_TEST(first_learn_vehicle_50__10){
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0.00001 /* 0.001*/;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -2512,7 +2515,7 @@ copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0; /* 0.000001*/ /* 0.001*/;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -2691,7 +2694,7 @@ copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0.0000001 /* 0.001*/;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
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@@ -2813,7 +2816,7 @@ HIDE_TEST(_first_learn_vehicle13){
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size_t nb_prod_thread = 2;
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size_t nb_calc_thread = 4;
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float learning_rate = 0.1;
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struct networks_qlearning *nnetworks = create_nework_qlearning(
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struct networks_qlearning *nnetworks = create_network_qlearning(
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pconf,
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randomize, minR, maxR, randomRange,
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nb_prod_thread, nb_calc_thread,
|
||||
@@ -2951,7 +2954,7 @@ HIDE_TEST(first_learn_vehicle){
|
||||
size_t nb_prod_thread = 2;
|
||||
size_t nb_calc_thread = 4;
|
||||
float learning_rate = 0.1;
|
||||
struct networks_qlearning *nnetworks = create_nework_qlearning(
|
||||
struct networks_qlearning *nnetworks = create_network_qlearning(
|
||||
pconf,
|
||||
randomize, minR, maxR, randomRange,
|
||||
nb_prod_thread, nb_calc_thread,
|
||||
@@ -3062,7 +3065,7 @@ HIDE_TEST(first_learn_vehicle){
|
||||
size_t nb_prod_thread = 2;
|
||||
size_t nb_calc_thread = 4;
|
||||
float learning_rate = 0.1;
|
||||
struct networks_qlearning *nnetworks = create_nework_qlearning(
|
||||
struct networks_qlearning *nnetworks = create_network_qlearning(
|
||||
pconf,
|
||||
randomize, minR, maxR, randomRange,
|
||||
nb_prod_thread, nb_calc_thread,
|
||||
|
||||
Reference in new issue
Block a user