fanasina/y_project
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

qlearn: add func caller name to store result more verbose, and add record result if ending before done

Browse Source
This commit is contained in:
fanasina
2025-12-15 11:15:51 +01:00
parent 3d54639d4c
commit 0e80467c19
3 changed files with 124 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files
deepQlearn_0/src/deepQlearning/learn_to_drive.c
+105 -10
View File
@@ -7,9 +7,11 @@ char *action_name[8] = {"LEFT", "CENTER", "RIGHT"};
#define USE_THRESHOLD 0 #define USE_THRESHOLD 0
float reLU(float x){ float reLU(float x){
#if CHECK_NAN
if(x!=x){// nan if(x!=x){// nan
printf("nan relu "); printf("nan relu ");
} }
#endif
#if USE_THRESHOLD #if USE_THRESHOLD
if(x>UPPER_THRESHOLD) return UPPER_THRESHOLD; if(x>UPPER_THRESHOLD) return UPPER_THRESHOLD;
#endif #endif
@@ -191,6 +193,7 @@ struct qlearning_params * create_qlearning_params (
qparams->minimum_threshold_exploration_factor = 0.0001; qparams->minimum_threshold_exploration_factor = 0.0001;
// qparams->threshold_number_same_action = 500; // qparams->threshold_number_same_action = 500;
qparams->caller_func_name=NULL;
return qparams; return qparams;
} }
@@ -257,6 +260,7 @@ void free_print_params (struct print_params *pprint){
} }
void free_qlearning_params(struct qlearning_params *q_params){ void free_qlearning_params(struct qlearning_params *q_params){
if(q_params->caller_func_name) free(q_params->caller_func_name);
free(q_params); free(q_params);
} }
void free_RL_agent(struct RL_agent *rlAgent){ void free_RL_agent(struct RL_agent *rlAgent){
@@ -464,8 +468,8 @@ char *fileNameDateScore(char * pre, char* post,size_t score){
return filename; return filename;
} }
const char* target_symlink = ".ff_target_.symlink"; //const char* target_symlink = ".ff_target_.symlink";
const char* main_symlink = ".ff_main_.symlink"; //const char* main_symlink = ".ff_main_.symlink";
const char* dest_folder=".ff_learnDir"; const char* dest_folder=".ff_learnDir";
void* learn_to_drive(void * lrnarg){ void* learn_to_drive(void * lrnarg){
@@ -521,27 +525,58 @@ void* learn_to_drive(void * lrnarg){
int len_cumul=0; int len_cumul=0;
char cumulSTR[128]; char cumulSTR[128];
len_cumul=sprintf(cumulSTR, " %ld ", car_status->cumulative_reward); len_cumul=sprintf(cumulSTR, " %ld ", car_status->cumulative_reward);
char *mainfuncCaller=malloc(128);
char *targetfuncCaller=malloc(128);
char *mainSymlinkCaller=malloc(256);
char *targetSymlinkCaller=malloc(256);
if(qlParams->caller_func_name){
sprintf(mainfuncCaller,".ff_learnDir/.ff_main_%s",qlParams->caller_func_name);
sprintf(targetfuncCaller,".ff_learnDir/.ff_target_%s",qlParams->caller_func_name);
sprintf(mainSymlinkCaller,".ff_main_%s.symlink",qlParams->caller_func_name);
sprintf(targetSymlinkCaller,".ff_target_%s.symlink",qlParams->caller_func_name);
}else{
strcpy(mainfuncCaller,".ff_learnDir/.ff_main_");
strcpy(targetfuncCaller,".ff_learnDir/.ff_target_");
strcpy(mainSymlinkCaller,".ff_main_.symlink");
strcpy(targetSymlinkCaller,".ff_target_.symlink");
}
push_back_list_TYPE_L_INT(qlStatus->progress_best_cumul, car_status->cumulative_reward); push_back_list_TYPE_L_INT(qlStatus->progress_best_cumul, car_status->cumulative_reward);
char *file = fileNameDateScore(".ff_learnDir/.ff_main_",".txt",car_status->cumulative_reward); //char *file = fileNameDateScore(".ff_learnDir/.ff_main_",".txt",car_status->cumulative_reward);
char *file = fileNameDateScore(mainfuncCaller,"",car_status->cumulative_reward);
EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, rlAgent->networks->main_net ,weight_in, file); EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, rlAgent->networks->main_net ,weight_in, file);
unlink(main_symlink);
if(symlink(file, main_symlink)==-1){
fprintf(stderr,"debug: symlink %s with %s.\n",main_symlink, file); //unlink(main_symlink);
unlink(mainSymlinkCaller);
//if(symlink(file, main_symlink)==-1)
if(symlink(file, mainSymlinkCaller)==-1)
{
//fprintf(stderr,"debug: symlink %s with %s.\n",main_symlink, file);
fprintf(stderr,"debug: symlink %s with %s.\n",mainSymlinkCaller, file);
//fprintf(stderr,"debug: symlink %s with %s. explain:%s \n",main_symlink, file, explain_symlink(file, main_symlink) ); //fprintf(stderr,"debug: symlink %s with %s. explain:%s \n",main_symlink, file, explain_symlink(file, main_symlink) );
} }
else write(1,":",1); else write(1,":",1);
write(1,cumulSTR,len_cumul); write(1,cumulSTR,len_cumul);
free(file); free(file);
file = fileNameDateScore(".ff_learnDir/.ff_target_",".txt",car_status->cumulative_reward); //file = fileNameDateScore(".ff_learnDir/.ff_target_",".txt",car_status->cumulative_reward);
file = fileNameDateScore(targetfuncCaller,"",car_status->cumulative_reward);
EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, rlAgent->networks->target_net ,weight_in, file); EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, rlAgent->networks->target_net ,weight_in, file);
unlink(target_symlink); //unlink(target_symlink);
if(symlink(file, target_symlink)==-1){ //if(symlink(file, target_symlink)==-1)
fprintf(stderr,"debug: symlink %s with %s\n",target_symlink,file ); unlink(targetSymlinkCaller);
if(symlink(file, targetSymlinkCaller)==-1)
{
//fprintf(stderr,"debug: symlink %s with %s\n",target_symlink,file );
fprintf(stderr,"debug: symlink %s with %s\n",targetSymlinkCaller,file );
//fprintf(stderr,"debug: symlink %s with %s explain:%s\n",target_symlink,file,explain_symlink(file, target_symlink) ); //fprintf(stderr,"debug: symlink %s with %s explain:%s\n",target_symlink,file,explain_symlink(file, target_symlink) );
} }
else write(1,"-",1); else write(1,"-",1);
free(file); free(file);
free(mainfuncCaller);
free(targetfuncCaller);
free(mainSymlinkCaller);
free(targetSymlinkCaller);
} }
break; break;
} }
@@ -551,6 +586,66 @@ void* learn_to_drive(void * lrnarg){
// Sleep(pprint->delay->delay_between_episodes); // Sleep(pprint->delay->delay_between_episodes);
//} //}
} }
// UPDATE IF ENDING AND BETTER REWARD
if(car_status->cumulative_reward > qlStatus->progress_best_cumul->end_list->value)
{
int len_cumul=0;
char cumulSTR[128];
len_cumul=sprintf(cumulSTR, " %ld ", car_status->cumulative_reward);
char *funcCaller_extension=malloc(128);
char *mainSymlinkCaller=malloc(256);
char *targetSymlinkCaller=malloc(256);
if(qlParams->caller_func_name){
sprintf(funcCaller_extension,"%s.txt",qlParams->caller_func_name);
sprintf(mainSymlinkCaller,".ff_main_%s.symlink",qlParams->caller_func_name);
sprintf(targetSymlinkCaller,".ff_target_%s.symlink",qlParams->caller_func_name);
}else{
sprintf(funcCaller_extension,".%s","txt");
strcpy(mainSymlinkCaller,".ff_main_.symlink");
strcpy(targetSymlinkCaller,".ff_target_.symlink");
}
push_back_list_TYPE_L_INT(qlStatus->progress_best_cumul, car_status->cumulative_reward);
//char *file = fileNameDateScore(".ff_learnDir/.ff_main_",".txt",car_status->cumulative_reward);
char *file = fileNameDateScore(".ff_learnDir/.ff_main_",funcCaller_extension,car_status->cumulative_reward);
EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, rlAgent->networks->main_net ,weight_in, file);
//unlink(main_symlink);
unlink(mainSymlinkCaller);
//if(symlink(file, main_symlink)==-1)
if(symlink(file, mainSymlinkCaller)==-1)
{
//fprintf(stderr,"debug: symlink %s with %s.\n",main_symlink, file);
fprintf(stderr,"debug: symlink %s with %s.\n",mainSymlinkCaller, file);
//fprintf(stderr,"debug: symlink %s with %s. explain:%s \n",main_symlink, file, explain_symlink(file, main_symlink) );
}
else write(1,":",1);
write(1,cumulSTR,len_cumul);
free(file);
//file = fileNameDateScore(".ff_learnDir/.ff_target_",".txt",car_status->cumulative_reward);
file = fileNameDateScore(".ff_learnDir/.ff_target_",funcCaller_extension,car_status->cumulative_reward);
EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, rlAgent->networks->target_net ,weight_in, file);
//unlink(target_symlink);
//if(symlink(file, target_symlink)==-1)
unlink(targetSymlinkCaller);
if(symlink(file, targetSymlinkCaller)==-1)
{
//fprintf(stderr,"debug: symlink %s with %s\n",target_symlink,file );
fprintf(stderr,"debug: symlink %s with %s\n",targetSymlinkCaller,file );
//fprintf(stderr,"debug: symlink %s with %s explain:%s\n",target_symlink,file,explain_symlink(file, target_symlink) );
}
else write(1,"-",1);
free(file);
free(funcCaller_extension);
free(mainSymlinkCaller);
free(targetSymlinkCaller);
}
// END UPDATE
pthread_mutex_lock(qlStatus->mut_ending); pthread_mutex_lock(qlStatus->mut_ending);
qlStatus->ending = 1; qlStatus->ending = 1;
pthread_mutex_unlock(qlStatus->mut_ending); pthread_mutex_unlock(qlStatus->mut_ending);
deepQlearn_0/src/deepQlearning/learn_to_drive.h
+5
View File
@@ -22,6 +22,10 @@
#include "vehicle.h" #include "vehicle.h"
#ifndef CHECK_NAN
#define CHECK_NAN 0
#endif
//float reLU(float x); //float reLU(float x);
//float d_reLU(float x); //float d_reLU(float x);
@@ -48,6 +52,7 @@ struct qlearning_params {
long int nb_training_before_update_weight_in_target; long int nb_training_before_update_weight_in_target;
size_t number_episodes; size_t number_episodes;
// size_t threshold_number_same_action; // size_t threshold_number_same_action;
char *caller_func_name;
}; };
y_network_neural_network_/test/is_good.c
+13 -4
View File
@@ -778,6 +778,8 @@ TEST(_first_learn_vehicle_50__11){
size_t dim= 2; size_t dim= 2;
struct blocks * path = create_blocks(nb_block, dim); struct blocks * path = create_blocks(nb_block, dim);
LOG("debug: f_name = %s\n", __func__);
#if 1 #if 1
copy_coordinate(path->lower_bound_block[0], (float[]){0,0}); copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
@@ -922,6 +924,10 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
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, d_f_act , df );
UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, f_act , f ); 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( struct print_params *pprint = create_print_params(
12/*float scale_x*/,12 /*float scale_y*/, 12/*float scale_x*/,12 /*float scale_y*/,
dly/*struct delay_params * dly_p*/ dly/*struct delay_params * dly_p*/
@@ -1088,7 +1094,7 @@ copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
int randomRange = 500; int randomRange = 500;
size_t nb_prod_thread = 2; size_t nb_prod_thread = 2;
size_t nb_calc_thread = 4; size_t nb_calc_thread = 4;
float learning_rate = 0.0000001 /* 0.001*/; float learning_rate = 0.00001 /* 0.001*/;
struct networks_qlearning *nnetworks = create_network_qlearning( struct networks_qlearning *nnetworks = create_network_qlearning(
pconf, pconf,
randomize, minR, maxR, randomRange, randomize, minR, maxR, randomRange,
@@ -1235,7 +1241,7 @@ HIDE_TEST(__first_learn_vehicle13){
int randomRange = 5000; int randomRange = 5000;
size_t nb_prod_thread = 2; size_t nb_prod_thread = 2;
size_t nb_calc_thread = 4; size_t nb_calc_thread = 4;
float learning_rate = 0.1; float learning_rate = 0.00001;
struct networks_qlearning *nnetworks = create_network_qlearning( struct networks_qlearning *nnetworks = create_network_qlearning(
pconf, pconf,
randomize, minR, maxR, randomRange, randomize, minR, maxR, randomRange,
@@ -1243,6 +1249,9 @@ HIDE_TEST(__first_learn_vehicle13){
learning_rate learning_rate
); );
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");
struct status_qlearning *qlstatus = create_status_qlearning (); struct status_qlearning *qlstatus = create_status_qlearning ();
struct delay_params *dly = create_delay_params ( struct delay_params *dly = create_delay_params (
500/*size_t delay_between_episodes*/, 500/*size_t delay_between_episodes*/,
@@ -1253,7 +1262,7 @@ HIDE_TEST(__first_learn_vehicle13){
0.95/*float gamma*/, 0.95/*float gamma*/,
learning_rate, learning_rate,
0 /* (not used!)float discount_factor*/, 0 /* (not used!)float discount_factor*/,
0.85 /*float exploration_factor*/, 0.085 /*float exploration_factor*/,
20/*long int nb_training_before_update_weight_in_target*/, 20/*long int nb_training_before_update_weight_in_target*/,
10000/*size_t number_episodes*/ 10000/*size_t number_episodes*/
); );
@@ -1284,7 +1293,7 @@ HIDE_TEST(__first_learn_vehicle13){
struct arg_run_qlearn_bprint *argQL_BP = create_arg_run_qlearn_bprint(bash_arg, rlAgent); 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 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("16001", 2, 3, var);
pthread_t pollTh; pthread_t pollTh;