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add some test deep reinforcement learning

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fanasina
2024-07-18 02:10:30 +02:00
parent 491d0c283f
commit e64cef1688
6 changed files with 484 additions and 34 deletions
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deepQlearn_0/src/deepQlearning/learn_to_drive.c
+17
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@@ -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;
}