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learnig: add ending and fix leak memory

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fanasina
2025-05-12 15:16:19 +02:00
parent d2a84f4a82
commit 52e6353469
8 changed files with 229 additions and 28 deletions
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deepQlearn_0/test/is_good.c
+194 -16
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@@ -511,7 +511,7 @@ scanf("%c",&c);
}
#endif
// **************************************************************
#if 1
TEST(first_learn_vehicle_50__9){
@@ -633,20 +633,20 @@ EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weigh
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");
//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*/
50000 /*size_t delay_between_episodes*/,
500/*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*/,
0.01/*0.99*/ /*float exploration_factor*/,
20/*long int nb_training_before_update_weight_in_target*/,
10000/*size_t number_episodes*/
);
@@ -681,12 +681,184 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
// ****************************************************************
#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[]){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});
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[]){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[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*/,
1 /*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 = 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});
@@ -791,7 +963,7 @@ 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 = 0.00001 /* 0.001*/;
float learning_rate = 0; /* 0.000001*/ /* 0.001*/;
struct networks_qlearning *nnetworks = create_nework_qlearning(
pconf,
randomize, minR, maxR, randomRange,
@@ -803,9 +975,12 @@ EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weigh
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");
EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20250508_17h50m56s_26300.txt");
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_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*/,
@@ -852,7 +1027,7 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
#if 1
TEST(first_learn_vehicle_50__11){
TEST(first_learn_vehicle_50__12){
size_t nb_block = 10;
size_t dim= 2;
struct blocks * path = create_blocks(nb_block, dim);
@@ -967,7 +1142,7 @@ 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 = 0.00001 /* 0.001*/;
float learning_rate = 0.0000001 /* 0.001*/;
struct networks_qlearning *nnetworks = create_nework_qlearning(
pconf,
randomize, minR, maxR, randomRange,
@@ -976,9 +1151,12 @@ copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
);
//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");
*/
EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20250508_17h50m56s_26300.txt");
EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20250508_17h50m56s_26300.txt");
struct status_qlearning *qlstatus = create_status_qlearning ();
struct delay_params *dly = create_delay_params (
@@ -990,9 +1168,9 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
0.95/*float gamma*/,
learning_rate,
0 /* (not used!)float discount_factor*/,
0.0001/*0.99*/ /*float exploration_factor*/,
0.1/*0.99*/ /*float exploration_factor*/,
20/*long int nb_training_before_update_weight_in_target*/,
10000/*size_t number_episodes*/
1/*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 );
@@ -1029,7 +1207,7 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
#if 1
TEST(first_learn_vehicle){
TEST(first_learn_vehicle13){
size_t nb_block = 7;
size_t dim= 2;
struct blocks * path = create_blocks(nb_block, dim);