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

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fanasina
2025-12-15 11:15:51 +01:00
parent 3d54639d4c
commit 0e80467c19
3 changed files with 124 additions and 15 deletions
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y_network_neural_network_/test/is_good.c
+14 -5
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@@ -778,6 +778,8 @@ TEST(_first_learn_vehicle_50__11){
size_t dim= 2;
struct blocks * path = create_blocks(nb_block, dim);
LOG("debug: f_name = %s\n", __func__);
#if 1
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, 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*/
@@ -1088,7 +1094,7 @@ 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 = 0.0000001 /* 0.001*/;
float learning_rate = 0.00001 /* 0.001*/;
struct networks_qlearning *nnetworks = create_network_qlearning(
pconf,
randomize, minR, maxR, randomRange,
@@ -1235,7 +1241,7 @@ HIDE_TEST(__first_learn_vehicle13){
int randomRange = 5000;
size_t nb_prod_thread = 2;
size_t nb_calc_thread = 4;
float learning_rate = 0.1;
float learning_rate = 0.00001;
struct networks_qlearning *nnetworks = create_network_qlearning(
pconf,
randomize, minR, maxR, randomRange,
@@ -1243,7 +1249,10 @@ HIDE_TEST(__first_learn_vehicle13){
learning_rate
);
struct status_qlearning *qlstatus = create_status_qlearning ();
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 delay_params *dly = create_delay_params (
500/*size_t delay_between_episodes*/,
50/*size_t delay_between_games*/
@@ -1253,7 +1262,7 @@ HIDE_TEST(__first_learn_vehicle13){
0.95/*float gamma*/,
learning_rate,
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*/,
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_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;