first try qdeep learning

deepQlearn_0/src/deepQlearning/learn_to_drive.h

@@ -1,7 +1,17 @@
 #ifndef __LEARNING_VEHICLE__C_H____
 #define __LEARNING_VEHICLE__C_H____
 
-//#include <pthread.h>
+#include <stdlib.h>
+#include <pthread.h>
+ /* for Sleep : milliseconds */
+#ifdef WINDOWS
+#include <windows.h>
+//#ifdef LINUX
+#else
+#include <unistd.h>
+#define Sleep(x) usleep((x)*1000)
+#endif
+
 
 #include "neuron_t/neuron_t.h"
 
@@ -16,15 +26,17 @@
 
 
 struct qlearning_params {
-  double gamma;
-  double learning_rate;
-  double factor_update_learning_rate;
-  double minimum_threshold_learning_rate;
-  double discount_factor;
-  double exploration_factor;
-  double factor_update_exploration_factor;
-  double minimum_threshold_exploration_factor;
+  float gamma;
+  float learning_rate;
+  float factor_update_learning_rate;
+ // float epsilon;
+  float minimum_threshold_learning_rate;
+  float discount_factor;
+  float exploration_factor;
+  float factor_update_exploration_factor;
+  float minimum_threshold_exploration_factor;
   long int nb_training_before_update_weight_in_target;
+  size_t number_episodes;
 };
 
 
@@ -40,6 +52,15 @@ struct delay_params {
   size_t delay_between_games;
 };
 
+struct print_params {
+  bool printed;
+  pthread_mutex_t mut_printed;
+  float scale_x; 
+  float scale_y;
+  struct delay_params *delay;
+  char string_space[LOG_LENTH];
+};
+
 struct networks_qlearning {
   config_layers *config;
   neurons_TYPE_FLOAT *main_net;
@@ -51,39 +72,48 @@ struct RL_agent {
   struct networks_qlearning * networks;
   struct vehicle * car;
   struct status_qlearning * status;
-  struct delay_params * delay;
+  struct print_params * pprint;
   struct qlearning_params *qlearnParams;
 
 };
 
 struct networks_qlearning * create_nework_qlearning(
   struct config_layers * config,
-  bool randomize, float minR, float maxR,  int randomRange
+  bool randomize, float minR, float maxR,  int randomRange,
+  size_t nb_prod_thread,
+  size_t nb_calc_thread,
+  float learning_rate
 );
 struct status_qlearning * create_status_qlearning ();
 struct delay_params * create_delay_params (
   size_t delay_between_episodes,
   size_t delay_between_games
 );
+struct print_params * create_print_params(
+  float scale_x, float scale_y,  
+  struct delay_params * dly_p
+);
 
 struct qlearning_params * create_qlearning_params (
-  double gamma,
-  double learning_rate,
-  double discount_factor,
-  double exploration_factor,
-  long int nb_training_before_update_weight_in_target
+  float gamma,
+  float learning_rate,
+  float discount_factor,
+  float exploration_factor,
+  long int nb_training_before_update_weight_in_target,
+  size_t number_episodes
 );
 
 struct RL_agent * create_RL_agent (
   struct networks_qlearning * networks,
   struct vehicle * car,
   struct status_qlearning * status,
-  struct delay_params * delay,
+  struct print_params * pprint,
   struct qlearning_params *qlearnParams
 );
 
 void free_networks_qlearning (struct networks_qlearning * networks);
 void free_status_qlearning(struct status_qlearning *status_ql);
+void free_print_params (struct print_params *pprint);
 void free_delay_params (struct delay_params *dly_p);
 void free_qlearning_params(struct qlearning_params *q_params);
 void free_RL_agent(struct RL_agent *rlAgent);
@@ -92,9 +122,11 @@ void copy_weight_in_networks_from_main_to_target(struct networks_qlearning * net
 void copy_weight_in_networks_from_main_to_best(struct networks_qlearning * networks);
 
 void train_qlearning(struct RL_agent * rlAgent,
-  int action ,
-  tensor_TYPE_FLOAT * new_state /*input*/,
-  tensor_TYPE_FLOAT * state /*input*/,
-  long reward);
+  int action); 
+//  tensor_TYPE_FLOAT * new_state /*input*/,
+//  tensor_TYPE_FLOAT * state /*input*/,
+//  long reward;
+
+void learn_to_drive(struct RL_agent * rlAgent);
 
 #endif /* __LEARNING_VEHICLE__C_H____ */