Fixed TensorBoard lesson logging.

python/unitytrainers/curriculum.py

                 parameters = self.data['parameters']
                 for key in parameters:
                     config[key] = parameters[key][self.lesson_num]
-                logger.info('\n{0} lesson changed. Now in Lesson {1} : \t{2}'
+                logger.info('{0} lesson changed. Now in lesson {1}: {2}'
                             .format(self._brain_name,
                                     self.lesson_num,
                                     ', '.join([str(x) + ' -> ' + str(config[x]) for x in config])))

python/unitytrainers/trainer.py

         """
         raise UnityTrainerException("The update_model method was not implemented.")
 
-    def write_summary(self, lesson_nums):
+    def write_summary(self, lesson_num):
-        :param lesson_nums: The lesson the trainer is at.
+        :param lesson_num: The lesson the trainer is at.
         """
         if (self.get_step % self.trainer_parameters['summary_freq'] == 0 and self.get_step != 0 and
                 self.is_training and self.get_step <= self.get_max_steps):
                     stat_mean = float(np.mean(self.stats[key]))
                     summary.value.add(tag='Info/{}'.format(key), simple_value=stat_mean)
                     self.stats[key] = []
-            self.write_tensorboard_text('LessonNumbers', lesson_nums)
+            summary.value.add(tag='Info/Lesson', simple_value=lesson_num)
             self.summary_writer.add_summary(summary, self.get_step)
             self.summary_writer.flush()
 

python/unitytrainers/trainer_controller.py

                             # Perform gradient descent with experience buffer
                             trainer.update_model()
                         # Write training statistics to Tensorboard.
-                        trainer.write_summary(self.meta_curriculum.lesson_nums)
+                        trainer.write_summary(self.meta_curriculum.brains_to_curriculums[brain_name].lesson_num)
                         if self.train_model and trainer.get_step <= trainer.get_max_steps:
                             trainer.increment_step_and_update_last_reward()
                     if self.train_model: