Log lesson in TensorBoard

python/PPO.ipynb

   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "env = UnityEnvironment(file_name=env_name, curriculum=curriculum_file)\n",
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
     "            trainer.update_model(batch_size, num_epoch)\n",
     "        if steps % summary_freq == 0 and steps != 0 and train_model:\n",
     "            # Write training statistics to tensorboard.\n",
-    "            trainer.write_summary(summary_writer, steps)\n",
+    "            trainer.write_summary(summary_writer, steps, env._curriculum.lesson_number)\n",
     "        if steps % save_freq == 0 and steps != 0 and train_model:\n",
     "            # Save Tensorflow model\n",
     "            save_model(sess, model_path=model_path, steps=steps, saver=saver)\n",

python/ppo.py

             trainer.update_model(batch_size, num_epoch)
         if steps % summary_freq == 0 and steps != 0 and train_model:
             # Write training statistics to tensorboard.
-            trainer.write_summary(summary_writer, steps)
+            trainer.write_summary(summary_writer, steps, env._curriculum.lesson_number)
         if steps % save_freq == 0 and steps != 0 and train_model:
             # Save Tensorflow model
             save_model(sess, model_path=model_path, steps=steps, saver=saver)

python/ppo/trainer.py

         for key in self.history_dict:
             self.history_dict[key] = empty_local_history(self.history_dict[key])
 
-    def write_summary(self, summary_writer, steps):
+    def write_summary(self, summary_writer, steps, lesson_number):
         """
         Saves training statistics to Tensorboard.
         :param summary_writer: writer associated with Tensorflow session.
                 stat_mean = float(np.mean(self.stats[key]))
                 summary.value.add(tag='Info/{}'.format(key), simple_value=stat_mean)
                 self.stats[key] = []
+        summary.value.add(tag='Info/Lesson', simple_value=lesson_number)
         summary_writer.add_summary(summary, steps)
         summary_writer.flush()