Adding target reward and target sucess (#58)

* Adding target reward

* Adding target successs

* Addressing comments

* Using custom_reward_threshold and target_success_rate

* Adding exit message

* Moving success rate to environment

* Making target_success_rate optional

rl_coach/graph_managers/graph_manager.py

@@ -34,6 +34,7 @@ from rl_coach.logger import screen, Logger
 from rl_coach.utils import set_cpu, start_shell_command_and_wait
 from rl_coach.data_stores.data_store_impl import get_data_store
 from rl_coach.orchestrators.kubernetes_orchestrator import RunType
+from rl_coach.data_stores.data_store import SyncFiles
 
 
 class ScheduleParameters(Parameters):
@@ -458,12 +459,12 @@ class GraphManager(object):
         """
         [manager.sync() for manager in self.level_managers]
 
-    def evaluate(self, steps: PlayingStepsType, keep_networks_in_sync: bool=False) -> None:
+    def evaluate(self, steps: PlayingStepsType, keep_networks_in_sync: bool=False) -> bool:
         """
         Perform evaluation for several steps
         :param steps: the number of steps as a tuple of steps time and steps count
         :param keep_networks_in_sync: sync the network parameters with the global network before each episode
-        :return: None
+        :return: bool, True if the target reward and target success has been reached
         """
         self.verify_graph_was_created()
 
@@ -478,6 +479,16 @@ class GraphManager(object):
                 while self.current_step_counter < count_end:
                     self.act(EnvironmentEpisodes(1))
                     self.sync()
+        if self.should_stop():
+            if self.task_parameters.checkpoint_save_dir:
+                open(os.path.join(self.task_parameters.checkpoint_save_dir, SyncFiles.FINISHED.value), 'w').close()
+            if hasattr(self, 'data_store_params'):
+                data_store = get_data_store(self.data_store_params)
+                data_store.save_to_store()
+
+            screen.success("Reached required success rate. Exiting.")
+            return True
+        return False
 
     def improve(self):
         """
@@ -508,7 +519,8 @@ class GraphManager(object):
         count_end = self.total_steps_counters[RunPhase.TRAIN] + self.improve_steps
         while self.total_steps_counters[RunPhase.TRAIN] < count_end:
             self.train_and_act(self.steps_between_evaluation_periods)
-            self.evaluate(self.evaluation_steps)
+            if self.evaluate(self.evaluation_steps):
+                break
 
     def _restore_checkpoint_tf(self, checkpoint_dir: str):
         import tensorflow as tf
@@ -609,3 +621,6 @@ class GraphManager(object):
 
     def should_train(self) -> bool:
         return any([manager.should_train() for manager in self.level_managers])
+
+    def should_stop(self) -> bool:
+        return all([manager.should_stop() for manager in self.level_managers])