Making stop condition optional by using a flag (#113)

* apply stop condition flag (default: ignore the stop condition)

rl_coach/base_parameters.py

@@ -512,7 +512,7 @@ class AgentParameters(Parameters):
 class TaskParameters(Parameters):
     def __init__(self, framework_type: Frameworks=Frameworks.tensorflow, evaluate_only: bool=False, use_cpu: bool=False,
                  experiment_path='/tmp', seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None,
-                 checkpoint_save_dir=None, export_onnx_graph: bool=False):
+                 checkpoint_save_dir=None, export_onnx_graph: bool=False, apply_stop_condition: bool=False):
         """
         :param framework_type: deep learning framework type. currently only tensorflow is supported
         :param evaluate_only: the task will be used only for evaluating the model
@@ -523,6 +523,8 @@ class TaskParameters(Parameters):
         :param checkpoint_restore_dir: the directory to restore the checkpoints from
         :param checkpoint_save_dir: the directory to store the checkpoints in
         :param export_onnx_graph: If set to True, this will export an onnx graph each time a checkpoint is saved
+        :param apply_stop_condition: If set to True, this will apply the stop condition defined by reaching a target success rate
+
         """
         self.framework_type = framework_type
         self.task_index = 0  # TODO: not really needed
@@ -534,6 +536,7 @@ class TaskParameters(Parameters):
         self.checkpoint_save_dir = checkpoint_save_dir
         self.seed = seed
         self.export_onnx_graph = export_onnx_graph
+        self.apply_stop_condition = apply_stop_condition
 
 
 class DistributedTaskParameters(TaskParameters):
@@ -541,7 +544,7 @@ class DistributedTaskParameters(TaskParameters):
                  task_index: int, evaluate_only: bool=False, num_tasks: int=None,
                  num_training_tasks: int=None, use_cpu: bool=False, experiment_path=None, dnd=None,
                  shared_memory_scratchpad=None, seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None,
-                 checkpoint_save_dir=None, export_onnx_graph: bool=False):
+                 checkpoint_save_dir=None, export_onnx_graph: bool=False, apply_stop_condition: bool=False):
         """
         :param framework_type: deep learning framework type. currently only tensorflow is supported
         :param evaluate_only: the task will be used only for evaluating the model
@@ -560,11 +563,13 @@ class DistributedTaskParameters(TaskParameters):
         :param checkpoint_restore_dir: the directory to restore the checkpoints from
         :param checkpoint_save_dir: the directory to store the checkpoints in
         :param export_onnx_graph: If set to True, this will export an onnx graph each time a checkpoint is saved
+        :param apply_stop_condition: If set to True, this will apply the stop condition defined by reaching a target success rate
+
         """
         super().__init__(framework_type=framework_type, evaluate_only=evaluate_only, use_cpu=use_cpu,
                          experiment_path=experiment_path, seed=seed, checkpoint_save_secs=checkpoint_save_secs,
                          checkpoint_restore_dir=checkpoint_restore_dir, checkpoint_save_dir=checkpoint_save_dir,
-                         export_onnx_graph=export_onnx_graph)
+                         export_onnx_graph=export_onnx_graph, apply_stop_condition=apply_stop_condition)
         self.parameters_server_hosts = parameters_server_hosts
         self.worker_hosts = worker_hosts
         self.job_type = job_type

rl_coach/coach.py

@@ -541,6 +541,11 @@ class CoachLauncher(object):
                             type=RunType,
                             default=RunType.ORCHESTRATOR,
                             choices=list(RunType))
+        parser.add_argument('-asc', '--apply_stop_condition',
+                            help="(flag) If set, this will apply a stop condition on the run, defined by reaching a" 
+                                 "target success rate as set by the environment or a custom success rate as defined "
+                                 "in the preset. ",
+                            action='store_true')
 
         return parser
 
@@ -590,7 +595,8 @@ class CoachLauncher(object):
             checkpoint_save_secs=args.checkpoint_save_secs,
             checkpoint_restore_dir=args.checkpoint_restore_dir,
             checkpoint_save_dir=args.checkpoint_save_dir,
-            export_onnx_graph=args.export_onnx_graph
+            export_onnx_graph=args.export_onnx_graph,
+            apply_stop_condition=args.apply_stop_condition
         )
 
         start_graph(graph_manager=graph_manager, task_parameters=task_parameters)
@@ -629,7 +635,8 @@ class CoachLauncher(object):
                 checkpoint_save_secs=args.checkpoint_save_secs,
                 checkpoint_restore_dir=args.checkpoint_restore_dir,
                 checkpoint_save_dir=args.checkpoint_save_dir,
-                export_onnx_graph=args.export_onnx_graph
+                export_onnx_graph=args.export_onnx_graph,
+                apply_stop_condition=args.apply_stop_condition
             )
             # we assume that only the evaluation workers are rendering
             graph_manager.visualization_parameters.render = args.render and evaluation_worker

rl_coach/graph_managers/graph_manager.py

@@ -669,4 +669,4 @@ class GraphManager(object):
             self.memory_backend = get_memory_backend(self.agent_params.memory.memory_backend_params)
 
     def should_stop(self) -> bool:
-        return all([manager.should_stop() for manager in self.level_managers])
+        return self.task_parameters.apply_stop_condition and all([manager.should_stop() for manager in self.level_managers])