bug-fix for dumping movies (+ small refactoring and rename 'VideoDumpMethod -> 'VideoDumpFilter')

rl_coach/base_parameters.py

@@ -22,8 +22,8 @@ from collections import OrderedDict
 from enum import Enum
 from typing import Dict, List, Union
 
-from rl_coach.core_types import TrainingSteps, EnvironmentSteps, GradientClippingMethod, RunPhase
+from rl_coach.core_types import TrainingSteps, EnvironmentSteps, GradientClippingMethod, RunPhase, \
-# from rl_coach.environments.environment import SelectedPhaseOnlyDumpMethod, MaxDumpMethod
+    SelectedPhaseOnlyDumpFilter, MaxDumpFilter
 from rl_coach.filters.filter import NoInputFilter
 
 
@@ -285,7 +285,6 @@ class NetworkComponentParameters(Parameters):
         self.dense_layer = dense_layer
 
 
-
 class VisualizationParameters(Parameters):
     def __init__(self,
                  print_networks_summary=False,
@@ -293,7 +292,7 @@ class VisualizationParameters(Parameters):
                  dump_signals_to_csv_every_x_episodes=5,
                  dump_gifs=False,
                  dump_mp4=False,
-                 video_dump_methods=[],
+                 video_dump_methods=None,
                  dump_in_episode_signals=False,
                  dump_parameters_documentation=True,
                  render=False,
@@ -352,6 +351,8 @@ class VisualizationParameters(Parameters):
             which will be passed to the agent and allow using those images.
         """
         super().__init__()
+        if video_dump_methods is None:
+            video_dump_methods = [SelectedPhaseOnlyDumpFilter(RunPhase.TEST), MaxDumpFilter()]
         self.print_networks_summary = print_networks_summary
         self.dump_csv = dump_csv
         self.dump_gifs = dump_gifs
@@ -363,7 +364,7 @@ class VisualizationParameters(Parameters):
         self.native_rendering = native_rendering
         self.max_fps_for_human_control = max_fps_for_human_control
         self.tensorboard = tensorboard
-        self.video_dump_methods = video_dump_methods
+        self.video_dump_filters = video_dump_methods
         self.add_rendered_image_to_env_response = add_rendered_image_to_env_response
 
 

rl_coach/core_types.py

@@ -22,6 +22,8 @@ from typing import List, Union, Dict, Any, Type
 
 import numpy as np
 
+from rl_coach.utils import force_list
+
 ActionType = Union[int, float, np.ndarray, List]
 GoalType = Union[None, np.ndarray]
 ObservationType = np.ndarray
@@ -692,3 +694,79 @@ class Episode(object):
 
     def __getitem__(self, sliced):
         return self.transitions[sliced]
+
+
+"""
+Video Dumping Methods
+"""
+
+
+class VideoDumpFilter(object):
+    """
+    Method used to decide when to dump videos
+    """
+    def should_dump(self, episode_terminated=False, **kwargs):
+        raise NotImplementedError("")
+
+
+class AlwaysDumpFilter(VideoDumpFilter):
+    """
+    Dump video for every episode
+    """
+    def __init__(self):
+        super().__init__()
+
+    def should_dump(self, episode_terminated=False, **kwargs):
+        return True
+
+
+class MaxDumpFilter(VideoDumpFilter):
+    """
+    Dump video every time a new max total reward has been achieved
+    """
+    def __init__(self):
+        super().__init__()
+        self.max_reward_achieved = -np.inf
+
+    def should_dump(self, episode_terminated=False, **kwargs):
+        # if the episode has not finished yet we want to be prepared for dumping a video
+        if not episode_terminated:
+            return True
+        if kwargs['total_reward_in_current_episode'] > self.max_reward_achieved:
+            self.max_reward_achieved = kwargs['total_reward_in_current_episode']
+            return True
+        else:
+            return False
+
+
+class EveryNEpisodesDumpFilter(object):
+    """
+    Dump videos once in every N episodes
+    """
+    def __init__(self, num_episodes_between_dumps: int):
+        super().__init__()
+        self.num_episodes_between_dumps = num_episodes_between_dumps
+        self.last_dumped_episode = 0
+        if num_episodes_between_dumps < 1:
+            raise ValueError("the number of episodes between dumps should be a positive number")
+
+    def should_dump(self, episode_terminated=False, **kwargs):
+        if kwargs['episode_idx'] >= self.last_dumped_episode + self.num_episodes_between_dumps - 1:
+            self.last_dumped_episode = kwargs['episode_idx']
+            return True
+        else:
+            return False
+
+
+class SelectedPhaseOnlyDumpFilter(object):
+    """
+    Dump videos when the phase of the environment matches a predefined phase
+    """
+    def __init__(self, run_phases: Union[RunPhase, List[RunPhase]]):
+        self.run_phases = force_list(run_phases)
+
+    def should_dump(self, episode_terminated=False, **kwargs):
+        if kwargs['_phase'] in self.run_phases:
+            return True
+        else:
+            return False

rl_coach/environments/environment.py

@@ -317,6 +317,13 @@ class Environment(EnvironmentInterface):
         else:
             self.renderer.render_image(self.get_rendered_image())
 
+    def handle_episode_ended(self) -> None:
+        """
+        End an episode
+        :return: None
+        """
+        self.dump_video_of_last_episode_if_needed()
+
     def reset_internal_state(self, force_environment_reset=False) -> EnvResponse:
         """
         Reset the environment and all the variable of the wrapper
@@ -324,7 +331,6 @@ class Environment(EnvironmentInterface):
         :return: A dictionary containing the observation, reward, done flag, action and measurements
         """
 
-        self.dump_video_of_last_episode_if_needed()
         self._restart_environment_episode(force_environment_reset)
         self.last_episode_time = time.time()
 
@@ -392,17 +398,16 @@ class Environment(EnvironmentInterface):
         self.goal = goal
 
     def should_dump_video_of_the_current_episode(self, episode_terminated=False):
-        if self.visualization_parameters.video_dump_methods:
+        if self.visualization_parameters.video_dump_filters:
-            for video_dump_method in force_list(self.visualization_parameters.video_dump_methods):
+            for video_dump_filter in force_list(self.visualization_parameters.video_dump_filters):
-                if not video_dump_method.should_dump(episode_terminated, **self.__dict__):
+                if not video_dump_filter.should_dump(episode_terminated, **self.__dict__):
                     return False
             return True
-        return False
+        return True
 
     def dump_video_of_last_episode_if_needed(self):
-        if self.visualization_parameters.video_dump_methods and self.last_episode_images != []:
+        if self.last_episode_images != [] and self.should_dump_video_of_the_current_episode(episode_terminated=True):
-            if self.should_dump_video_of_the_current_episode(episode_terminated=True):
+            self.dump_video_of_last_episode()
-                self.dump_video_of_last_episode()
 
     def dump_video_of_last_episode(self):
         frame_skipping = max(1, int(5 / self.frame_skip))
@@ -464,78 +469,3 @@ class Environment(EnvironmentInterface):
         """
         return np.transpose(self.state['observation'], [1, 2, 0])
 
-
-"""
-Video Dumping Methods
-"""
-
-
-class VideoDumpMethod(object):
-    """
-    Method used to decide when to dump videos
-    """
-    def should_dump(self, episode_terminated=False, **kwargs):
-        raise NotImplementedError("")
-
-
-class AlwaysDumpMethod(VideoDumpMethod):
-    """
-    Dump video for every episode
-    """
-    def __init__(self):
-        super().__init__()
-
-    def should_dump(self, episode_terminated=False, **kwargs):
-        return True
-
-
-class MaxDumpMethod(VideoDumpMethod):
-    """
-    Dump video every time a new max total reward has been achieved
-    """
-    def __init__(self):
-        super().__init__()
-        self.max_reward_achieved = -np.inf
-
-    def should_dump(self, episode_terminated=False, **kwargs):
-        # if the episode has not finished yet we want to be prepared for dumping a video
-        if not episode_terminated:
-            return True
-        if kwargs['total_reward_in_current_episode'] > self.max_reward_achieved:
-            self.max_reward_achieved = kwargs['total_reward_in_current_episode']
-            return True
-        else:
-            return False
-
-
-class EveryNEpisodesDumpMethod(object):
-    """
-    Dump videos once in every N episodes
-    """
-    def __init__(self, num_episodes_between_dumps: int):
-        super().__init__()
-        self.num_episodes_between_dumps = num_episodes_between_dumps
-        self.last_dumped_episode = 0
-        if num_episodes_between_dumps < 1:
-            raise ValueError("the number of episodes between dumps should be a positive number")
-
-    def should_dump(self, episode_terminated=False, **kwargs):
-        if kwargs['episode_idx'] >= self.last_dumped_episode + self.num_episodes_between_dumps - 1:
-            self.last_dumped_episode = kwargs['episode_idx']
-            return True
-        else:
-            return False
-
-
-class SelectedPhaseOnlyDumpMethod(object):
-    """
-    Dump videos when the phase of the environment matches a predefined phase
-    """
-    def __init__(self, run_phases: Union[RunPhase, List[RunPhase]]):
-        self.run_phases = force_list(run_phases)
-
-    def should_dump(self, episode_terminated=False, **kwargs):
-        if kwargs['_phase'] in self.run_phases:
-            return True
-        else:
-            return False

rl_coach/graph_managers/graph_manager.py

@@ -325,8 +325,7 @@ class GraphManager(object):
         """
         self.total_steps_counters[self.phase][EnvironmentEpisodes] += 1
 
-        # TODO: we should disentangle ending the episode from resetting the internal state
+        [environment.handle_episode_ended() for environment in self.environments]
-        # self.reset_internal_state()
 
     def train(self, steps: TrainingSteps) -> None:
         """