applying filters for a csv loaded dataset + some bug-fixes in data lo…

…ading (#319)

rl_coach/agents/agent.py

@@ -95,19 +95,6 @@ def __init__(self, agent_parameters: AgentParameters, parent: Union['LevelManage
                 if self.ap.memory.memory_backend_params.run_type != 'trainer':
                     self.memory.set_memory_backend(self.memory_backend)
 
-            if agent_parameters.memory.load_memory_from_file_path:
-                if isinstance(agent_parameters.memory.load_memory_from_file_path, PickledReplayBuffer):
-                    screen.log_title("Loading a pickled replay buffer. Pickled file path: {}"
-                                     .format(agent_parameters.memory.load_memory_from_file_path.filepath))
-                    self.memory.load_pickled(agent_parameters.memory.load_memory_from_file_path.filepath)
-                elif isinstance(agent_parameters.memory.load_memory_from_file_path, CsvDataset):
-                    screen.log_title("Loading a replay buffer from a CSV file. CSV file path: {}"
-                                     .format(agent_parameters.memory.load_memory_from_file_path.filepath))
-                    self.memory.load_csv(agent_parameters.memory.load_memory_from_file_path)
-                else:
-                    raise ValueError('Trying to load a replay buffer using an unsupported method - {}. '
-                                     .format(agent_parameters.memory.load_memory_from_file_path))
-
             if self.shared_memory and self.is_chief:
                 self.shared_memory_scratchpad.add(self.memory_lookup_name, self.memory)
 
@@ -262,6 +249,38 @@ def set_session(self, sess) -> None:
         self.output_filter.set_session(sess)
         self.pre_network_filter.set_session(sess)
         [network.set_session(sess) for network in self.networks.values()]
+        self.initialize_session_dependent_components()
+
+    def initialize_session_dependent_components(self):
+        """
+        Initialize components which require a session as part of their initialization.
+
+        :return: None
+        """
+
+        # Loading a memory from a CSV file, requires an input filter to filter through the data.
+        # The filter needs a session before it can be used.
+        if self.ap.memory.load_memory_from_file_path:
+            self.load_memory_from_file()
+
+    def load_memory_from_file(self):
+        """
+        Load memory transitions from a file.
+
+        :return: None
+        """
+
+        if isinstance(self.ap.memory.load_memory_from_file_path, PickledReplayBuffer):
+            screen.log_title("Loading a pickled replay buffer. Pickled file path: {}"
+                             .format(self.ap.memory.load_memory_from_file_path.filepath))
+            self.memory.load_pickled(self.ap.memory.load_memory_from_file_path.filepath)
+        elif isinstance(self.ap.memory.load_memory_from_file_path, CsvDataset):
+            screen.log_title("Loading a replay buffer from a CSV file. CSV file path: {}"
+                             .format(self.ap.memory.load_memory_from_file_path.filepath))
+            self.memory.load_csv(self.ap.memory.load_memory_from_file_path, self.input_filter)
+        else:
+            raise ValueError('Trying to load a replay buffer using an unsupported method - {}. '
+                             .format(self.ap.memory.load_memory_from_file_path))
 
     def register_signal(self, signal_name: str, dump_one_value_per_episode: bool=True,
                         dump_one_value_per_step: bool=False) -> Signal:

rl_coach/filters/observation/observation_stacking_filter.py

@@ -21,7 +21,7 @@
 
 from rl_coach.core_types import ObservationType
 from rl_coach.filters.observation.observation_filter import ObservationFilter
-from rl_coach.spaces import ObservationSpace
+from rl_coach.spaces import ObservationSpace, VectorObservationSpace
 
 
 class LazyStack(object):
@@ -63,6 +63,7 @@ def __init__(self, stack_size: int, stacking_axis: int=-1):
         self.stack_size = stack_size
         self.stacking_axis = stacking_axis
         self.stack = []
+        self.input_observation_space = None
 
         if stack_size <= 0:
             raise ValueError("The stack shape must be a positive number")
@@ -86,22 +87,28 @@ def validate_input_observation_space(self, input_observation_space: ObservationS
             raise ValueError("The stacking axis is larger than the number of dimensions in the observation space")
 
     def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
-
         if len(self.stack) == 0:
             self.stack = deque([observation] * self.stack_size, maxlen=self.stack_size)
         else:
             if update_internal_state:
                 self.stack.append(observation)
         observation = LazyStack(self.stack, self.stacking_axis)
 
+        if isinstance(self.input_observation_space, VectorObservationSpace):
+            # when stacking vectors, we cannot avoid copying the memory as we're flattening it all
+            observation = np.array(observation).flatten()
+
         return observation
 
     def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
-        if self.stacking_axis == -1:
-            input_observation_space.shape = np.append(input_observation_space.shape, values=[self.stack_size], axis=0)
+        if isinstance(input_observation_space, VectorObservationSpace):
+            self.input_observation_space = input_observation_space = VectorObservationSpace(input_observation_space.shape * self.stack_size)
         else:
-            input_observation_space.shape = np.insert(input_observation_space.shape, obj=self.stacking_axis,
-                                                     values=[self.stack_size], axis=0)
+            if self.stacking_axis == -1:
+                input_observation_space.shape = np.append(input_observation_space.shape, values=[self.stack_size], axis=0)
+            else:
+                input_observation_space.shape = np.insert(input_observation_space.shape, obj=self.stacking_axis,
+                                                         values=[self.stack_size], axis=0)
         return input_observation_space
 
     def reset(self) -> None:

rl_coach/memories/episodic/episodic_experience_replay.py

@@ -25,6 +25,7 @@
 import random
 
 from rl_coach.core_types import Transition, Episode
+from rl_coach.filters.filter import InputFilter
 from rl_coach.logger import screen
 from rl_coach.memories.memory import Memory, MemoryGranularity, MemoryParameters
 from rl_coach.utils import ReaderWriterLock, ProgressBar
@@ -408,11 +409,12 @@ def mean_reward(self) -> np.ndarray:
         self.reader_writer_lock.release_writing()
         return mean
 
-    def load_csv(self, csv_dataset: CsvDataset) -> None:
+    def load_csv(self, csv_dataset: CsvDataset, input_filter: InputFilter) -> None:
         """
         Restore the replay buffer contents from a csv file.
         The csv file is assumed to include a list of transitions.
         :param csv_dataset: A construct which holds the dataset parameters
+        :param input_filter: A filter used to filter the CSV data before feeding it to the memory.
         """
         self.assert_not_frozen()
 
@@ -429,18 +431,30 @@ def load_csv(self, csv_dataset: CsvDataset) -> None:
         for e_id in episode_ids:
             progress_bar.update(e_id)
             df_episode_transitions = df[df['episode_id'] == e_id]
+            input_filter.reset()
+
+            if len(df_episode_transitions) < 2:
+                # we have to have at least 2 rows in each episode for creating a transition
+                continue
+
             episode = Episode()
+            transitions = []
             for (_, current_transition), (_, next_transition) in zip(df_episode_transitions[:-1].iterrows(),
                                                                      df_episode_transitions[1:].iterrows()):
                 state = np.array([current_transition[col] for col in state_columns])
                 next_state = np.array([next_transition[col] for col in state_columns])
 
-                episode.insert(
+                transitions.append(
                     Transition(state={'observation': state},
                                action=current_transition['action'], reward=current_transition['reward'],
                                next_state={'observation': next_state}, game_over=False,
                                info={'all_action_probabilities':
-                                         ast.literal_eval(current_transition['all_action_probabilities'])}))
+                                         ast.literal_eval(current_transition['all_action_probabilities'])}),
+                    )
+
+            transitions = input_filter.filter(transitions, deep_copy=False)
+            for t in transitions:
+                episode.insert(t)
 
             # Set the last transition to end the episode
             if csv_dataset.is_episodic: