using the CoRL2017 experiment suite for CARLA_CIL

rl_coach/agents/cil_agent.py

@@ -29,6 +29,7 @@ class CILAlgorithmParameters(AlgorithmParameters):
     def __init__(self):
         super().__init__()
         self.collect_new_data = False
+        self.state_key_with_the_class_index = 'high_level_command'
 
 
 class CILNetworkParameters(NetworkParameters):
@@ -63,7 +64,7 @@ class CILAgent(ImitationAgent):
         self.current_high_level_control = 0
 
     def choose_action(self, curr_state):
-        self.current_high_level_control = curr_state['high_level_command']
+        self.current_high_level_control = curr_state[self.ap.algorithm.state_key_with_the_class_index]
         return super().choose_action(curr_state)
 
     def extract_action_values(self, prediction):
@@ -74,7 +75,7 @@ class CILAgent(ImitationAgent):
 
         target_values = self.networks['main'].online_network.predict({**batch.states(network_keys)})
 
-        branch_to_update = batch.states(['high_level_command'])['high_level_command']
+        branch_to_update = batch.states([self.ap.algorithm.state_key_with_the_class_index])[self.ap.algorithm.state_key_with_the_class_index]
         for idx, branch in enumerate(branch_to_update):
             target_values[branch][idx] = batch.actions()[idx]
 

rl_coach/environments/carla_environment.py

@@ -34,6 +34,7 @@ try:
     from carla.sensor import Camera
     from carla.client import VehicleControl
     from carla.planner.planner import Planner
+    from carla.driving_benchmark.experiment_suites.experiment_suite import ExperimentSuite
 except ImportError:
     from rl_coach.logger import failed_imports
     failed_imports.append("CARLA")
@@ -103,7 +104,8 @@ class CarlaEnvironmentParameters(EnvironmentParameters):
         self.server_width = 720
         self.camera_height = 128
         self.camera_width = 180
-        self.config = None #'environments/CarlaSettings.ini'  # TODO: remove the config to prevent confusion
+        self.experiment_suite = None  # an optional CARLA experiment suite to use
+        self.config = None
         self.level = 'town1'
         self.quality = self.Quality.LOW
         self.cameras = [CameraTypes.FRONT]
@@ -126,7 +128,7 @@ class CarlaEnvironment(Environment):
                  seed: int, frame_skip: int, human_control: bool, custom_reward_threshold: Union[int, float],
                  visualization_parameters: VisualizationParameters,
                  server_height: int, server_width: int, camera_height: int, camera_width: int,
-                 verbose: bool, config: str, episode_max_time: int,
+                 verbose: bool, experiment_suite: ExperimentSuite, config: str, episode_max_time: int,
                  allow_braking: bool, quality: CarlaEnvironmentParameters.Quality,
                  cameras: List[CameraTypes], weather_id: List[int], experiment_path: str,
                  num_speedup_steps: int, max_speed: float, **kwargs):
@@ -161,6 +163,7 @@ class CarlaEnvironment(Environment):
                 high=255)
 
         # setup server settings
+        self.experiment_suite = experiment_suite
         self.config = config
         if self.config:
             # load settings from file
@@ -191,12 +194,17 @@ class CarlaEnvironment(Environment):
         # open the client
         self.game = CarlaClient(self.host, self.port, timeout=99999999)
         self.game.connect()
-        scene = self.game.load_settings(self.settings)
+        if self.experiment_suite:
+            self.current_experiment = self.experiment_suite.get_experiments()[0]
+            scene = self.game.load_settings(self.current_experiment.conditions)
+        else:
+            scene = self.game.load_settings(self.settings)
 
         # get available start positions
         self.positions = scene.player_start_spots
-        self.num_pos = len(self.positions)
+        self.num_positions = len(self.positions)
-        self.iterator_start_positions = 0
+        self.current_start_position_idx = 0
+        self.current_pose = 0
 
         # action space
         self.action_space = BoxActionSpace(shape=2, low=np.array([-1, -1]), high=np.array([1, 1]))
@@ -391,18 +399,24 @@ class CarlaEnvironment(Environment):
         self.game.send_control(self.control)
 
     def _restart_environment_episode(self, force_environment_reset=False):
-        self.iterator_start_positions += 1
+        # select start and end positions
-        if self.iterator_start_positions >= self.num_pos:
+        if self.experiment_suite:
-            self.iterator_start_positions = 0
+            # if an expeirent suite is available, follow its given poses
+            self.current_start_position_idx = self.current_experiment.poses[self.current_pose][0]
+            self.current_goal = self.current_experiment.poses[self.current_pose][1]
+            self.current_pose += 1
+        else:
+            # go over all the possible positions in a cyclic manner
+            self.current_start_position_idx = (self.current_start_position_idx + 1) % self.num_positions
+
+            # choose a random goal destination TODO: follow the CoRL destinations and start positions
+            self.current_goal = random.choice(self.positions)
 
         try:
-            self.game.start_episode(self.iterator_start_positions)
+            self.game.start_episode(self.current_start_position_idx)
         except:
             self.game.connect()
-            self.game.start_episode(self.iterator_start_positions)
+            self.game.start_episode(self.current_start_position_idx)
-
-        # choose a random goal destination TODO: follow the CoRL destinations and start positions
-        self.current_goal = random.choice(self.positions)
 
         # start the game with some initial speed
         for i in range(self.num_speedup_steps):

rl_coach/presets/CARLA_CIL.py

@@ -1,10 +1,17 @@
-import os
-import sys
-
 import numpy as np
+
+# make sure you have $CARLA_ROOT/PythonClient in your PYTHONPATH
+from carla.driving_benchmark.experiment_suites import CoRL2017
+
+from rl_coach.agents.cil_agent import CILAgentParameters
 from rl_coach.architectures.tensorflow_components.architecture import Conv2d, Dense
+from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
+from rl_coach.architectures.tensorflow_components.heads.cil_head import RegressionHeadParameters
 from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.base_parameters import VisualizationParameters
+from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase
 from rl_coach.environments.carla_environment import CarlaEnvironmentParameters, CameraTypes
+from rl_coach.environments.environment import MaxDumpMethod, SelectedPhaseOnlyDumpMethod
 from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
 from rl_coach.filters.filter import InputFilter
 from rl_coach.filters.observation.observation_crop_filter import ObservationCropFilter
@@ -12,17 +19,10 @@ from rl_coach.filters.observation.observation_reduction_by_sub_parts_name_filter
     ObservationReductionBySubPartsNameFilter
 from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
 from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
-from rl_coach.schedules import ConstantSchedule
-from rl_coach.spaces import ImageObservationSpace
-
-from rl_coach.agents.cil_agent import CILAgentParameters
-from rl_coach.architectures.tensorflow_components.heads.cil_head import RegressionHeadParameters
 from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
 from rl_coach.graph_managers.graph_manager import ScheduleParameters
-from rl_coach.base_parameters import VisualizationParameters
+from rl_coach.schedules import ConstantSchedule
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
+from rl_coach.spaces import ImageObservationSpace
-from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase
-from rl_coach.environments.environment import MaxDumpMethod, SelectedPhaseOnlyDumpMethod
 
 ####################
 # Graph Scheduling #
@@ -116,8 +116,9 @@ env_params.level = 'town1'
 env_params.cameras = [CameraTypes.FRONT]
 env_params.camera_height = 600
 env_params.camera_width = 800
-env_params.allow_braking = True
+env_params.allow_braking = False
 env_params.quality = CarlaEnvironmentParameters.Quality.EPIC
+env_params.experiment_suite = CoRL2017('Town01')
 
 vis_params = VisualizationParameters()
 vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()]