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163 lines
7.6 KiB
Python
163 lines
7.6 KiB
Python
import os
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import numpy as np
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import tensorflow as tf
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# make sure you have $CARLA_ROOT/PythonClient in your PYTHONPATH
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from carla.driving_benchmark.experiment_suites import CoRL2017
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from rl_coach.logger import screen
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from rl_coach.agents.cil_agent import CILAgentParameters
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from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
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from rl_coach.architectures.tensorflow_components.heads.cil_head import RegressionHeadParameters
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from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense, BatchnormActivationDropout
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from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
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from rl_coach.base_parameters import VisualizationParameters
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from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
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from rl_coach.environments.carla_environment import CarlaEnvironmentParameters
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from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
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from rl_coach.filters.filter import InputFilter
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from rl_coach.filters.observation.observation_crop_filter import ObservationCropFilter
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from rl_coach.filters.observation.observation_reduction_by_sub_parts_name_filter import \
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ObservationReductionBySubPartsNameFilter
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from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
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from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
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from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
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from rl_coach.graph_managers.graph_manager import ScheduleParameters
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from rl_coach.schedules import ConstantSchedule
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from rl_coach.spaces import ImageObservationSpace
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from rl_coach.utilities.carla_dataset_to_replay_buffer import create_dataset
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####################
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# Graph Scheduling #
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####################
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schedule_params = ScheduleParameters()
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schedule_params.improve_steps = TrainingSteps(10000000000)
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schedule_params.steps_between_evaluation_periods = TrainingSteps(500)
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schedule_params.evaluation_steps = EnvironmentEpisodes(5)
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schedule_params.heatup_steps = EnvironmentSteps(0)
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################
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# Agent Params #
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################
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agent_params = CILAgentParameters()
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# forward camera and measurements input
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agent_params.network_wrappers['main'].input_embedders_parameters = {
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'CameraRGB': InputEmbedderParameters(
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scheme=[
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Conv2d(32, 5, 2),
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BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
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Conv2d(32, 3, 1),
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BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
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Conv2d(64, 3, 2),
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BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
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Conv2d(64, 3, 1),
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BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
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Conv2d(128, 3, 2),
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BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
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Conv2d(128, 3, 1),
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BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
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Conv2d(256, 3, 1),
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BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
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Conv2d(256, 3, 1),
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BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
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Dense(512),
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BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.3),
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Dense(512),
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BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.3)
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],
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activation_function='none' # we define the activation function for each layer explicitly
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),
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'measurements': InputEmbedderParameters(
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scheme=[
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Dense(128),
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BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5),
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Dense(128),
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BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5)
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],
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activation_function='none' # we define the activation function for each layer explicitly
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)
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}
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# simple fc middleware
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agent_params.network_wrappers['main'].middleware_parameters = \
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FCMiddlewareParameters(
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scheme=[
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Dense(512),
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BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5)
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],
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activation_function='none'
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)
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# output branches
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agent_params.network_wrappers['main'].heads_parameters = [
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RegressionHeadParameters(
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scheme=[
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Dense(256),
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BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5),
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Dense(256),
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BatchnormActivationDropout(activation_function=tf.tanh)
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],
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num_output_head_copies=4 # follow lane, left, right, straight
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)
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]
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# TODO: there should be another head predicting the speed which is connected directly to the forward camera embedding
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agent_params.network_wrappers['main'].batch_size = 120
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agent_params.network_wrappers['main'].learning_rate = 0.0002
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# crop and rescale the image + use only the forward speed measurement
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agent_params.input_filter = InputFilter()
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agent_params.input_filter.add_observation_filter('CameraRGB', 'cropping',
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ObservationCropFilter(crop_low=np.array([115, 0, 0]),
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crop_high=np.array([510, -1, -1])))
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agent_params.input_filter.add_observation_filter('CameraRGB', 'rescale',
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ObservationRescaleToSizeFilter(
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ImageObservationSpace(np.array([88, 200, 3]), high=255)))
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agent_params.input_filter.add_observation_filter('CameraRGB', 'to_uint8', ObservationToUInt8Filter(0, 255))
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agent_params.input_filter.add_observation_filter(
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'measurements', 'select_speed',
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ObservationReductionBySubPartsNameFilter(
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["forward_speed"], reduction_method=ObservationReductionBySubPartsNameFilter.ReductionMethod.Keep))
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# no exploration is used
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agent_params.exploration = AdditiveNoiseParameters()
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agent_params.exploration.noise_percentage_schedule = ConstantSchedule(0)
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agent_params.exploration.evaluation_noise_percentage = 0
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# no playing during the training phase
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agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(0)
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# use the following command line to download and extract the CARLA dataset:
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# python rl_coach/utilities/carla_dataset_to_replay_buffer.py
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agent_params.memory.load_memory_from_file_path = "./datasets/carla_train_set_replay_buffer.p"
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agent_params.memory.state_key_with_the_class_index = 'high_level_command'
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agent_params.memory.num_classes = 4
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# download dataset if it doesn't exist
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if not os.path.exists(agent_params.memory.load_memory_from_file_path):
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screen.log_title("The CARLA dataset is not present in the following path: {}"
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.format(agent_params.memory.load_memory_from_file_path))
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result = screen.ask_yes_no("Do you want to download it now?")
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if result:
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create_dataset(None, "./datasets/carla_train_set_replay_buffer.p")
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else:
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screen.error("Please update the path to the CARLA dataset in the CARLA_CIL preset", crash=True)
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###############
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# Environment #
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###############
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env_params = CarlaEnvironmentParameters()
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env_params.cameras = ['CameraRGB']
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env_params.camera_height = 600
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env_params.camera_width = 800
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env_params.separate_actions_for_throttle_and_brake = True
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env_params.allow_braking = True
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env_params.quality = CarlaEnvironmentParameters.Quality.EPIC
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env_params.experiment_suite = CoRL2017('Town01')
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graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
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schedule_params=schedule_params, vis_params=VisualizationParameters())
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