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initial CIL implementation (WIP)
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itaicaspi-intel
127
rl_coach/presets/CARLA_CIL.py
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127
rl_coach/presets/CARLA_CIL.py
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import os
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import sys
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import numpy as np
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from rl_coach.architectures.tensorflow_components.architecture import Conv2d, Dense
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from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
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from rl_coach.environments.carla_environment import CarlaEnvironmentParameters, CameraTypes
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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.schedules import ConstantSchedule
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from rl_coach.spaces import ImageObservationSpace
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from rl_coach.agents.cil_agent import CILAgentParameters
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from rl_coach.architectures.tensorflow_components.heads.cil_head import RegressionHeadParameters
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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.base_parameters import VisualizationParameters
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from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
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from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase
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from rl_coach.environments.environment import MaxDumpMethod, SelectedPhaseOnlyDumpMethod
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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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'forward_camera': InputEmbedderParameters(scheme=[Conv2d([32, 5, 2]),
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Conv2d([32, 3, 1]),
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Conv2d([64, 3, 2]),
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Conv2d([64, 3, 1]),
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Conv2d([128, 3, 2]),
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Conv2d([128, 3, 1]),
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Conv2d([256, 3, 1]),
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Conv2d([256, 3, 1]),
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Dense([512]),
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Dense([512])],
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dropout=True,
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batchnorm=True),
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'measurements': InputEmbedderParameters(scheme=[Dense([128]),
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Dense([128])])
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}
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# TODO: batch norm is currently applied to the fc layers which is not desired
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# TODO: dropout should be configured differenetly per layer [1.0] * 8 + [0.7] * 2 + [0.5] * 2 + [0.5] * 1 + [0.5, 1.] * 5
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# simple fc middleware
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agent_params.network_wrappers['main'].middleware_parameters = FCMiddlewareParameters(scheme=[Dense([512])])
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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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RegressionHeadParameters(),
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RegressionHeadParameters(),
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RegressionHeadParameters()
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]
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# agent_params.network_wrappers['main'].num_output_head_copies = 4 # follow lane, left, right, straight
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agent_params.network_wrappers['main'].rescale_gradient_from_head_by_factor = [1, 1, 1, 1]
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agent_params.network_wrappers['main'].loss_weights = [1, 1, 1, 1]
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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('forward_camera', '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('forward_camera', '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('forward_camera', '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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# the CARLA dataset should be downloaded through the following repository:
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# https://github.com/carla-simulator/imitation-learning
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# the dataset should then be converted to the Coach format using the script utils/carla_dataset_to_replay_buffer.py
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# the path to the converted dataset should be updated below
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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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###############
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# Environment #
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###############
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env_params = CarlaEnvironmentParameters()
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env_params.level = 'town1'
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env_params.cameras = [CameraTypes.FRONT]
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env_params.camera_height = 600
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env_params.camera_width = 800
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env_params.allow_braking = True
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env_params.quality = CarlaEnvironmentParameters.Quality.EPIC
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vis_params = VisualizationParameters()
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vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()]
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vis_params.dump_mp4 = True
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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=vis_params)
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