def test_filter():
# make an RGB observation smaller
squeeze_filter = InputFilter()
squeeze_filter.add_observation_filter('observation', 'squeeze',
ObservationSqueezeFilter())
squeeze_filter_with_axis = InputFilter()
squeeze_filter_with_axis.add_observation_filter(
'observation', 'squeeze', ObservationSqueezeFilter(2))
observation = np.random.rand(20, 30, 1, 3)
env_response = EnvResponse(next_state={'observation': observation},
reward=0,
game_over=False)
result = squeeze_filter.filter(env_response)[0]
result_with_axis = squeeze_filter_with_axis.filter(env_response)[0]
unfiltered_observation_shape = env_response.next_state['observation'].shape
filtered_observation_shape = result.next_state['observation'].shape
filtered_observation_with_axis_shape = result_with_axis.next_state[
'observation'].shape
# make sure the original observation is unchanged
assert unfiltered_observation_shape == observation.shape
# make sure the filtering is done correctly
assert filtered_observation_shape == (20, 30, 3)
assert filtered_observation_with_axis_shape == (20, 30, 3)
observation = np.random.rand(1, 30, 1, 3)
env_response = EnvResponse(next_state={'observation': observation},
reward=0,
game_over=False)
result = squeeze_filter.filter(env_response)[0]
assert result.next_state['observation'].shape == (30, 3)
def test_filter(env_response):
crop_low = np.array([0, 5, 10])
crop_high = np.array([5, 10, 20])
crop_filter = InputFilter()
crop_filter.add_observation_filter('observation', 'crop', ObservationCropFilter(crop_low, crop_high))
result = crop_filter.filter(env_response)[0]
unfiltered_observation = env_response.next_state['observation']
filtered_observation = result.next_state['observation']
# validate the shape of the filtered observation
assert filtered_observation.shape == (5, 5, 10)
# validate the content of the filtered observation
assert np.all(filtered_observation == unfiltered_observation[0:5, 5:10, 10:20])
# crop with -1 on some axes
crop_low = np.array([0, 0, 0])
crop_high = np.array([5, -1, -1])
crop_filter = InputFilter()
crop_filter.add_observation_filter('observation', 'crop', ObservationCropFilter(crop_low, crop_high))
result = crop_filter.filter(env_response)[0]
unfiltered_observation = env_response.next_state['observation']
filtered_observation = result.next_state['observation']
# validate the shape of the filtered observation
assert filtered_observation.shape == (5, 20, 30)
# validate the content of the filtered observation
assert np.all(filtered_observation == unfiltered_observation[0:5, :, :])
def test_get_filtered_observation_space():
# error on wrong number of channels
rescale_filter = InputFilter()
rescale_filter.add_observation_filter(
'observation', 'rescale',
ObservationRescaleSizeByFactorFilter(
0.5, RescaleInterpolationType.BILINEAR))
observation_space = ObservationSpace(np.array([10, 20, 5]))
with pytest.raises(ValueError):
filtered_observation_space = rescale_filter.get_filtered_observation_space(
'observation', observation_space)
# error on wrong number of dimensions
observation_space = ObservationSpace(np.array([10, 20, 10, 3]))
with pytest.raises(ValueError):
filtered_observation_space = rescale_filter.get_filtered_observation_space(
'observation', observation_space)
# make sure the new observation space shape is calculated correctly
observation_space = ObservationSpace(np.array([10, 20, 3]))
filtered_observation_space = rescale_filter.get_filtered_observation_space(
'observation', observation_space)
assert np.all(filtered_observation_space.shape == np.array([5, 10, 3]))
# make sure the original observation space is unchanged
assert np.all(observation_space.shape == np.array([10, 20, 3]))
def test_get_filtered_observation_space():
# error on observation space with shape not matching the filter squeeze axis configuration
squeeze_filter = InputFilter()
squeeze_filter.add_observation_filter('observation', 'squeeze',
ObservationSqueezeFilter(axis=3))
observation_space = ObservationSpace(np.array([20, 1, 30, 3]), 0, 100)
small_observation_space = ObservationSpace(np.array([20, 1, 30]), 0, 100)
with pytest.raises(ValueError):
squeeze_filter.get_filtered_observation_space('observation',
observation_space)
squeeze_filter.get_filtered_observation_space('observation',
small_observation_space)
# verify output observation space is correct
observation_space = ObservationSpace(np.array([1, 2, 3, 1]), 0, 200)
result = squeeze_filter.get_filtered_observation_space(
'observation', observation_space)
assert np.all(result.shape == np.array([1, 2, 3]))
squeeze_filter = InputFilter()
squeeze_filter.add_observation_filter('observation', 'squeeze',
ObservationSqueezeFilter())
result = squeeze_filter.get_filtered_observation_space(
'observation', observation_space)
assert np.all(result.shape == np.array([2, 3]))
def test_filter():
# make an RGB observation smaller
env_response = EnvResponse(
next_state={'observation': np.ones([20, 30, 3])},
reward=0,
game_over=False)
rescale_filter = InputFilter()
rescale_filter.add_observation_filter(
'observation', 'rescale', ObservationRescaleSizeByFactorFilter(0.5))
result = rescale_filter.filter(env_response)[0]
unfiltered_observation = env_response.next_state['observation']
filtered_observation = result.next_state['observation']
# make sure the original observation is unchanged
assert unfiltered_observation.shape == (20, 30, 3)
# validate the shape of the filtered observation
assert filtered_observation.shape == (10, 15, 3)
# make a grayscale observation bigger
env_response = EnvResponse(next_state={'observation': np.ones([20, 30])},
reward=0,
game_over=False)
rescale_filter = InputFilter()
rescale_filter.add_observation_filter(
'observation', 'rescale', ObservationRescaleSizeByFactorFilter(2))
result = rescale_filter.filter(env_response)[0]
filtered_observation = result.next_state['observation']
# validate the shape of the filtered observation
assert filtered_observation.shape == (40, 60)
assert np.all(filtered_observation == np.ones([40, 60]))
def test_filter():
# make an RGB observation smaller
transition = EnvResponse(next_state={'observation': np.ones([20, 30, 3])},
reward=0,
game_over=False)
rescale_filter = InputFilter()
rescale_filter.add_observation_filter(
'observation', 'rescale',
ObservationRescaleToSizeFilter(
ImageObservationSpace(np.array([10, 20, 3]), high=255)))
result = rescale_filter.filter(transition)[0]
unfiltered_observation = transition.next_state['observation']
filtered_observation = result.next_state['observation']
# make sure the original observation is unchanged
assert unfiltered_observation.shape == (20, 30, 3)
# validate the shape of the filtered observation
assert filtered_observation.shape == (10, 20, 3)
assert np.all(filtered_observation == np.ones([10, 20, 3]))
# make a grayscale observation bigger
transition = EnvResponse(next_state={'observation': np.ones([20, 30])},
reward=0,
game_over=False)
rescale_filter = InputFilter()
rescale_filter.add_observation_filter(
'observation', 'rescale',
ObservationRescaleToSizeFilter(
ImageObservationSpace(np.array([40, 60]), high=255)))
result = rescale_filter.filter(transition)[0]
filtered_observation = result.next_state['observation']
# validate the shape of the filtered observation
assert filtered_observation.shape == (40, 60)
assert np.all(filtered_observation == np.ones([40, 60]))
# rescale channels -> error
# with pytest.raises(ValueError):
# InputFilter(
# observation_filters=OrderedDict([('rescale',
# ObservationRescaleToSizeFilter(ImageObservationSpace(np.array([10, 20, 1]),
# high=255)
# ))]))
# TODO: validate input to filter
# different number of axes -> error
# env_response = EnvResponse(state={'observation': np.ones([20, 30, 3])}, reward=0, game_over=False)
# rescale_filter = ObservationRescaleToSizeFilter(ObservationSpace(np.array([10, 20]))
# )
# with pytest.raises(ValueError):
# result = rescale_filter.filter(transition)
# channels first -> error
with pytest.raises(ValueError):
ObservationRescaleToSizeFilter(
ImageObservationSpace(np.array([3, 10, 20]), high=255))
def test_filter():
# Keep
observation_space = VectorObservationSpace(
3, measurements_names=['a', 'b', 'c'])
env_response = EnvResponse(next_state={'observation': np.ones([3])},
reward=0,
game_over=False)
reduction_filter = InputFilter()
reduction_filter.add_observation_filter(
'observation', 'reduce',
ObservationReductionBySubPartsNameFilter(
["a"],
ObservationReductionBySubPartsNameFilter.ReductionMethod.Keep))
reduction_filter.get_filtered_observation_space('observation',
observation_space)
result = reduction_filter.filter(env_response)[0]
unfiltered_observation = env_response.next_state['observation']
filtered_observation = result.next_state['observation']
# make sure the original observation is unchanged
assert unfiltered_observation.shape == (3, )
# validate the shape of the filtered observation
assert filtered_observation.shape == (1, )
# Discard
reduction_filter = InputFilter()
reduction_filter.add_observation_filter(
'observation', 'reduce',
ObservationReductionBySubPartsNameFilter(
["a"],
ObservationReductionBySubPartsNameFilter.ReductionMethod.Discard))
reduction_filter.get_filtered_observation_space('observation',
observation_space)
result = reduction_filter.filter(env_response)[0]
unfiltered_observation = env_response.next_state['observation']
filtered_observation = result.next_state['observation']
# make sure the original observation is unchanged
assert unfiltered_observation.shape == (3, )
# validate the shape of the filtered observation
assert filtered_observation.shape == (2, )
def test_get_filtered_observation_space():
# error on observation space with values not matching the filter configuration
uint8_filter = InputFilter()
uint8_filter.add_observation_filter(
'observation', 'to_uint8',
ObservationToUInt8Filter(input_low=0, input_high=200))
observation_space = ObservationSpace(np.array([1, 2, 3]), 0, 100)
with pytest.raises(ValueError):
uint8_filter.get_filtered_observation_space('observation',
observation_space)
# verify output observation space is correct
observation_space = ObservationSpace(np.array([1, 2, 3]), 0, 200)
result = uint8_filter.get_filtered_observation_space(
'observation', observation_space)
assert np.all(result.high == 255)
assert np.all(result.low == 0)
assert np.all(result.shape == observation_space.shape)
def test_filter():
# make an RGB observation smaller
uint8_filter = InputFilter()
uint8_filter.add_observation_filter(
'observation', 'to_uint8',
ObservationToUInt8Filter(input_low=0, input_high=255))
observation = np.random.rand(20, 30, 3) * 255.0
env_response = EnvResponse(next_state={'observation': observation},
reward=0,
game_over=False)
result = uint8_filter.filter(env_response)[0]
unfiltered_observation = env_response.next_state['observation']
filtered_observation = result.next_state['observation']
# make sure the original observation is unchanged
assert unfiltered_observation.dtype == 'float64'
# make sure the filtering is done correctly
assert filtered_observation.dtype == 'uint8'
assert np.all(filtered_observation == observation.astype('uint8'))
def test_get_filtered_observation_space():
# error on wrong number of channels
with pytest.raises(ValueError):
observation_filters = InputFilter()
observation_filters.add_observation_filter(
'observation', 'rescale',
ObservationRescaleToSizeFilter(
ImageObservationSpace(np.array([5, 10, 5]), high=255),
RescaleInterpolationType.BILINEAR))
# mismatch and wrong number of channels
rescale_filter = InputFilter()
rescale_filter.add_observation_filter(
'observation', 'rescale',
ObservationRescaleToSizeFilter(
ImageObservationSpace(np.array([5, 10, 3]), high=255),
RescaleInterpolationType.BILINEAR))
observation_space = PlanarMapsObservationSpace(np.array([10, 20, 5]),
low=0,
high=255)
with pytest.raises(ValueError):
rescale_filter.get_filtered_observation_space('observation',
observation_space)
# error on wrong number of dimensions
observation_space = ObservationSpace(np.array([10, 20, 10, 3]), high=255)
with pytest.raises(ValueError):
rescale_filter.get_filtered_observation_space('observation',
observation_space)
# make sure the new observation space shape is calculated correctly
observation_space = ImageObservationSpace(np.array([10, 20, 3]), high=255)
filtered_observation_space = rescale_filter.get_filtered_observation_space(
'observation', observation_space)
assert np.all(filtered_observation_space.shape == np.array([5, 10, 3]))
# make sure the original observation space is unchanged
assert np.all(observation_space.shape == np.array([10, 20, 3]))
def test_get_filtered_observation_space():
# Keep
observation_space = VectorObservationSpace(
3, measurements_names=['a', 'b', 'c'])
env_response = EnvResponse(next_state={'observation': np.ones([3])},
reward=0,
game_over=False)
reduction_filter = InputFilter()
reduction_filter.add_observation_filter(
'observation', 'reduce',
ObservationReductionBySubPartsNameFilter(
["a"],
ObservationReductionBySubPartsNameFilter.ReductionMethod.Keep))
filtered_observation_space = reduction_filter.get_filtered_observation_space(
'observation', observation_space)
assert np.all(filtered_observation_space.shape == np.array([1]))
assert filtered_observation_space.measurements_names == ['a']
# Discard
observation_space = VectorObservationSpace(
3, measurements_names=['a', 'b', 'c'])
env_response = EnvResponse(next_state={'observation': np.ones([3])},
reward=0,
game_over=False)
reduction_filter = InputFilter()
reduction_filter.add_observation_filter(
'observation', 'reduce',
ObservationReductionBySubPartsNameFilter(
["a"],
ObservationReductionBySubPartsNameFilter.ReductionMethod.Discard))
filtered_observation_space = reduction_filter.get_filtered_observation_space(
'observation', observation_space)
assert np.all(filtered_observation_space.shape == np.array([2]))
assert filtered_observation_space.measurements_names == ['b', 'c']
def test_get_filtered_observation_space():
crop_low = np.array([0, 5, 10])
crop_high = np.array([5, 10, 20])
crop_filter = InputFilter()
crop_filter.add_observation_filter('observation', 'crop', ObservationCropFilter(crop_low, crop_high))
observation_space = ObservationSpace(np.array([5, 10, 20]))
filtered_observation_space = crop_filter.get_filtered_observation_space('observation', observation_space)
# make sure the new observation space shape is calculated correctly
assert np.all(filtered_observation_space.shape == np.array([5, 5, 10]))
# make sure the original observation space is unchanged
assert np.all(observation_space.shape == np.array([5, 10, 20]))
# crop_high is bigger than the observation space
high_error_observation_space = ObservationSpace(np.array([3, 8, 14]))
with pytest.raises(ValueError):
crop_filter.get_filtered_observation_space('observation', high_error_observation_space)
# crop_low is bigger than the observation space
low_error_observation_space = ObservationSpace(np.array([3, 3, 10]))
with pytest.raises(ValueError):
crop_filter.get_filtered_observation_space('observation', low_error_observation_space)
# crop with -1 on some axes
crop_low = np.array([0, 0, 0])
crop_high = np.array([5, -1, -1])
crop_filter = InputFilter()
crop_filter.add_observation_filter('observation', 'crop', ObservationCropFilter(crop_low, crop_high))
observation_space = ObservationSpace(np.array([5, 10, 20]))
filtered_observation_space = crop_filter.get_filtered_observation_space('observation', observation_space)
# make sure the new observation space shape is calculated correctly
assert np.all(filtered_observation_space.shape == np.array([5, 10, 20]))
def get_graph_manager(hp_dict,
agent_list,
run_phase_subject,
enable_domain_randomization=False,
done_condition=any,
run_type=str(RunType.ROLLOUT_WORKER),
pause_physics=None,
unpause_physics=None):
####################
# Hyperparameters #
####################
training_algorithm = agent_list[
0].ctrl.model_metadata.training_algorithm if agent_list else None
params = get_updated_hyper_parameters(hp_dict, training_algorithm)
params_json = json.dumps(params, indent=2, sort_keys=True)
print("Using the following hyper-parameters", params_json, sep='\n')
####################
# Graph Scheduling #
####################
schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(
params[HyperParameterKeys.TERMINATION_CONDITION_MAX_EPISODES.value])
schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(40)
schedule_params.evaluation_steps = EnvironmentEpisodes(5)
schedule_params.heatup_steps = EnvironmentSteps(0)
#########
# Agent #
#########
trainable_agents_list = list()
non_trainable_agents_list = list()
for agent in agent_list:
if agent.network_settings:
if TrainingAlgorithm.SAC.value == training_algorithm:
agent_params = get_sac_params(DeepRacerSACAgentParams(), agent,
params, run_type)
else:
agent_params = get_clipped_ppo_params(
DeepRacerClippedPPOAgentParams(), agent, params)
agent_params.env_agent = agent
input_filter = InputFilter(is_a_reference_filter=True)
for observation in agent.network_settings['input_embedders'].keys(
):
if observation == Input.LEFT_CAMERA.value or observation == Input.CAMERA.value or\
observation == Input.OBSERVATION.value:
input_filter.add_observation_filter(
observation, 'to_grayscale', ObservationRGBToYFilter())
input_filter.add_observation_filter(
observation, 'to_uint8',
ObservationToUInt8Filter(0, 255))
input_filter.add_observation_filter(
observation, 'stacking', ObservationStackingFilter(1))
if observation == Input.STEREO.value:
input_filter.add_observation_filter(
observation, 'to_uint8',
ObservationToUInt8Filter(0, 255))
if observation == Input.LIDAR.value:
input_filter.add_observation_filter(
observation, 'clipping',
ObservationClippingFilter(0.15, 1.0))
if observation == Input.SECTOR_LIDAR.value:
input_filter.add_observation_filter(
observation, 'binary', ObservationBinarySectorFilter())
agent_params.input_filter = input_filter()
trainable_agents_list.append(agent_params)
else:
non_trainable_agents_list.append(agent)
###############
# Environment #
###############
env_params = DeepRacerRacetrackEnvParameters()
env_params.agents_params = trainable_agents_list
env_params.non_trainable_agents = non_trainable_agents_list
env_params.level = 'DeepRacerRacetrackEnv-v0'
env_params.run_phase_subject = run_phase_subject
env_params.enable_domain_randomization = enable_domain_randomization
env_params.done_condition = done_condition
env_params.pause_physics = pause_physics
env_params.unpause_physics = unpause_physics
vis_params = VisualizationParameters()
vis_params.dump_mp4 = False
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 400
preset_validation_params.max_episodes_to_achieve_reward = 10000
graph_manager = MultiAgentGraphManager(
agents_params=trainable_agents_list,
env_params=env_params,
schedule_params=schedule_params,
vis_params=vis_params,
preset_validation_params=preset_validation_params,
done_condition=done_condition)
return graph_manager, params_json
def rgb_to_y_filter():
rgb_to_y_filter = InputFilter()
rgb_to_y_filter.add_observation_filter('observation', 'rgb_to_y',
ObservationRGBToYFilter())
return rgb_to_y_filter
def get_graph_manager(hp_dict,
agent_list,
run_phase_subject,
enable_domain_randomization=False,
done_condition=any,
run_type=str(RunType.ROLLOUT_WORKER),
pause_physics=None,
unpause_physics=None):
####################
# Hyperparameters #
####################
# Note: The following three line hard-coded to pick the first agent's trainig algorithm
# and dump the hyper parameters for the particular training algorithm into json
# for training jobs (so that the console display the training hyperparameters correctly)
# since right now, we only support training one model at a time.
# TODO: clean these lines up when we support multi-agent training.
training_algorithm = agent_list[
0].ctrl.model_metadata.training_algorithm if agent_list else None
params = get_updated_hyper_parameters(hp_dict, training_algorithm)
params_json = json.dumps(params, indent=2, sort_keys=True)
print("Using the following hyper-parameters", params_json, sep='\n')
####################
# Graph Scheduling #
####################
schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(
params[HyperParameterKeys.TERMINATION_CONDITION_MAX_EPISODES.value])
schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(40)
schedule_params.evaluation_steps = EnvironmentEpisodes(5)
schedule_params.heatup_steps = EnvironmentSteps(0)
#########
# Agent #
#########
trainable_agents_list = list()
non_trainable_agents_list = list()
for agent in agent_list:
if agent.network_settings:
training_algorithm = agent.ctrl.model_metadata.training_algorithm
params = get_updated_hyper_parameters(hp_dict, training_algorithm)
if TrainingAlgorithm.SAC.value == training_algorithm:
agent_params = get_sac_params(DeepRacerSACAgentParams(), agent,
params, run_type)
else:
agent_params = get_clipped_ppo_params(
DeepRacerClippedPPOAgentParams(), agent, params)
agent_params.env_agent = agent
input_filter = InputFilter(is_a_reference_filter=True)
for observation in agent.network_settings['input_embedders'].keys(
):
if observation == Input.LEFT_CAMERA.value or observation == Input.CAMERA.value or \
observation == Input.OBSERVATION.value:
input_filter.add_observation_filter(
observation, 'to_grayscale', ObservationRGBToYFilter())
input_filter.add_observation_filter(
observation, 'to_uint8',
ObservationToUInt8Filter(0, 255))
input_filter.add_observation_filter(
observation, 'stacking', ObservationStackingFilter(1))
if observation == Input.STEREO.value:
input_filter.add_observation_filter(
observation, 'to_uint8',
ObservationToUInt8Filter(0, 255))
if observation == Input.LIDAR.value:
input_filter.add_observation_filter(
observation, 'clipping',
ObservationClippingFilter(0.15, 1.0))
if observation == Input.SECTOR_LIDAR.value:
sector_binary_filter = ObservationSectorDiscretizeFilter(
num_sectors=NUMBER_OF_LIDAR_SECTORS,
num_values_per_sector=1,
clipping_dist=SECTOR_LIDAR_CLIPPING_DIST)
input_filter.add_observation_filter(
observation, 'binary', sector_binary_filter)
if observation == Input.DISCRETIZED_SECTOR_LIDAR.value:
num_sectors = agent.ctrl.model_metadata.lidar_num_sectors
num_values_per_sector = agent.ctrl.model_metadata.lidar_num_values_per_sector
clipping_dist = agent.ctrl.model_metadata.lidar_clipping_dist
sector_discretize_filter = ObservationSectorDiscretizeFilter(
num_sectors=num_sectors,
num_values_per_sector=num_values_per_sector,
clipping_dist=clipping_dist)
input_filter.add_observation_filter(
observation, 'discrete', sector_discretize_filter)
agent_params.input_filter = input_filter()
trainable_agents_list.append(agent_params)
else:
non_trainable_agents_list.append(agent)
###############
# Environment #
###############
env_params = DeepRacerRacetrackEnvParameters()
env_params.agents_params = trainable_agents_list
env_params.non_trainable_agents = non_trainable_agents_list
env_params.level = 'DeepRacerRacetrackEnv-v0'
env_params.run_phase_subject = run_phase_subject
env_params.enable_domain_randomization = enable_domain_randomization
env_params.done_condition = done_condition
env_params.pause_physics = pause_physics
env_params.unpause_physics = unpause_physics
vis_params = VisualizationParameters()
vis_params.dump_mp4 = False
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 400
preset_validation_params.max_episodes_to_achieve_reward = 10000
graph_manager = MultiAgentGraphManager(
agents_params=trainable_agents_list,
env_params=env_params,
schedule_params=schedule_params,
vis_params=vis_params,
preset_validation_params=preset_validation_params,
done_condition=done_condition)
return graph_manager, params_json
def get_graph_manager(hp_dict, agent_list, run_phase_subject):
####################
# All Default Parameters #
####################
params = {}
params["batch_size"] = int(hp_dict.get("batch_size", 64))
params["num_epochs"] = int(hp_dict.get("num_epochs", 10))
params["stack_size"] = int(hp_dict.get("stack_size", 1))
params["lr"] = float(hp_dict.get("lr", 0.0003))
params["exploration_type"] = (hp_dict.get("exploration_type",
"categorical")).lower()
params["e_greedy_value"] = float(hp_dict.get("e_greedy_value", .05))
params["epsilon_steps"] = int(hp_dict.get("epsilon_steps", 10000))
params["beta_entropy"] = float(hp_dict.get("beta_entropy", .01))
params["discount_factor"] = float(hp_dict.get("discount_factor", .999))
params["loss_type"] = hp_dict.get("loss_type",
"Mean squared error").lower()
params["num_episodes_between_training"] = int(
hp_dict.get("num_episodes_between_training", 20))
params["term_cond_max_episodes"] = int(
hp_dict.get("term_cond_max_episodes", 100000))
params["term_cond_avg_score"] = float(
hp_dict.get("term_cond_avg_score", 100000))
params_json = json.dumps(params, indent=2, sort_keys=True)
print("Using the following hyper-parameters", params_json, sep='\n')
####################
# Graph Scheduling #
####################
schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(
params["term_cond_max_episodes"])
schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(40)
schedule_params.evaluation_steps = EnvironmentEpisodes(5)
schedule_params.heatup_steps = EnvironmentSteps(0)
#########
# Agent #
#########
trainable_agents_list = list()
non_trainable_agents_list = list()
for agent in agent_list:
agent_params = DeepRacerAgentParams()
if agent.network_settings:
agent_params.env_agent = agent
agent_params.network_wrappers['main'].learning_rate = params["lr"]
agent_params.network_wrappers['main'].input_embedders_parameters = \
create_input_embedder(agent.network_settings['input_embedders'],
agent.network_settings['embedder_type'],
agent.network_settings['activation_function'])
agent_params.network_wrappers['main'].middleware_parameters = \
create_middle_embedder(agent.network_settings['middleware_embedders'],
agent.network_settings['embedder_type'],
agent.network_settings['activation_function'])
input_filter = InputFilter(is_a_reference_filter=True)
for observation in agent.network_settings['input_embedders'].keys(
):
if observation == Input.LEFT_CAMERA.value or observation == Input.CAMERA.value or\
observation == Input.OBSERVATION.value:
input_filter.add_observation_filter(
observation, 'to_grayscale', ObservationRGBToYFilter())
input_filter.add_observation_filter(
observation, 'to_uint8',
ObservationToUInt8Filter(0, 255))
input_filter.add_observation_filter(
observation, 'stacking', ObservationStackingFilter(1))
if observation == Input.STEREO.value:
input_filter.add_observation_filter(
observation, 'to_uint8',
ObservationToUInt8Filter(0, 255))
if observation == Input.LIDAR.value:
input_filter.add_observation_filter(
observation, 'clipping',
ObservationClippingFilter(0.15, 1.0))
if observation == Input.SECTOR_LIDAR.value:
input_filter.add_observation_filter(
observation, 'binary', ObservationBinarySectorFilter())
agent_params.input_filter = input_filter()
agent_params.network_wrappers['main'].batch_size = params[
"batch_size"]
agent_params.network_wrappers['main'].optimizer_epsilon = 1e-5
agent_params.network_wrappers['main'].adam_optimizer_beta2 = 0.999
if params["loss_type"] == "huber":
agent_params.network_wrappers[
'main'].replace_mse_with_huber_loss = True
agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2
agent_params.algorithm.clipping_decay_schedule = LinearSchedule(
1.0, 0, 1000000)
agent_params.algorithm.beta_entropy = params["beta_entropy"]
agent_params.algorithm.gae_lambda = 0.95
agent_params.algorithm.discount = params["discount_factor"]
agent_params.algorithm.optimization_epochs = params["num_epochs"]
agent_params.algorithm.estimate_state_value_using_gae = True
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = \
EnvironmentEpisodes(params["num_episodes_between_training"])
agent_params.algorithm.num_consecutive_playing_steps = \
EnvironmentEpisodes(params["num_episodes_between_training"])
agent_params.algorithm.distributed_coach_synchronization_type = \
DistributedCoachSynchronizationType.SYNC
if params["exploration_type"] == "categorical":
agent_params.exploration = CategoricalParameters()
else:
agent_params.exploration = EGreedyParameters()
agent_params.exploration.epsilon_schedule = LinearSchedule(
1.0, params["e_greedy_value"], params["epsilon_steps"])
trainable_agents_list.append(agent_params)
else:
non_trainable_agents_list.append(agent)
###############
# Environment #
###############
env_params = DeepRacerRacetrackEnvParameters()
env_params.agents_params = trainable_agents_list
env_params.non_trainable_agents = non_trainable_agents_list
env_params.level = 'DeepRacerRacetrackEnv-v0'
env_params.run_phase_subject = run_phase_subject
vis_params = VisualizationParameters()
vis_params.dump_mp4 = False
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 400
preset_validation_params.max_episodes_to_achieve_reward = 10000
graph_manager = MultiAgentGraphManager(
agents_params=trainable_agents_list,
env_params=env_params,
schedule_params=schedule_params,
vis_params=vis_params,
preset_validation_params=preset_validation_params)
return graph_manager, params_json
# Fetch
gym_fetch_envs = ['reach', 'slide', 'push', 'pick_and_place']
fetch_v1 = {
e: "{}".format('Fetch' + lower_under_to_upper(e) + '-v1')
for e in gym_fetch_envs
}
"""
Atari Environment Components
"""
AtariInputFilter = InputFilter(is_a_reference_filter=True)
AtariInputFilter.add_reward_filter('clipping', RewardClippingFilter(-1.0, 1.0))
AtariInputFilter.add_observation_filter(
'observation',
'rescaling',
ObservationRescaleToSizeFilter(
ImageObservationSpace(
np.array([84, 84, 3]), #np.array([224, 224, 3]),
high=255)))
#AtariInputFilter.add_observation_filter('observation', 'to_grayscale', ObservationRGBToYFilter())
#AtariInputFilter.add_observation_filter('observation', 'to_uint8', ObservationToUInt8Filter(0, 255))
#AtariInputFilter.add_observation_filter('observation', 'stacking', ObservationStackingFilter(4))
AtariOutputFilter = NoOutputFilter()
class Atari(GymEnvironmentParameters):
def __init__(self, level=None):
super().__init__(level=level)
self.frame_skip = 4
self.max_over_num_frames = 2
self.random_initialization_steps = 30
'SELECT_WEAPON5': ord("5"),
'SELECT_WEAPON6': ord("6"),
'SELECT_WEAPON7': ord("7"),
'SELECT_WEAPON8': ord("8"),
'SELECT_WEAPON9': ord("9"),
'SPEED': 304, # shift
'STRAFE': 9, # tab
'TURN180': ord("u"),
'TURN_LEFT': ord("a"), # left arrow
'TURN_RIGHT': ord("d"), # right arrow
'USE': ord("f"),
}
DoomInputFilter = InputFilter(is_a_reference_filter=True)
DoomInputFilter.add_observation_filter(
'observation', 'rescaling',
ObservationRescaleToSizeFilter(
ImageObservationSpace(np.array([60, 76, 3]), high=255)))
DoomInputFilter.add_observation_filter('observation', 'to_grayscale',
ObservationRGBToYFilter())
DoomInputFilter.add_observation_filter('observation', 'to_uint8',
ObservationToUInt8Filter(0, 255))
DoomInputFilter.add_observation_filter('observation', 'stacking',
ObservationStackingFilter(3))
DoomOutputFilter = OutputFilter(is_a_reference_filter=True)
DoomOutputFilter.add_action_filter('to_discrete', FullDiscreteActionSpaceMap())
class DoomEnvironmentParameters(EnvironmentParameters):
def __init__(self, level=None):
super().__init__(level=level)
key_map = {
'BRAKE': (274, ), # down arrow
'GAS': (273, ), # up arrow
'TURN_LEFT': (276, ), # left arrow
'TURN_RIGHT': (275, ), # right arrow
'GAS_AND_TURN_LEFT': (273, 276),
'GAS_AND_TURN_RIGHT': (273, 275),
'BRAKE_AND_TURN_LEFT': (274, 276),
'BRAKE_AND_TURN_RIGHT': (274, 275),
}
CarlaInputFilter = InputFilter(is_a_reference_filter=True)
CarlaInputFilter.add_observation_filter(
'forward_camera', 'rescaling',
ObservationRescaleToSizeFilter(
ImageObservationSpace(np.array([128, 180, 3]), high=255)))
CarlaInputFilter.add_observation_filter('forward_camera', 'to_grayscale',
ObservationRGBToYFilter())
CarlaInputFilter.add_observation_filter('forward_camera', 'to_uint8',
ObservationToUInt8Filter(0, 255))
CarlaInputFilter.add_observation_filter('forward_camera', 'stacking',
ObservationStackingFilter(4))
CarlaOutputFilter = NoOutputFilter()
class CameraTypes(Enum):
FRONT = "forward_camera"
LEFT = "left_camera"
RIGHT = "right_camera"
def stack_filter():
stack_filter = InputFilter()
stack_filter.add_observation_filter(
'observation', 'stack', ObservationStackingFilter(4, stacking_axis=-1))
return stack_filter
def get_graph_manager(**hp_dict):
####################
# All Default Parameters #
####################
params = {}
params["batch_size"] = int(hp_dict.get("batch_size", 64))
params["num_epochs"] = int(hp_dict.get("num_epochs", 10))
params["stack_size"] = int(hp_dict.get("stack_size", 1))
params["lr"] = float(hp_dict.get("lr", 0.0003))
params["lr_decay_rate"] = float(hp_dict.get("lr_decay_rate", 0))
params["lr_decay_steps"] = float(hp_dict.get("lr_decay_steps", 0))
params["exploration_type"] = (hp_dict.get("exploration_type", "categorical")).lower()
params["e_greedy_value"] = float(hp_dict.get("e_greedy_value", .05))
params["epsilon_steps"] = int(hp_dict.get("epsilon_steps", 10000))
params["beta_entropy"] = float(hp_dict.get("beta_entropy", .01))
params["discount_factor"] = float(hp_dict.get("discount_factor", .999))
params["loss_type"] = hp_dict.get("loss_type", "Mean squared error").lower()
params["num_episodes_between_training"] = int(hp_dict.get("num_episodes_between_training", 20))
params["term_cond_max_episodes"] = int(hp_dict.get("term_cond_max_episodes", 100000))
params["term_cond_avg_score"] = float(hp_dict.get("term_cond_avg_score", 100000))
params["tensorboard"] = hp_dict.get("tensorboard", False)
params["dump_mp4"] = hp_dict.get("dump_mp4", False)
params["dump_gifs"] = hp_dict.get("dump_gifs", False)
params_json = json.dumps(params, indent=2, sort_keys=True)
print("Using the following hyper-parameters", params_json, sep='\n')
####################
# Graph Scheduling #
####################
schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(params["term_cond_max_episodes"])
schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(40)
schedule_params.evaluation_steps = EnvironmentEpisodes(5)
schedule_params.heatup_steps = EnvironmentSteps(0)
#########
# Agent #
#########
agent_params = ClippedPPOAgentParameters()
agent_params.network_wrappers['main'].learning_rate = params["lr"]
agent_params.network_wrappers['main'].learning_rate_decay_rate = params["lr_decay_rate"]
agent_params.network_wrappers['main'].learning_rate_decay_steps = params["lr_decay_steps"]
agent_params.network_wrappers['main'].input_embedders_parameters['observation'].activation_function = 'relu'
# Replace the default CNN with single layer Conv2d(32, 3, 1)
# agent_params.network_wrappers['main'].input_embedders_parameters['observation'].scheme = EmbedderScheme.Shallow
# agent_params.network_wrappers['main'].input_embedders_parameters['observation'].dropout_rate = 0.3
agent_params.network_wrappers['main'].middleware_parameters.activation_function = 'relu'
# agent_params.network_wrappers['main'].middleware_parameters.scheme = MiddlewareScheme.Shallow
# agent_params.network_wrappers['main'].middleware_parameters.dropout_rate = 0.3
agent_params.network_wrappers['main'].batch_size = params["batch_size"]
agent_params.network_wrappers['main'].optimizer_epsilon = 1e-5
agent_params.network_wrappers['main'].adam_optimizer_beta2 = 0.999
# agent_params.network_wrappers['main'].l2_regularization = 2e-5
if params["loss_type"] == "huber":
agent_params.network_wrappers['main'].replace_mse_with_huber_loss = True
agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2
agent_params.algorithm.clipping_decay_schedule = LinearSchedule(1.0, 0, 1000000)
agent_params.algorithm.beta_entropy = params["beta_entropy"]
agent_params.algorithm.gae_lambda = 0.95
agent_params.algorithm.discount = params["discount_factor"]
agent_params.algorithm.optimization_epochs = params["num_epochs"]
agent_params.algorithm.estimate_state_value_using_gae = True
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentEpisodes(
params["num_episodes_between_training"])
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(params["num_episodes_between_training"])
agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC
if params["exploration_type"] == "categorical":
agent_params.exploration = CategoricalParameters()
else:
agent_params.exploration = EGreedyParameters()
agent_params.exploration.epsilon_schedule = LinearSchedule(1.0,
params["e_greedy_value"],
params["epsilon_steps"])
###############
# Environment #
###############
DeepRacerInputFilter = InputFilter(is_a_reference_filter=True)
# Add an observation image pertubation for many aspects
# DeepRacerInputFilter.add_observation_filter('observation', 'perturb_color', ObservationColorPerturbation(0.2))
# Rescale to much smaller input when using shallow networks to avoid OOM
# DeepRacerInputFilter.add_observation_filter('observation', 'rescaling',
# ObservationRescaleToSizeFilter(ImageObservationSpace(np.array([84, 84, 3]),
# high=255)))
DeepRacerInputFilter.add_observation_filter('observation', 'to_grayscale', ObservationRGBToYFilter())
DeepRacerInputFilter.add_observation_filter('observation', 'to_uint8', ObservationToUInt8Filter(0, 255))
DeepRacerInputFilter.add_observation_filter('observation', 'stacking',
ObservationStackingFilter(params["stack_size"]))
env_params = GymVectorEnvironment()
env_params.default_input_filter = DeepRacerInputFilter
env_params.level = 'DeepRacerRacetrackCustomActionSpaceEnv-v0'
vis_params = VisualizationParameters()
vis_params.tensorboard = params["tensorboard"]
vis_params.dump_mp4 = params["dump_mp4"]
vis_params.dump_gifs = params["dump_gifs"]
# AlwaysDumpFilter, MaxDumpFilter, EveryNEpisodesDumpFilter, SelectedPhaseOnlyDumpFilter
vis_params.video_dump_filters = [AlwaysDumpFilter()]
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 400
preset_validation_params.max_episodes_to_achieve_reward = 10000
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
schedule_params=schedule_params, vis_params=vis_params,
preset_validation_params=preset_validation_params)
return graph_manager, params_json
agent_params.algorithm.optimization_epochs = 10
agent_params.algorithm.estimate_state_value_using_gae = True
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentEpisodes(
20)
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(20)
agent_params.exploration = CategoricalParameters()
agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC
###############
# Environment #
###############
SilverstoneInputFilter = InputFilter(is_a_reference_filter=True)
SilverstoneInputFilter.add_observation_filter('front_camera', 'to_grayscale',
ObservationRGBToYFilter())
SilverstoneInputFilter.add_observation_filter('front_camera', 'to_uint8',
ObservationToUInt8Filter(0, 255))
SilverstoneInputFilter.add_observation_filter('front_camera', 'stacking',
ObservationStackingFilter(1))
SilverstoneInputFilter.add_observation_filter(
'lidar', 'clipping', ObservationClippingFilter(0.15, 1.0))
env_params = GymVectorEnvironment()
env_params.default_input_filter = SilverstoneInputFilter
env_params.level = 'DeepRacerRacetrackCustomActionSpaceEnv-v0'
vis_params = VisualizationParameters()
vis_params.dump_mp4 = False
########
# Starcraft Constants
_NOOP = actions.FUNCTIONS.no_op.id
_MOVE_SCREEN = actions.FUNCTIONS.Move_screen.id
_SELECT_ARMY = actions.FUNCTIONS.select_army.id
_PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index
_NOT_QUEUED = [0]
_SELECT_ALL = [0]
class StarcraftObservationType(Enum):
Features = 0
RGB = 1
StarcraftInputFilter = InputFilter(is_a_reference_filter=True)
StarcraftInputFilter.add_observation_filter('screen', 'move_axis',
ObservationMoveAxisFilter(0, -1))
StarcraftInputFilter.add_observation_filter(
'screen', 'rescaling',
ObservationRescaleToSizeFilter(
PlanarMapsObservationSpace(np.array([84, 84, 1]),
low=0,
high=255,
channels_axis=-1)))
StarcraftInputFilter.add_observation_filter('screen', 'to_uint8',
ObservationToUInt8Filter(0, 255))
StarcraftInputFilter.add_observation_filter('minimap', 'move_axis',
ObservationMoveAxisFilter(0, -1))
StarcraftInputFilter.add_observation_filter(
'minimap', 'rescaling',
ObservationRescaleToSizeFilter(
agent_params.network_wrappers[
'main'].gradients_clipping_method = GradientClippingMethod.ClipByValue
###############
# Environment #
###############
import jsbsim
import gym_jsbsim
from rl_coach.filters.filter import NoInputFilter, NoOutputFilter
from rl_coach.filters.filter import InputFilter
from rl_coach.filters.observation.observation_stacking_filter import ObservationStackingFilter
vis_params = VisualizationParameters(native_rendering=True)
input = InputFilter(is_a_reference_filter=True)
input.add_observation_filter('observation', 'stacking',
ObservationStackingFilter(10))
class MyGymVectorEnvironment(GymVectorEnvironment):
def __init__(self, level=None):
super().__init__(level=level)
self.frame_skip = 1
self.default_input_filter = NoInputFilter(
) # hrmm.. my custom input filter errored out
self.default_output_filter = NoOutputFilter()
env_params = MyGymVectorEnvironment(
level='JSBSim-HeadingControlTask-Cessna172P-Shaping.EXTRA-FG-v0')
# this preset is currently broken - no test
def get_graph_manager(**hp_dict):
####################
# All Default Parameters #
####################
params = {}
params["batch_size"] = int(hp_dict.get("batch_size", 64))
params["num_epochs"] = int(hp_dict.get("num_epochs", 10))
params["stack_size"] = int(hp_dict.get("stack_size", 1))
params["lr"] = float(hp_dict.get("lr", 0.0003))
params["exploration_type"] = (hp_dict.get("exploration_type",
"huber")).lower()
params["e_greedy_value"] = float(hp_dict.get("e_greedy_value", .05))
params["epsilon_steps"] = int(hp_dict.get("epsilon_steps", 10000))
params["beta_entropy"] = float(hp_dict.get("beta_entropy", .01))
params["discount_factor"] = float(hp_dict.get("discount_factor", .999))
params["loss_type"] = hp_dict.get("loss_type",
"Mean squared error").lower()
params["num_episodes_between_training"] = int(
hp_dict.get("num_episodes_between_training", 20))
params["term_cond_max_episodes"] = int(
hp_dict.get("term_cond_max_episodes", 100000))
params["term_cond_avg_score"] = float(
hp_dict.get("term_cond_avg_score", 100000))
params_json = json.dumps(params, indent=2, sort_keys=True)
print("Using the following hyper-parameters", params_json, sep='\n')
####################
# Graph Scheduling #
####################
schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(
params["term_cond_max_episodes"])
schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(40)
schedule_params.evaluation_steps = EnvironmentEpisodes(5)
schedule_params.heatup_steps = EnvironmentSteps(0)
#########
# Agent #
#########
agent_params = ClippedPPOAgentParameters()
agent_params.network_wrappers['main'].learning_rate = params["lr"]
agent_params.network_wrappers['main'].input_embedders_parameters[
'observation'].activation_function = 'relu'
agent_params.network_wrappers[
'main'].middleware_parameters.activation_function = 'relu'
agent_params.network_wrappers['main'].batch_size = params["batch_size"]
agent_params.network_wrappers['main'].optimizer_epsilon = 1e-5
agent_params.network_wrappers['main'].adam_optimizer_beta2 = 0.999
if params["loss_type"] == "huber":
agent_params.network_wrappers[
'main'].replace_mse_with_huber_loss = True
agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2
agent_params.algorithm.clipping_decay_schedule = LinearSchedule(
1.0, 0, 1000000)
agent_params.algorithm.beta_entropy = params["beta_entropy"]
agent_params.algorithm.gae_lambda = 0.95
agent_params.algorithm.discount = params["discount_factor"]
agent_params.algorithm.optimization_epochs = params["num_epochs"]
agent_params.algorithm.estimate_state_value_using_gae = True
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentEpisodes(
params["num_episodes_between_training"])
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(
params["num_episodes_between_training"])
agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC
if params["exploration_type"] == "categorical":
agent_params.exploration = CategoricalParameters()
else:
agent_params.exploration = EGreedyParameters()
agent_params.exploration.epsilon_schedule = LinearSchedule(
1.0, params["e_greedy_value"], params["epsilon_steps"])
###############
# Environment #
###############
SilverstoneInputFilter = InputFilter(is_a_reference_filter=True)
SilverstoneInputFilter.add_observation_filter('observation',
'to_grayscale',
ObservationRGBToYFilter())
SilverstoneInputFilter.add_observation_filter(
'observation', 'to_uint8', ObservationToUInt8Filter(0, 255))
SilverstoneInputFilter.add_observation_filter(
'observation', 'stacking',
ObservationStackingFilter(params["stack_size"]))
env_params = GymVectorEnvironment()
env_params.default_input_filter = SilverstoneInputFilter
env_params.level = 'SilverstoneRacetrack-Discrete-v0'
vis_params = VisualizationParameters()
vis_params.dump_mp4 = False
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 400
preset_validation_params.max_episodes_to_achieve_reward = 1000
graph_manager = BasicRLGraphManager(
agent_params=agent_params,
env_params=env_params,
schedule_params=schedule_params,
vis_params=vis_params,
preset_validation_params=preset_validation_params)
return graph_manager, params_json
def get_graph_manager(**hp_dict):
####################
# All Default Parameters #
####################
params = {}
params["batch_size"] = int(hp_dict.get("batch_size", 64))
params["num_epochs"] = int(hp_dict.get("num_epochs", 10))
params["stack_size"] = int(hp_dict.get("stack_size", 1))
params["lr"] = float(hp_dict.get("lr", 0.0003))
params["exploration_type"] = (hp_dict.get("exploration_type", "huber")).lower()
params["e_greedy_value"] = float(hp_dict.get("e_greedy_value", .05))
params["epsilon_steps"] = int(hp_dict.get("epsilon_steps", 10000))
params["beta_entropy"] = float(hp_dict.get("beta_entropy", .01))
params["discount_factor"] = float(hp_dict.get("discount_factor", .999))
params["loss_type"] = hp_dict.get("loss_type", "Mean squared error").lower()
params["num_episodes_between_training"] = int(hp_dict.get("num_episodes_between_training", 20))
params["term_cond_max_episodes"] = int(hp_dict.get("term_cond_max_episodes", 100000))
params["term_cond_avg_score"] = float(hp_dict.get("term_cond_avg_score", 100000))
params_json = json.dumps(params, indent=2, sort_keys=True)
print("Using the following hyper-parameters", params_json, sep='\n')
####################
# Graph Scheduling #
####################
schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(params["term_cond_max_episodes"])
schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(40)
schedule_params.evaluation_steps = EnvironmentEpisodes(5)
schedule_params.heatup_steps = EnvironmentSteps(0)
#########
# Agent #
#########
agent_params = ClippedPPOAgentParameters()
agent_params.network_wrappers['main'].learning_rate = params["lr"]
agent_params.network_wrappers['main'].input_embedders_parameters['observation'].activation_function = 'relu'
agent_params.network_wrappers['main'].middleware_parameters.activation_function = 'relu'
agent_params.network_wrappers['main'].batch_size = params["batch_size"]
agent_params.network_wrappers['main'].optimizer_epsilon = 1e-5
agent_params.network_wrappers['main'].adam_optimizer_beta2 = 0.999
if params["loss_type"] == "huber":
agent_params.network_wrappers['main'].replace_mse_with_huber_loss = True
agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2
agent_params.algorithm.clipping_decay_schedule = LinearSchedule(1.0, 0, 1000000)
agent_params.algorithm.beta_entropy = params["beta_entropy"]
agent_params.algorithm.gae_lambda = 0.95
agent_params.algorithm.discount = params["discount_factor"]
agent_params.algorithm.optimization_epochs = params["num_epochs"]
agent_params.algorithm.estimate_state_value_using_gae = True
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentEpisodes(
params["num_episodes_between_training"])
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(params["num_episodes_between_training"])
agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC
if params["exploration_type"] == "categorical":
agent_params.exploration = CategoricalParameters()
else:
agent_params.exploration = EGreedyParameters()
agent_params.exploration.epsilon_schedule = LinearSchedule(1.0,
params["e_greedy_value"],
params["epsilon_steps"])
###############
# Environment #
###############
SilverstoneInputFilter = InputFilter(is_a_reference_filter=True)
SilverstoneInputFilter.add_observation_filter('observation', 'to_grayscale', ObservationRGBToYFilter())
SilverstoneInputFilter.add_observation_filter('observation', 'to_uint8', ObservationToUInt8Filter(0, 255))
SilverstoneInputFilter.add_observation_filter('observation', 'stacking',
ObservationStackingFilter(params["stack_size"]))
env_params = GymVectorEnvironment()
env_params.default_input_filter = SilverstoneInputFilter
env_params.level = 'SilverstoneRacetrack-Discrete-v0'
vis_params = VisualizationParameters()
vis_params.dump_mp4 = False
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 400
preset_validation_params.max_episodes_to_achieve_reward = 1000
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
schedule_params=schedule_params, vis_params=vis_params,
preset_validation_params=preset_validation_params)
return graph_manager, params_json
#huber loss
agent_params.network_wrappers['main'].replace_mse_with_huber_loss = True
agent_params.exploration = CategoricalParameters()
agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC
###############
# Environment #
###############
SilverstoneInputFilter = InputFilter(is_a_reference_filter=True)
# SilverstoneInputFilter.add_observation_filter('left_camera', 'to_grayscale', ObservationRGBToYFilter())
# SilverstoneInputFilter.add_observation_filter('left_camera', 'to_uint8', ObservationToUInt8Filter(0, 255))
# SilverstoneInputFilter.add_observation_filter('left_camera', 'stacking', ObservationStackingFilter(1))
SilverstoneInputFilter.add_observation_filter('STEREO_CAMERAS', 'to_uint8',
ObservationToUInt8Filter(0, 255))
SilverstoneInputFilter.add_observation_filter(
'LIDAR', 'clipping', ObservationClippingFilter(0.1, 0.5))
env_params = GymVectorEnvironment()
env_params.default_input_filter = SilverstoneInputFilter
env_params.level = 'DeepRacerRacetrackCustomActionSpaceEnv-v0'
vis_params = VisualizationParameters()
vis_params.dump_mp4 = False
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
agent_params.algorithm.beta_entropy = 0.01 # also try 0.001
agent_params.algorithm.gae_lambda = 0.95
agent_params.algorithm.discount = 0.999
agent_params.algorithm.optimization_epochs = 10
agent_params.algorithm.estimate_state_value_using_gae = True
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentEpisodes(
20)
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(20)
agent_params.exploration = CategoricalParameters()
agent_params.memory.max_size = (MemoryGranularity.Transitions, 10**5)
###############
# Environment #
###############
turtlebot3_input_filter = InputFilter(is_a_reference_filter=True)
turtlebot3_input_filter.add_observation_filter('observation', 'to_grayscale',
ObservationRGBToYFilter())
turtlebot3_input_filter.add_observation_filter(
'observation', 'to_uint8', ObservationToUInt8Filter(0, 255))
turtlebot3_input_filter.add_observation_filter('observation', 'stacking',
ObservationStackingFilter(1))
env_params = GymVectorEnvironment()
env_params.default_input_filter = turtlebot3_input_filter
env_params.level = 'RoboMaker-ObjectTracker-v0'
vis_params = VisualizationParameters()
vis_params.dump_mp4 = False
########
# Test #
########
agent_params.algorithm.optimization_epochs = 10
agent_params.algorithm.estimate_state_value_using_gae = True
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentEpisodes(
20)
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(20)
agent_params.exploration = CategoricalParameters()
agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC
###############
# Environment #
###############
SilverstoneInputFilter = InputFilter(is_a_reference_filter=True)
SilverstoneInputFilter.add_observation_filter('observation', 'to_grayscale',
ObservationRGBToYFilter())
SilverstoneInputFilter.add_observation_filter('observation', 'to_uint8',
ObservationToUInt8Filter(0, 255))
SilverstoneInputFilter.add_observation_filter('observation', 'stacking',
ObservationStackingFilter(1))
env_params = GymVectorEnvironment()
env_params.default_input_filter = SilverstoneInputFilter
env_params.level = 'DeepRacerRacetrackCustomActionSpaceEnv-v0'
vis_params = VisualizationParameters()
vis_params.dump_mp4 = False
########
# Test #
########
agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2
agent_params.algorithm.clipping_decay_schedule = LinearSchedule(1.0, 0, 1000000)
agent_params.algorithm.beta_entropy = 0.01 # also try 0.001
agent_params.algorithm.gae_lambda = 0.95
agent_params.algorithm.discount = 0.999
agent_params.algorithm.optimization_epochs = 10
agent_params.algorithm.estimate_state_value_using_gae = True
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentEpisodes(20)
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(20)
agent_params.exploration = CategoricalParameters()
###############
# Environment #
###############
turtlebot3_input_filter = InputFilter(is_a_reference_filter=True)
turtlebot3_input_filter.add_observation_filter('observation', 'to_grayscale', ObservationRGBToYFilter())
turtlebot3_input_filter.add_observation_filter('observation', 'to_uint8', ObservationToUInt8Filter(0, 255))
turtlebot3_input_filter.add_observation_filter('observation', 'stacking', ObservationStackingFilter(1))
env_params = GymVectorEnvironment()
env_params.default_input_filter = turtlebot3_input_filter
env_params.level = 'SageMaker-TurtleBot3-Discrete-v0'
vis_params = VisualizationParameters()
vis_params.dump_mp4 = False
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
