def __init__(
self,
agent_params: AgentParameters,
env_params: EnvironmentParameters,
schedule_params: ScheduleParameters,
vis_params: VisualizationParameters = VisualizationParameters(),
preset_validation_params:
PresetValidationParameters = PresetValidationParameters(),
name='simple_rl_graph'):
super().__init__(name, schedule_params, vis_params)
self.agent_params = agent_params
self.env_params = env_params
self.preset_validation_params = preset_validation_params
self.agent_params.visualization = vis_params
if self.agent_params.input_filter is None:
if env_params is not None:
self.agent_params.input_filter = env_params.default_input_filter(
)
else:
# In cases where there is no environment (e.g. batch-rl and imitation learning), there is nowhere to get
# a default filter from. So using a default no-filter.
# When there is no environment, the user is expected to define input/output filters (if required) using
# the preset.
self.agent_params.input_filter = NoInputFilter()
if self.agent_params.output_filter is None:
if env_params is not None:
self.agent_params.output_filter = env_params.default_output_filter(
)
else:
self.agent_params.output_filter = NoOutputFilter()
def __init__(self, level=None):
super().__init__(level=level)
self.frame_skip = 1
self.default_input_filter = NoInputFilter()
self.default_output_filter = NoOutputFilter()
self.agents_params = None
self.non_trainable_agents = None
def __init__(self, level=None):
super().__init__(level=level)
self.frame_skip = 1
self.default_input_filter = NoInputFilter()
self.default_output_filter = NoOutputFilter()
self.agents_params = None
self.non_trainable_agents = None
self.run_phase_subject = None
self.enable_domain_randomization = False
def __init__(self, level=None):
super().__init__(level=level)
self.frame_skip = 1
self.default_input_filter = NoInputFilter()
self.default_output_filter = NoOutputFilter()
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
self.default_input_filter = AtariInputFilter
self.default_output_filter = AtariOutputFilter
gym_atari_envs = [
'air_raid', 'alien', 'amidar', 'assault', 'asterix', 'asteroids',
'atlantis', 'bank_heist', 'battle_zone', 'beam_rider', 'berzerk',
####################
# DQN Agent Params #
####################
agent_params = DDQNAgentParameters()
agent_params.network_wrappers['main'].learning_rate = 0.00025
agent_params.network_wrappers['main'].heads_parameters = [
DuelingQHeadParameters()
]
agent_params.memory.max_size = (MemoryGranularity.Transitions, 1000000)
agent_params.algorithm.discount = 0.99
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(4)
agent_params.exploration.epsilon_schedule = LinearSchedule(
1, 0.1, (N + 7) * 2000)
agent_params.input_filter = NoInputFilter()
agent_params.output_filter = NoOutputFilter()
###############
# Environment #
###############
env_params = GymEnvironmentParameters()
env_params.level = 'rl_coach.environments.toy_problems.exploration_chain:ExplorationChain'
env_params.additional_simulator_parameters = {
'chain_length': N,
'max_steps': N + 7
}
vis_params = VisualizationParameters()
# preset_validation_params = PresetValidationParameters()
# preset_validation_params.test = True
6: carla.WeatherParameters.HardRainNoon,
7: carla.WeatherParameters.SoftRainNoon,
8: carla.WeatherParameters.ClearSunset,
9: carla.WeatherParameters.CloudySunset,
10: carla.WeatherParameters.WetSunset,
11: carla.WeatherParameters.WetCloudySunset,
12: carla.WeatherParameters.MidRainSunset,
13: carla.WeatherParameters.HardRainSunset,
14: carla.WeatherParameters.SoftRainSunset
}
# Set up the input and output filters
# Input filters apply operations to the input space defined, several input filters can be defined. The order is executed sequentially
# Output filters apply operations on the output space defined.
CarlaInputFilter = NoInputFilter()
CarlaOutputFilter = NoOutputFilter()
# Enumerate observation sources
# New observation sources need to be appended here
class SensorTypes(Enum):
FRONT_CAMERA = "forward_camera"
LIDAR = "lidar"
BIRDEYE = "birdeye"
class CarlaEnvironmentParameters(EnvironmentParameters):
def __init__(self):
super().__init__()
self.host = 'localhost'
self.port = 2000
def __init__(self):
super().__init__()
self.default_input_filter = NoInputFilter()
self.default_output_filter = NoOutputFilter()
def __init__(self):
super().__init__()
self.random_initialization_steps = 0
self.max_over_num_frames = 1
self.additional_simulator_parameters = None
@property
def path(self):
return 'rl_coach.environments.gym_environment:GymEnvironment'
"""
Roboschool Environment Components
"""
RoboSchoolInputFilters = NoInputFilter()
RoboSchoolOutputFilters = NoOutputFilter()
class Roboschool(GymEnvironmentParameters):
def __init__(self):
super().__init__()
self.frame_skip = 1
self.default_input_filter = RoboSchoolInputFilters
self.default_output_filter = RoboSchoolOutputFilters
gym_roboschool_envs = [
'inverted_pendulum', 'inverted_pendulum_swingup',
'inverted_double_pendulum', 'reacher', 'hopper', 'walker2d',
'half_cheetah', 'ant', 'humanoid', 'humanoid_flagrun',
'humanoid_flagrun_harder', 'pong'
def __init__(self, level=None):
super().__init__(level=level)
self.observation_type = ObservationType.Measurements
self.default_input_filter = ControlSuiteInputFilter
self.default_output_filter = ControlSuiteOutputFilter
@property
def path(self):
return 'rl_coach.environments.control_suite_environment:ControlSuiteEnvironment'
"""
ControlSuite Environment Components
"""
ControlSuiteInputFilter = NoInputFilter()
ControlSuiteOutputFilter = NoOutputFilter()
control_suite_envs = {
':'.join(env): ':'.join(env)
for env in suite.BENCHMARKING
}
# Environment
class ControlSuiteEnvironment(Environment):
def __init__(
self,
level: LevelSelection,
frame_skip: int,
visualization_parameters: VisualizationParameters,
seed: Union[None, int] = None,
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()
# LabInputFilter = NoInputFilter()
LabInputFilter = InputFilter(is_a_reference_filter=True)
# LabInputFilter.add_reward_filter('clipping', RewardClippingFilter(-1.0, 1.0))
LabInputFilter.add_observation_filter(
'observation', 'rescaling',
ObservationRescaleToSizeFilter(
ImageObservationSpace(np.array([84, 84, 3]), high=255)))
LabInputFilter.add_observation_filter('observation', 'to_grayscale',
ObservationRGBToYFilter())
LabInputFilter.add_observation_filter('observation', 'to_uint8',
ObservationToUInt8Filter(0, 255))
# # stack last 4 images as s_t
LabInputFilter.add_observation_filter('observation', 'stacking',
ObservationStackingFilter(4))
## what will we get from agent
LabOutputFilter = NoOutputFilter()
"""
lab environment parameters
"""
class LabEnvironmentParameters(EnvironmentParameters):
def __init__(self,
level,
human_control=False,
random_initialization_steps=30,
rotation=20,
width=84,
height=84,
fps=60):
super().__init__(level=level)
