def upload_step_metrics(self, metrics):
metrics[StepMetrics.EPISODE.value] = self._number_of_trials_
self._progress_ = metrics[StepMetrics.PROG.value]
self._episode_status = metrics[StepMetrics.EPISODE_STATUS.value]
if self._episode_status in self.reset_count_dict:
self.reset_count_dict[self._episode_status] += 1
StepMetrics.validate_dict(metrics)
sim_trace_log(metrics)
if self.is_save_simtrace_enabled:
write_simtrace_to_local_file(self._simtrace_local_path, metrics)
def upload_step_metrics(self, metrics):
self._progress_ = metrics[StepMetrics.PROG.value]
self._episode_status = metrics[StepMetrics.EPISODE_STATUS.value]
self._episode_reward_ += metrics[StepMetrics.REWARD.value]
if not self._is_eval_:
metrics[StepMetrics.EPISODE.value] = self._episode_
StepMetrics.validate_dict(metrics)
sim_trace_log(metrics)
if self.is_save_simtrace_enabled:
write_simtrace_to_local_file(self._simtrace_local_path,
metrics)
def upload_step_metrics(self, metrics):
self._progress_ = metrics[StepMetrics.PROG.value]
self._episode_status = metrics[StepMetrics.EPISODE_STATUS.value]
# Community fix to have reward for evaluation runs during training
self._episode_reward_ += metrics[StepMetrics.REWARD.value]
if not self._is_eval_:
metrics[StepMetrics.EPISODE.value] = self._episode_
StepMetrics.validate_dict(metrics)
sim_trace_log(metrics)
if self.is_save_simtrace_enabled:
write_simtrace_to_local_file(self._simtrace_local_path,
metrics)
self._update_mp4_video_metrics(metrics)
def upload_step_metrics(self, metrics):
metrics[StepMetrics.EPISODE.value] = self._number_of_trials_
self._progress_ = metrics[StepMetrics.PROG.value]
self._episode_status = metrics[StepMetrics.EPISODE_STATUS.value]
if self._episode_status in self.reset_count_dict:
self.reset_count_dict[self._episode_status] += 1
StepMetrics.validate_dict(metrics)
sim_trace_log(metrics)
if self.is_save_simtrace_enabled:
write_simtrace_to_local_file(
os.path.join(os.path.join(ITERATION_DATA_LOCAL_FILE_PATH, self._agent_name_),
IterationDataLocalFileNames.SIM_TRACE_EVALUATION_LOCAL_FILE.value),
metrics)
self._update_mp4_video_metrics(metrics)
def upload_step_metrics(self, metrics):
self._progress_ = metrics[StepMetrics.PROG.value]
self._episode_status = metrics[StepMetrics.EPISODE_STATUS.value]
# Community fix to have reward for evaluation runs during training
self._episode_reward_ += metrics[StepMetrics.REWARD.value]
if not self._is_eval_:
metrics[StepMetrics.EPISODE.value] = self._episode_
StepMetrics.validate_dict(metrics)
sim_trace_log(metrics)
if self.is_save_simtrace_enabled:
write_simtrace_to_local_file(
os.path.join(os.path.join(ITERATION_DATA_LOCAL_FILE_PATH, self._agent_name_),
IterationDataLocalFileNames.SIM_TRACE_TRAINING_LOCAL_FILE.value),
metrics)
self._update_mp4_video_metrics(metrics)
def upload_step_metrics(self, metrics):
self._progress_ = metrics[StepMetrics.PROG.value]
self._episode_status = metrics[StepMetrics.EPISODE_STATUS.value]
self._episode_reward_ += metrics[StepMetrics.REWARD.value]
#! TODO have this work with new sim trace class
if not self._is_eval_:
metrics[StepMetrics.EPISODE.value] = self._episode_
self._episode_reward_ += metrics[StepMetrics.REWARD.value]
StepMetrics.validate_dict(metrics)
sim_trace_log(metrics)
if self.is_save_simtrace_enabled:
write_simtrace_to_local_file(
os.path.join(
os.path.join(ITERATION_DATA_LOCAL_FILE_PATH,
self._agent_name_),
IterationDataLocalFileNames.
SIM_TRACE_TRAINING_LOCAL_FILE.value), metrics)
def __init__(self, config_dict, run_phase_sink, metrics):
'''agent_name - String containing the name of the agent
config_dict - Dictionary containing all the keys in ConfigParams
run_phase_sink - Sink to recieve notification of a change in run phase
'''
# reset rules manager
self._metrics = metrics
self._is_continuous = config_dict[const.ConfigParams.IS_CONTINUOUS.value]
self._is_reset = False
self._pause_count = 0
self._reset_rules_manager = construct_reset_rules_manager(config_dict)
self._ctrl_status = dict()
self._ctrl_status[AgentCtrlStatus.AGENT_PHASE.value] = AgentPhase.RUN.value
self._config_dict = config_dict
self._number_of_resets = config_dict[const.ConfigParams.NUMBER_OF_RESETS.value]
self._off_track_penalty = config_dict[const.ConfigParams.OFF_TRACK_PENALTY.value]
self._collision_penalty = config_dict[const.ConfigParams.COLLISION_PENALTY.value]
self._pause_end_time = 0.0
self._reset_count = 0
# simapp_version speed scale
self._speed_scale_factor_ = get_speed_factor(config_dict[const.ConfigParams.VERSION.value])
# Store the name of the agent used to set agents position on the track
self._agent_name_ = config_dict[const.ConfigParams.AGENT_NAME.value]
# Store the name of the links in the agent, this should be const
self._agent_link_name_list_ = config_dict[const.ConfigParams.LINK_NAME_LIST.value]
# Store the reward function
self._reward_ = config_dict[const.ConfigParams.REWARD.value]
self._track_data_ = TrackData.get_instance()
# Create publishers for controlling the car
self._velocity_pub_dict_ = OrderedDict()
self._steering_pub_dict_ = OrderedDict()
for topic in config_dict[const.ConfigParams.VELOCITY_LIST.value]:
self._velocity_pub_dict_[topic] = rospy.Publisher(topic, Float64, queue_size=1)
for topic in config_dict[const.ConfigParams.STEERING_LIST.value]:
self._steering_pub_dict_[topic] = rospy.Publisher(topic, Float64, queue_size=1)
#Create default reward parameters
self._reward_params_ = const.RewardParam.make_default_param()
#Creat the default metrics dictionary
self._step_metrics_ = StepMetrics.make_default_metric()
# State variable to track if the car direction has been reversed
self._reverse_dir_ = False
# Dictionary of bools indicating starting position behavior
self._start_pos_behavior_ = \
{'change_start' : config_dict[const.ConfigParams.CHANGE_START.value],
'alternate_dir' : config_dict[const.ConfigParams.ALT_DIR.value]}
# Dictionary to track the previous way points
self._prev_waypoints_ = {'prev_point' : Point(0, 0), 'prev_point_2' : Point(0, 0)}
# Dictionary containing some of the data for the agent
self._data_dict_ = {'max_progress': 0.0,
'current_progress': 0.0,
'prev_progress': 0.0,
'steps': 0.0,
'start_ndist': 0.0}
#Load the action space
self._action_space_, self._json_actions_ = \
load_action_space(config_dict[const.ConfigParams.ACTION_SPACE_PATH.value])
#! TODO evaluate if this is the best way to reset the car
rospy.wait_for_service(SET_MODEL_STATE)
rospy.wait_for_service(GET_MODEL_STATE)
self.set_model_state = ServiceProxyWrapper(SET_MODEL_STATE, SetModelState)
self.get_model_client = ServiceProxyWrapper(GET_MODEL_STATE, GetModelState)
# Adding the reward data publisher
self.reward_data_pub = RewardDataPublisher(self._agent_name_, self._json_actions_)
# init time
self.last_time = 0.0
self.curr_time = 0.0
# subscriber to time to update camera position
self.camera_manager = CameraManager.get_instance()
# True if the agent is in the training phase
self._is_training_ = False
rospy.Subscriber('/clock', Clock, self._update_sim_time)
# Register to the phase sink
run_phase_sink.register(self)
# Make sure velicty and angle are set to 0
send_action(self._velocity_pub_dict_, self._steering_pub_dict_, 0.0, 0.0)
start_pose = self._track_data_._center_line_.interpolate_pose(self._data_dict_['start_ndist'] * self._track_data_.get_track_length(),
reverse_dir=self._reverse_dir_,
finite_difference=FiniteDifference.FORWARD_DIFFERENCE)
self._track_data_.initialize_object(self._agent_name_, start_pose, ObstacleDimensions.BOT_CAR_DIMENSION)
self.car_model_state = self.get_model_client(self._agent_name_, '')
self._reset_agent(reset_pos=const.ResetPos.START_POS.value)
def __init__(self, config_dict, run_phase_sink, metrics):
'''config_dict (dict): containing all the keys in ConfigParams
run_phase_sink (RunPhaseSubject): Sink to receive notification of a change in run phase
metrics (EvalMetrics/TrainingMetrics): Training or evaluation metrics
'''
# reset rules manager
self._metrics = metrics
self._is_continuous = config_dict[
const.ConfigParams.IS_CONTINUOUS.value]
self._reset_rules_manager = construct_reset_rules_manager(config_dict)
self._ctrl_status = dict()
self._ctrl_status[
AgentCtrlStatus.AGENT_PHASE.value] = AgentPhase.RUN.value
self._config_dict = config_dict
self._done_condition = config_dict.get(
const.ConfigParams.DONE_CONDITION.value, any)
self._number_of_resets = config_dict[
const.ConfigParams.NUMBER_OF_RESETS.value]
self._off_track_penalty = config_dict[
const.ConfigParams.OFF_TRACK_PENALTY.value]
self._collision_penalty = config_dict[
const.ConfigParams.COLLISION_PENALTY.value]
self._pause_duration = 0.0
self._reset_count = 0
self._curr_crashed_object_name = ''
# simapp_version speed scale
self._speed_scale_factor_ = get_speed_factor(
config_dict[const.ConfigParams.VERSION.value])
# Store the name of the agent used to set agents position on the track
self._agent_name_ = config_dict[const.ConfigParams.AGENT_NAME.value]
# Set start lane. This only support for two agents H2H race
self._agent_idx_ = get_racecar_idx(self._agent_name_)
# Get track data
self._track_data_ = TrackData.get_instance()
if self._agent_idx_ is not None:
self._start_lane_ = self._track_data_.inner_lane \
if self._agent_idx_ % 2 else self._track_data_.outer_lane
else:
self._start_lane_ = self._track_data_.center_line
# Store the name of the links in the agent, this should be const
self._agent_link_name_list_ = config_dict[
const.ConfigParams.LINK_NAME_LIST.value]
# Store the reward function
self._reward_ = config_dict[const.ConfigParams.REWARD.value]
# Create publishers for controlling the car
self._velocity_pub_dict_ = OrderedDict()
self._steering_pub_dict_ = OrderedDict()
for topic in config_dict[const.ConfigParams.VELOCITY_LIST.value]:
self._velocity_pub_dict_[topic] = rospy.Publisher(topic,
Float64,
queue_size=1)
for topic in config_dict[const.ConfigParams.STEERING_LIST.value]:
self._steering_pub_dict_[topic] = rospy.Publisher(topic,
Float64,
queue_size=1)
#Create default reward parameters
self._reward_params_ = const.RewardParam.make_default_param()
#Create the default metrics dictionary
self._step_metrics_ = StepMetrics.make_default_metric()
# Dictionary of bools indicating starting position behavior
self._start_pos_behavior_ = \
{'change_start' : config_dict[const.ConfigParams.CHANGE_START.value],
'alternate_dir' : config_dict[const.ConfigParams.ALT_DIR.value]}
# Dictionary to track the previous way points
self._prev_waypoints_ = {
'prev_point': Point(0, 0),
'prev_point_2': Point(0, 0)
}
# Normalized distance of new start line from the original start line of the track.
start_ndist = 0.0
# Normalized start position offset w.r.t to start_ndist, which is the start line of the track.
start_pos_offset = config_dict.get(
const.ConfigParams.START_POSITION.value, 0.0)
self._start_line_ndist_offset = start_pos_offset / self._track_data_.get_track_length(
)
# Dictionary containing some of the data for the agent
# - During the reset call, every value except start_ndist will get wiped out by self._clear_data
# (reset happens prior to every episodes begin)
# - If self._start_line_ndist_offset is not 0 (usually some minus value),
# then initial current_progress suppose to be non-zero (usually some minus value) as progress
# suppose to be based on start_ndist.
# - This will be correctly calculated by first call of utils.compute_current_prog function.
# As prev_progress will be initially 0.0 and physical position is not at start_ndist,
# utils.compute_current_prog will return negative progress if self._start_line_ndist_offset is negative value
# (meaning behind start line) and will return positive progress if self._start_line_ndist_offset is
# positive value (meaning ahead of start line).
self._data_dict_ = {
'max_progress': 0.0,
'current_progress': 0.0,
'prev_progress': 0.0,
'steps': 0.0,
'start_ndist': start_ndist,
'prev_car_pose': 0.0
}
#Load the action space
self._action_space_, self._json_actions_ = \
load_action_space(config_dict[const.ConfigParams.ACTION_SPACE_PATH.value])
#! TODO evaluate if this is the best way to reset the car
# Adding the reward data publisher
self.reward_data_pub = RewardDataPublisher(self._agent_name_,
self._json_actions_)
# subscriber to time to update camera position
self.camera_manager = CameraManager.get_instance()
# True if the agent is in the training phase
self._is_training_ = False
# Register to the phase sink
run_phase_sink.register(self)
# Make sure velocity and angle are set to 0
send_action(self._velocity_pub_dict_, self._steering_pub_dict_, 0.0,
0.0)
# start_dist should be hypothetical start line (start_ndist) plus
# start position offset (start_line_ndist_offset).
start_pose = self._start_lane_.interpolate_pose(
(self._data_dict_['start_ndist'] + self._start_line_ndist_offset) *
self._track_data_.get_track_length(),
finite_difference=FiniteDifference.FORWARD_DIFFERENCE)
self._track_data_.initialize_object(self._agent_name_, start_pose, \
ObstacleDimensions.BOT_CAR_DIMENSION)
self.make_link_points = lambda link_state: Point(
link_state.pose.position.x, link_state.pose.position.y)
self.reference_frames = ['' for _ in self._agent_link_name_list_]
self._pause_car_model_pose = None
self._park_position = DEFAULT_PARK_POSITION
AbstractTracker.__init__(self, TrackerPriority.HIGH)
def set_reward_and_metrics(reward_params, step_metrics, pos_dict, track_data,
next_index, prev_index, action, json_actions):
'''Populates the reward_params and step_metrics dictionaries with the common
metrics and parameters.
reward_params - Dictionary containing the input parameters to the reward function
step_metrics - Dictionary containing the metrics that are sent to s3
pos_dict - Dictionary containing the agent position data, keys defined in AgentPos
track_data - Object containing all the track information and geometry
next_index - The index of the next way point
prev_index - The index of the previous way point
action - Integer containing the action to take
json_actions - Dictionary that maps action into steering and angle
'''
try:
# Check that the required keys are present in the dicts that are being
# passed in, these methods will throw an exception if a key is missing
RewardParam.validate_dict(reward_params)
StepMetrics.validate_dict(step_metrics)
model_point = pos_dict[AgentPos.POINT.value]
# Geat the nearest points
nearest_pnts_dict = track_data.get_nearest_points(model_point)
# Compute distance from center and road width
nearest_dist_dict = track_data.get_nearest_dist(
nearest_pnts_dict, model_point)
# Compute the distance from the previous and next points
distance_from_prev, distance_from_next = \
track_data.get_distance_from_next_and_prev(model_point, prev_index,
next_index)
# Compute which points are on the track
wheel_on_track = track_data.points_on_track(
pos_dict[AgentPos.LINK_POINTS.value])
# Get the model orientation
model_orientation = pos_dict[AgentPos.ORIENTATION.value]
# Set the reward and metric parameters
reward_params[RewardParam.CENTER_DIST.value[0]] = \
nearest_dist_dict[TrackNearDist.NEAR_DIST_CENT.value]
reward_params[RewardParam.CLS_WAYPNY.value[0]] = [
prev_index, next_index
]
reward_params[RewardParam.LEFT_CENT.value[0]] = \
nearest_dist_dict[TrackNearDist.NEAR_DIST_IN.value] < \
nearest_dist_dict[TrackNearDist.NEAR_DIST_OUT.value]
reward_params[RewardParam.WAYPNTS.value[0]] = track_data.get_way_pnts()
reward_params[RewardParam.TRACK_WIDTH.value[0]] = \
nearest_pnts_dict[TrackNearPnts.NEAR_PNT_IN.value] \
.distance(nearest_pnts_dict[TrackNearPnts.NEAR_PNT_OUT.value])
reward_params[
RewardParam.TRACK_LEN.value[0]] = track_data.get_track_length()
step_metrics[StepMetrics.X.value] = \
reward_params[RewardParam.X.value[0]] = model_point.x
step_metrics[StepMetrics.Y.value] = \
reward_params[RewardParam.Y.value[0]] = model_point.y
step_metrics[StepMetrics.YAW.value] = \
reward_params[RewardParam.HEADING.value[0]] = \
Rotation.from_quat(model_orientation).as_euler('zyx')[0] * 180.0 / math.pi
step_metrics[StepMetrics.CLS_WAYPNT.value] = \
next_index if distance_from_next < distance_from_prev else prev_index
step_metrics[
StepMetrics.TRACK_LEN.value] = track_data.get_track_length()
step_metrics[StepMetrics.STEER.value] = \
reward_params[RewardParam.STEER.value[0]] = \
float(json_actions[action]['steering_angle'])
step_metrics[StepMetrics.THROTTLE.value] = \
reward_params[RewardParam.SPEED.value[0]] = \
float(json_actions[action]['speed'])
step_metrics[StepMetrics.WHEELS_TRACK.value] = \
reward_params[RewardParam.WHEELS_ON_TRACK.value[0]] = all(wheel_on_track)
step_metrics[StepMetrics.ACTION.value] = action
except KeyError as ex:
raise GenericRolloutException("Key {}, not found".format(ex))
except Exception as ex:
raise GenericRolloutException(
'Cannot compute reward and metrics: {}'.format(ex))
def __init__(self, config_dict, run_phase_sink, metrics):
'''agent_name - String containing the name of the agent
config_dict - Dictionary containing all the keys in ConfigParams
run_phase_sink - Sink to recieve notification of a change in run phase
'''
# reset rules manager
self._metrics = metrics
self._is_continuous = config_dict[
const.ConfigParams.IS_CONTINUOUS.value]
self._is_reset = False
self._reset_rules_manager = construct_reset_rules_manager(config_dict)
self._ctrl_status = dict()
self._ctrl_status[
AgentCtrlStatus.AGENT_PHASE.value] = AgentPhase.RUN.value
self._config_dict = config_dict
self._done_condition = config_dict.get(
const.ConfigParams.DONE_CONDITION.value, any)
self._number_of_resets = config_dict[
const.ConfigParams.NUMBER_OF_RESETS.value]
self._off_track_penalty = config_dict[
const.ConfigParams.OFF_TRACK_PENALTY.value]
self._collision_penalty = config_dict[
const.ConfigParams.COLLISION_PENALTY.value]
self._pause_duration = 0.0
self._reset_count = 0
self._curr_crashed_object_name = None
self._last_crashed_object_name = None
# simapp_version speed scale
self._speed_scale_factor_ = get_speed_factor(
config_dict[const.ConfigParams.VERSION.value])
# Store the name of the agent used to set agents position on the track
self._agent_name_ = config_dict[const.ConfigParams.AGENT_NAME.value]
# Set start lane. This only support for two agents H2H race
self._agent_idx_ = get_racecar_idx(self._agent_name_)
# Get track data
self._track_data_ = TrackData.get_instance()
if self._agent_idx_ is not None:
self._start_lane_ = self._track_data_.inner_lane \
if self._agent_idx_ % 2 else self._track_data_.outer_lane
else:
self._start_lane_ = self._track_data_.center_line
# Store the name of the links in the agent, this should be const
self._agent_link_name_list_ = config_dict[
const.ConfigParams.LINK_NAME_LIST.value]
# Store the reward function
self._reward_ = config_dict[const.ConfigParams.REWARD.value]
# Create publishers for controlling the car
self._velocity_pub_dict_ = OrderedDict()
self._steering_pub_dict_ = OrderedDict()
for topic in config_dict[const.ConfigParams.VELOCITY_LIST.value]:
self._velocity_pub_dict_[topic] = rospy.Publisher(topic,
Float64,
queue_size=1)
for topic in config_dict[const.ConfigParams.STEERING_LIST.value]:
self._steering_pub_dict_[topic] = rospy.Publisher(topic,
Float64,
queue_size=1)
#Create default reward parameters
self._reward_params_ = const.RewardParam.make_default_param()
#Creat the default metrics dictionary
self._step_metrics_ = StepMetrics.make_default_metric()
# Dictionary of bools indicating starting position behavior
self._start_pos_behavior_ = \
{'change_start' : config_dict[const.ConfigParams.CHANGE_START.value],
'alternate_dir' : config_dict[const.ConfigParams.ALT_DIR.value]}
# Dictionary to track the previous way points
self._prev_waypoints_ = {
'prev_point': Point(0, 0),
'prev_point_2': Point(0, 0)
}
# Normalize start position in meter to normalized start_ndist in percentage
start_ndist = config_dict.get(const.ConfigParams.START_POSITION.value, 0.0) / \
self._track_data_.get_track_length()
# Dictionary containing some of the data for the agent
self._data_dict_ = {
'max_progress': 0.0,
'current_progress': 0.0,
'prev_progress': 0.0,
'steps': 0.0,
'start_ndist': start_ndist
}
#Load the action space
self._action_space_, self._json_actions_ = \
load_action_space(config_dict[const.ConfigParams.ACTION_SPACE_PATH.value])
#! TODO evaluate if this is the best way to reset the car
# Adding the reward data publisher
self.reward_data_pub = RewardDataPublisher(self._agent_name_,
self._json_actions_)
# subscriber to time to update camera position
self.camera_manager = CameraManager.get_instance()
# True if the agent is in the training phase
self._is_training_ = False
# Register to the phase sink
run_phase_sink.register(self)
# Make sure velicty and angle are set to 0
send_action(self._velocity_pub_dict_, self._steering_pub_dict_, 0.0,
0.0)
start_pose = self._track_data_.center_line.interpolate_pose(
self._data_dict_['start_ndist'] *
self._track_data_.get_track_length(),
finite_difference=FiniteDifference.FORWARD_DIFFERENCE)
self._track_data_.initialize_object(self._agent_name_, start_pose, \
ObstacleDimensions.BOT_CAR_DIMENSION)
self.make_link_points = lambda link_state: Point(
link_state.pose.position.x, link_state.pose.position.y)
self.reference_frames = ['' for _ in self._agent_link_name_list_]
self._pause_car_model_pose = None
self._park_position = DEFAULT_PARK_POSITION
AbstractTracker.__init__(self, TrackerPriority.HIGH)
def __init__(self, config_dict):
'''agent_name - String containing the name of the agent
config_dict - Dictionary containing all the keys in ConfigParams
'''
# simapp_version speed scale
self._speed_scale_factor_ = get_speed_factor(
config_dict[const.ConfigParams.VERSION.value])
# Store the name of the agent used to set agents position on the track
self._agent_name_ = config_dict[const.ConfigParams.AGENT_NAME.value]
# Store the name of the links in the agent, this should be const
self._agent_link_name_list_ = config_dict[
const.ConfigParams.LINK_NAME_LIST.value]
# Store the reward function
self._reward_ = config_dict[const.ConfigParams.REWARD.value]
self._track_data_ = TrackData.get_instance()
# Create publishers for controlling the car
self._velocity_pub_dict_ = OrderedDict()
self._steering_pub_dict_ = OrderedDict()
for topic in config_dict[const.ConfigParams.VELOCITY_LIST.value]:
self._velocity_pub_dict_[topic] = rospy.Publisher(topic,
Float64,
queue_size=1)
for topic in config_dict[const.ConfigParams.STEERING_LIST.value]:
self._steering_pub_dict_[topic] = rospy.Publisher(topic,
Float64,
queue_size=1)
#Create default reward parameters
self._reward_params_ = const.RewardParam.make_default_param()
#Creat the default metrics dictionary
self._step_metrics_ = StepMetrics.make_default_metric()
# State variable to track if the car direction has been reversed
self._reverse_dir_ = False
# Dictionary of bools indicating starting position behavior
self._start_pos_behavior_ = \
{'change_start' : config_dict[const.ConfigParams.CHANGE_START.value],
'alternate_dir' : config_dict[const.ConfigParams.ALT_DIR.value]}
# Dictionary to track the previous way points
self._prev_waypoints_ = {
'prev_point': Point(0, 0),
'prev_point_2': Point(0, 0)
}
# Dictionary containing some of the data for the agent
self._data_dict_ = {
'prev_progress': 0.0,
'steps': 0.0,
'start_ndist': 0.0
}
#Load the action space
self._action_space_, self._json_actions_ = \
load_action_space(config_dict[const.ConfigParams.ACTION_SPACE_PATH.value])
#! TODO evaluate if this is the best way to reset the car
rospy.wait_for_service(SET_MODEL_STATE)
rospy.wait_for_service(GET_MODEL_STATE)
self.set_model_state = ServiceProxyWrapper(SET_MODEL_STATE,
SetModelState)
self.get_model_client = ServiceProxyWrapper(GET_MODEL_STATE,
GetModelState)
# Adding the reward data publisher
self.reward_data_pub = RewardDataPublisher(self._agent_name_,
self._json_actions_)
# init time
self.last_time = 0.0
self.curr_time = 0.0
# subscriber to time to update camera position
camera_types = [camera for camera in CameraType]
self.camera_manager = CameraManager(camera_types=camera_types)
rospy.Subscriber('/clock', Clock, self.update_camera)
# Make sure velicty and angle are set to 0
send_action(self._velocity_pub_dict_, self._steering_pub_dict_, 0.0,
0.0)