def __init__(self,
width,
height,
agent,
walls,
passengers,
goal_loc=None,
slip_prob=0,
gamma=0.99):
self.height = height
self.width = width
agent_obj = OOMDPObject(attributes=agent, name="agent")
wall_objs = self._make_oomdp_objs_from_list_of_dict(walls, "wall")
pass_objs = self._make_oomdp_objs_from_list_of_dict(
passengers, "passenger")
init_state = self._create_state(agent_obj, wall_objs, pass_objs)
self.goal_location = goal_loc
self.terminal_func = taxi_helpers.is_taxi_terminal_state if goal_loc is None else self._navigation_terminal_func
rf = self._taxi_reward_func if goal_loc is None else self._navigation_reward_func
OOMDP.__init__(self,
TaxiOOMDP.ACTIONS,
self._taxi_transition_func,
rf,
init_state=init_state,
gamma=gamma)
self.slip_prob = slip_prob
def reset(self):
'''
Summary:
Resets the OOMDP back to the initial configuration.
'''
self.time = self.init_time
OOMDP.reset(self)
def __init__(self,
panel,
date_time,
name_ext,
timestep=30,
panel_step=.1,
reflective_index=0.65,
latitude_deg=40.7,
longitude_deg=142.17,
img_dims=16,
mode_dict = {'dual_axis':True, 'image_mode':False, 'cloud_mode':False}):
# Error check the lat/long.
if abs(latitude_deg) > 90 or abs(longitude_deg) > 180:
print "Error: latitude must be between [-90, 90], longitude between [-180,180]. Lat:", latitude_deg, "Long:", longitude_deg
quit()
if mode_dict['cloud_mode'] and not mode_dict['image_mode']:
print "Warning (SolarOOMDP): Clouds were set to active but image mode is off. No cloud simulation supported for non-image-mode."
mode_dict['cloud_mode'] = False
# Mode information
if not(mode_dict['dual_axis']):
# If we are in 1-axis tracking mode, change actions accordingly.
SolarOOMDP.ACTIONS = self.get_single_axis_actions()
self.dual_axis = False
else:
self.dual_axis = True
# Image stuff.
self.img_dims = img_dims
self.image_mode = mode_dict['image_mode']
self.cloud_mode = mode_dict['cloud_mode']
self.clouds = self._generate_clouds() if mode_dict['cloud_mode'] else []
#get panel information.
self.panel = panel
self.sqrt_num_panels = 1 # Assume only 1 panel for now.
# Global information
self.latitude_deg = latitude_deg # positive in the northern hemisphere
self.longitude_deg = longitude_deg # negative reckoning west from prime meridian in Greenwich, England
self.panel_step = panel_step
self.timestep = timestep #timestep in minutes
self.reflective_index = reflective_index
self.name_ext = name_ext
# Time stuff.
self.init_time = date_time
self.time = date_time
# Make state and call super.
panels = self._get_default_panel_obj_list()
init_state = self._create_state(panels, self.init_time)
OOMDP.__init__(self, SolarOOMDP.ACTIONS, self._transition_func, self._reward_func, init_state=init_state)
def __init__(self, width, height, agent, walls, passengers, slip_prob=0, gamma=0.99):
self.height = height
self.width = width
agent_obj = OOMDPObject(attributes=agent, name="agent")
wall_objs = self._make_oomdp_objs_from_list_of_dict(walls, "wall")
pass_objs = self._make_oomdp_objs_from_list_of_dict(passengers, "passenger")
init_state = self._create_state(agent_obj, wall_objs, pass_objs)
OOMDP.__init__(self, TaxiOOMDP.ACTIONS, self._taxi_transition_func, self._taxi_reward_func, init_state=init_state, gamma=gamma)
self.slip_prob = slip_prob
def __init__(self, width, height, agent, walls, passengers, slip_prob=0, gamma=0.99):
self.height = height
self.width = width
agent_obj = OOMDPObject(attributes=agent, name="agent")
wall_objs = self._make_oomdp_objs_from_list_of_dict(walls, "wall")
pass_objs = self._make_oomdp_objs_from_list_of_dict(passengers, "passenger")
init_state = self._create_state(agent_obj, wall_objs, pass_objs)
OOMDP.__init__(self, TaxiOOMDP.ACTIONS, self._taxi_transition_func, self._taxi_reward_func, init_state=init_state, gamma=gamma)
self.slip_prob = slip_prob
def __init__(self, width, height, agent, walls, blocks, exit, gamma=0.99):
init_state = self._create_init_state(height, width, agent, walls,
blocks, exit)
OOMDP.__init__(self,
BlockDudeOOMDP.ACTIONS,
self.objects,
self._block_dude_transition_func,
self._block_dude_reward_func,
init_state=init_state,
gamma=gamma)
self.height = height
self.width = width
def __init__(self,
width,
height,
agent,
walls,
goals,
crumbs,
slip_prob=0,
gamma=0.99,
step_cost=0,
weights=None,
env_code=None,
sample_rate=5):
self.env_code = env_code
self.height = height
self.width = width
if weights is not None:
self.weights = weights
else:
# use true weighting over reward variables (on the goal with the passenger, on a toll, on a traffic cell)
self.weights = np.array([[2, -0.4, -0.5]])
# objects that belong in the state (changing)
agent_obj = OOMDPObject(attributes=agent, name="agent")
agent_exit = {"x": 100, "y": 100}
crumb_objs = self._make_oomdp_objs_from_list_of_dict(crumbs, "crumb")
crumb_objs_exit = self._make_oomdp_objs_from_list_of_dict(
crumbs, "crumb")
# objects that belong to the MDP (static)
wall_objs = self._make_oomdp_objs_from_list_of_dict(walls, "wall")
self.walls = wall_objs
goal_objs = self._make_oomdp_objs_from_list_of_dict(goals, "goal")
self.goals = goal_objs
self.slip_prob = slip_prob
init_state = self._create_state(agent_obj, crumb_objs)
self.exit_state = self._create_state(
OOMDPObject(attributes=agent_exit, name="agent_exit"),
crumb_objs_exit)
self.exit_state.set_terminal(True)
self.exit_state.set_goal(False)
OOMDP.__init__(self,
CookieCrumbOOMDP.ACTIONS,
self._cookie_crumb_transition_func,
self._cookie_crumb_reward_func,
init_state=init_state,
gamma=gamma,
step_cost=step_cost,
sample_rate=sample_rate)
def __init__(self,
width,
height,
agent,
walls,
goal,
skateboard,
slip_prob=0,
gamma=0.99,
step_cost=0,
weights=None,
env_code=None,
sample_rate=5):
self.env_code = env_code
self.height = height
self.width = width
if weights is not None:
self.weights = weights
else:
self.weights = np.array([[2, -0.4, -0.5]])
# objects that belong in the state (changing)
agent_obj = OOMDPObject(attributes=agent, name="agent")
agent_exit = {"x": 100, "y": 100}
skateboard_objs = self._make_oomdp_objs_from_list_of_dict(
skateboard, "skateboard")
skateboard_objs_exit = self._make_oomdp_objs_from_list_of_dict(
skateboard, "skateboard")
# objects that belong to the MDP (static)
wall_objs = self._make_oomdp_objs_from_list_of_dict(walls, "wall")
self.walls = wall_objs
self.goal = goal
self.slip_prob = slip_prob
init_state = self._create_state(agent_obj, skateboard_objs)
self.exit_state = self._create_state(
OOMDPObject(attributes=agent_exit, name="agent_exit"),
skateboard_objs_exit)
self.exit_state.set_terminal(True)
self.exit_state.set_goal(False)
OOMDP.__init__(self,
SkateboardOOMDP.ACTIONS,
self._skateboard_transition_func,
self._skateboard_reward_func,
init_state=init_state,
gamma=gamma,
step_cost=step_cost,
sample_rate=sample_rate)
def __init__(self,
width,
height,
agent,
walls,
passengers,
slip_prob=0,
gamma=0.99,
weights=None):
self.height = height
self.width = width
if weights is not None:
self.weights = weights
else:
# use true weighting over reward variables (on the goal with the passenger, on a toll, on a traffic cell)
self.weights = np.array([[10, 0, -1]])
agent_obj = OOMDPObject(attributes=agent, name="agent")
agent_exit = {"x": 100, "y": 100, "has_passenger": 0}
wall_objs = self._make_oomdp_objs_from_list_of_dict(walls, "wall")
pass_objs = self._make_oomdp_objs_from_list_of_dict(
passengers, "passenger")
pass_objs_exit = self._make_oomdp_objs_from_list_of_dict(
passengers, "passenger")
self.walls = wall_objs
self.exit_state = self._create_state(
OOMDPObject(attributes=agent_exit, name="agent_exit"),
pass_objs_exit)
self.exit_state.set_terminal(True)
self.exit_state.set_goal(False)
init_state = self._create_state(agent_obj, pass_objs)
OOMDP.__init__(self,
TaxiOOMDP.ACTIONS,
self._taxi_transition_func,
self._taxi_reward_func,
init_state=init_state,
gamma=gamma,
sample_rate=1)
self.slip_prob = slip_prob
def create_goal_state(cls, dest_color):
goal_agent = {'current_color': dest_color, 'has_passenger': 0}
goal_passengers = [{
'current_color': dest_color,
'dest_color': dest_color
}]
goal_agent_obj = OOMDPObject(attributes=goal_agent, name='agent')
goal_passenger_objs = OOMDP._make_oomdp_objs_from_list_of_dict(
goal_passengers, 'passenger')
return TaxiL1OOMDP._create_state(goal_agent_obj,
goal_passenger_objs,
is_terminal=True)
def __init__(self,
agent_color='red',
passenger_color='blue',
passenger_dest_color='green'):
agent = {'current_color': agent_color, 'has_passenger': 0}
passengers = [{
'current_color': passenger_color,
'dest_color': passenger_dest_color
}]
agent_obj = OOMDPObject(attributes=agent, name='agent')
passenger_objs = self._make_oomdp_objs_from_list_of_dict(
passengers, 'passenger')
init_state = TaxiL1OOMDP._create_state(agent_obj, passenger_objs)
self.goal_state = TaxiL1OOMDP.create_goal_state(passenger_dest_color)
self.terminal_func = lambda state: state == self.goal_state
OOMDP.__init__(self, TaxiL1OOMDP.ACTIONS, self._transition_func,
self._reward_func, init_state)
def __init__(
self,
date_time,
serial_loc='/dev/ttyACM0',
baud=9600,
use_img=True,
panel_step=0.1, #radians, used to specify command to arduino
timestep=30,
latitude_deg=41.82,
longitude_deg=-71.4128):
self.image_dims = 16 #use for downsampling image from camera
self.use_img = use_img
# Global information
self.latitude_deg = latitude_deg # positive in the northern hemisphere
self.longitude_deg = longitude_deg # negative reckoning west from prime meridian in Greenwich, England
self.timestep = timestep #timestep in minutes
self.panel_step = panel_step
#initialize communication with the arduino
self.ser = serial.Serial(serial_loc, baud, timeout=120)
self.ser.flushOutput()
self.ser.flushInput()
self.ser.write("INIT")
print "establishing communication with panel"
result = self.ser.readline(
) #result should take the form of RECV,<current angle>
if result.startswith("RECV"):
initial_angle_ns = float(result.split(",")[1])
print "connection established, initial angle along ns axis is {}".format(
initial_angle_ns)
else:
raise Exception(
"ERROR: invalid response from arduino: {}".format(result))
if self.use_img:
#initialize camera connection
self.cam = VideoCapture(0)
t.sleep(0.2) # wait for camera
#take image
start_pixels = self._capture_img()
else:
start_pixels = None
# Time stuff.
self.init_time = date_time
self.time = date_time
#create initial state
init_state = self._create_state(0, initial_angle_ns, start_pixels,
self.init_time)
OOMDP.__init__(self,
ArduinoOOMDP.ACTIONS,
self.objects,
self._transition_func,
self._reward_func,
init_state=init_state)
def __init__(self,
width,
height,
agent,
walls,
passengers,
tolls,
traffic,
fuel_stations,
slip_prob=0,
gamma=0.99,
step_cost=0,
weights=None,
env_code=None,
sample_rate=5):
self.env_code = env_code
self.height = height
self.width = width
if weights is not None:
self.weights = weights
else:
# use true weighting over reward variables (on the goal with the passenger, on a toll, on a traffic cell)
self.weights = np.array([[2, -0.4, -0.5]])
# objects that belong in the state (changing)
agent_obj = OOMDPObject(attributes=agent, name="agent")
agent_exit = {"x": 100, "y": 100, "has_passenger": 0}
pass_objs = self._make_oomdp_objs_from_list_of_dict(
passengers, "passenger")
pass_objs_exit = self._make_oomdp_objs_from_list_of_dict(
passengers, "passenger")
# objects that belong to the MDP (static)
wall_objs = self._make_oomdp_objs_from_list_of_dict(walls, "wall")
toll_objs = self._make_oomdp_objs_from_list_of_dict(tolls, "toll")
traffic_objs = self._make_oomdp_objs_from_list_of_dict(
traffic, "traffic")
fuel_station_objs = self._make_oomdp_objs_from_list_of_dict(
fuel_stations, "fuel_station")
self.tolls = toll_objs
self.traffic_cells = traffic_objs
self.walls = wall_objs
self.fuel_stations = fuel_station_objs
self.slip_prob = slip_prob
init_state = self._create_state(agent_obj, pass_objs)
self.exit_state = self._create_state(
OOMDPObject(attributes=agent_exit, name="agent_exit"),
pass_objs_exit)
self.exit_state.set_terminal(True)
self.exit_state.set_goal(False)
if init_state.track_fuel():
OOMDP.__init__(self,
AugmentedTaxiOOMDP.AUGMENTED_ACTIONS,
self._taxi_transition_func,
self._taxi_reward_func,
init_state=init_state,
gamma=gamma,
step_cost=step_cost,
sample_rate=sample_rate)
else:
OOMDP.__init__(self,
AugmentedTaxiOOMDP.BASE_ACTIONS,
self._taxi_transition_func,
self._taxi_reward_func,
init_state=init_state,
gamma=gamma,
step_cost=step_cost,
sample_rate=sample_rate)