class BlueSkyEnv2(gym.Env):
def __init__(self):
"""
# Initialize client server interface
# NodeID: is required for each individual environment to connect to its respective simulation node, when
# running a single simulation this argument is not required.
# n_cpu: total amount of nodes that have to be initialized.
# NOTE: N_cpu and len(NodeID) have to be the same
# scenfile: The specific scenario file this environment is going to run.
# TODO:
-Use bs.settings for ports and plugin check.
-Starting of server integration (Now the server has to separately be started by using bluesky.py --headless
"""
global recdataflag, recdata
recdataflag = False
recdata = None
self.connected = False
self.scenfile = './synthetics/super/super2.scn'
self.min_hdg = 0 # [deg]
self.max_hdg = 360 # [deg]
self.min_lat = -1 # [lat/lon]
self.max_lat = 1 # [lat/lon]
self.min_lon = -1 # [lat/lon]
self.max_lon = 1 # [lat/lon]
self.min_dist_waypoint = 5
self.max_dist_waypoint = 125
# Define observation bounds and normalize so that all values range between -1 and 1 or 0 and 1,
# normalization improves neural networks ability to converge
self.low_obs = np.array(
[self.min_lat, self.min_lon, self.min_hdg, self.min_dist_waypoint])
self.high_obs = np.array(
[self.max_lat, self.max_lon, self.max_hdg, self.max_dist_waypoint])
# observation is a array of shape (4,) [latitude,longitude,hdg,dist_plane,dist_waypoint]
self.observation_space = spaces.Box(low=self.low_obs,
high=self.high_obs,
dtype=np.float32)
# Action space is normalized heading, shape (1,)
self.action_space = spaces.Box(low=-1,
high=1,
shape=(1, ),
dtype=np.float32)
def reset(self):
"""
Reset the environment to initial state
recdata is a dict that contains requested simulation data from Bluesky. Can be changed in plugin MLCONTROL.
"""
global recdataflag
self.ep = 0
if not self.connected:
self.NodeID = 0
self.myclient = Client()
self.myclient.connect(event_port=52000, stream_port=52001)
self.myclient.receive(1000)
self.myclient.event_received.connect(on_event)
self.myclient.actnode(self.myclient.servers[self.myclient.host_id]
['nodes'][self.NodeID])
self.connected = True
#Initialize env.
str_to_send = 'IC ' + self.scenfile + '; MLSTEP'
recdataflag = False
self.myclient.send_event(b'STACKCMD', str_to_send)
while not recdataflag:
self.myclient.receive(1000)
dist_wpt = tools.geo.kwikdist(recdata['lat'][0], recdata['lon'][0],
recdata['actwplat'][0],
recdata['actwplon'][0])
self.state = np.array([
recdata['lat'][0], recdata['lon'][0],
normalizer(recdata['hdg'][0], 'HDGToNorm', self.min_hdg,
self.max_hdg),
normalizer(dist_wpt, 'DistToNorm', self.min_dist_waypoint,
self.max_dist_waypoint)
])
# Reset data flag when data has been processed.
recdataflag = False
return self.state
def step(self, action):
"""
This function interacts with the Bluesky simulation node/server, and gives a action command to the environment
and returns state(t + step)
Reward accumulation is done outside this step function, so this function returns the reward gained during
this simulation step.
"""
# Initialize step parameters
global recdataflag
done = False
reward = 0
self.ep = self.ep + 1
# Loop over every agent
action_tot = normalizer(action[0], 'NormToHDG', self.min_hdg,
self.max_hdg)
self.myclient.send_event(
b'STACKCMD',
'SUP0 HDG ' + np.array2string(action_tot) + '; MLSTEP')
# Wait for server to respond
while not recdataflag:
self.myclient.receive(1000)
# Do some intermediate state update calculations.
dist_wpt = tools.geo.kwikdist(recdata['lat'][0], recdata['lon'][0],
recdata['actwplat'][0],
recdata['actwplon'][0])
# Assign rewards if goals states are met or any other arbitary rewards.
if dist_wpt <= self.min_dist_waypoint:
reward = reward + 10
done = True
if self.ep >= 500:
done = True
#Penalty for every timestep
reward = reward - 0.1
# Update state to state(t + step)
self.state = np.array([
recdata['lat'][0], recdata['lon'][0],
normalizer(recdata['hdg'][0], 'HDGToNorm', self.min_hdg,
self.max_hdg),
normalizer(dist_wpt, 'DistToNorm', self.min_dist_waypoint,
self.max_dist_waypoint)
])
if done is True:
self.myclient.send_event(b'STACKCMD', 'HOLD')
# Reset data flag when data has been processed.
recdataflag = False
return self.state, reward, done, {}
def render(self, mode='human'):
"""
Obsolete rendering platform, for debugging purposes.
To render simulation, start bluesky --client and connect to running server.
"""
return
def close(self):
if self.viewer:
self.viewer.close()
self.viewer = None
class BlueSkyEnv(gym.Env):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': 50
}
def __init__(self, **kwargs):
"""
# Initialize client server interface
# NodeID: is required for each individual environment to connect to its respective simulation node, when
# running a single simulation this argument is not required.
# n_cpu: total amount of nodes that have to be initialized.
# NOTE: N_cpu and len(NodeID) have to be the same
# scenfile: The specific scenario file this environment is going to run.
# TODO:
-Use bs.settings for ports and plugin check.
-Starting of server integration (Now the server has to separately be started by using bluesky.py --headless
"""
global recdataflag, recdata
recdataflag = False
recdata = None
self.NodeID = kwargs['NodeID']
self.n_cpu = kwargs['n_cpu']
self.cfgfile = ''
self.scenfile = kwargs['scenfile']
if self.scenfile is None:
self.scenfile = './synthetics/super/super3.scn'
if self.NodeID != 0 and self.n_cpu >= 1:
# print('Client {0:2d} waiting for node to initialize'.format(self.NodeID))
time.sleep(5)
self.myclient = Client()
self.myclient.connect(event_port=52000, stream_port=52001)
self.myclient.receive(1000)
self.myclient.actnode(
self.myclient.servers[self.myclient.host_id]['nodes'][self.NodeID])
self.myclient.event_received.connect(on_event)
if self.NodeID == 0 and self.n_cpu >= 1:
str_to_send = 'addnodes ' + str(self.n_cpu - 1)
# print(str_to_send)
self.myclient.send_event(b'STACKCMD', str_to_send)
print('Initializing {0:2d} nodes'.format(self.n_cpu))
time.sleep(5)
# Set constants for environment
self.nm = 1852 # Nautical miles [nm] in meter [m]
self.ep = 0
self.los = 5 * 1.05 # [nm] Horizontal separation minimum for resolution
self.wpt_reached = 5 # [nm]
self.min_hdg = 0 # [deg]
self.max_hdg = 360 # [deg]
self.min_lat = -1 # [lat/lon]
self.max_lat = 1 # [lat/lon]
self.min_lon = -1 # [lat/lon]
self.max_lon = 1 # [lat/lon]
self.min_dist_plane = self.los * 0.95
self.max_dist_plane = 125
self.min_dist_waypoint = self.wpt_reached * 0.95
self.max_dist_waypoint = 125
# TODO:State definitions and other state information still has to be formalized.
self.state = None
self.state_object = None
# Define observation bounds and normalize so that all values range between -1 and 1 or 0 and 1,
# normalization improves neural networks ability to converge
self.low_obs = np.array([
self.min_lat, self.min_lon,
normalizer(self.min_hdg, 'HDGToNorm', self.min_hdg, self.max_hdg),
normalizer(self.min_dist_plane, 'DistToNorm', self.min_dist_plane,
self.max_dist_plane),
normalizer(self.min_dist_waypoint, 'DistToNorm',
self.min_dist_waypoint, self.max_dist_waypoint)
])
self.high_obs = np.array([
self.max_lat, self.max_lon,
normalizer(self.max_hdg, 'HDGToNorm', self.min_hdg, self.max_hdg),
normalizer(self.max_dist_plane, 'DistToNorm', self.min_dist_plane,
self.max_dist_plane),
normalizer(self.max_dist_waypoint, 'DistToNorm',
self.min_dist_waypoint, self.max_dist_waypoint)
])
self.viewer = None
# observation is a array of shape (5,) [latitude,longitude,hdg,dist_plane,dist_waypoint]
self.observation_space = spaces.Box(low=self.low_obs,
high=self.high_obs,
dtype=np.float32)
# Action space is normalized heading, shape (1,)
self.action_space = spaces.Box(low=-1,
high=1,
shape=(1, ),
dtype=np.float32)
def reset(self):
"""
Reset the environment to initial state
recdata is a dict that contains requested simulation data from Bluesky. Can be changed in plugin MLCONTROL.
"""
global recdataflag
str_to_send = 'IC ' + self.scenfile + '; MLSTEP'
recdataflag = False
self.myclient.send_event(b'STACKCMD', str_to_send)
while not recdataflag:
self.myclient.receive(1000)
# Calculate distance to plane and waypoint. This method will change when more research is done into state def.
dist_plane = tools.geo.kwikdist(recdata['lat'][0], recdata['lon'][0],
recdata['lat'][1], recdata['lon'][1])
dist_wpt = tools.geo.kwikdist(recdata['lat'][0], recdata['lon'][0],
recdata['actwplat'][0],
recdata['actwplon'][0])
# Set state to initial state.
self.state = np.array([
recdata['lat'][0], recdata['lon'][0],
normalizer(recdata['hdg'][0], 'HDGToNorm', self.min_hdg,
self.max_hdg),
normalizer(dist_plane, 'DistToNorm', self.min_dist_plane,
self.max_dist_plane),
normalizer(dist_wpt, 'DistToNorm', self.min_dist_waypoint,
self.max_dist_waypoint)
])
self.state_object = np.array(
[recdata['lat'][1], recdata['lon'][1], recdata['hdg'][1]])
self.ep = 0
# Reset data flag when data has been processed.
recdataflag = False
return self.state
def step(self, action):
"""
This function interacts with the Bluesky simulation node/server, and gives a action command to the environment
and returns state(t + step)
Reward accumulation is done outside this step function, so this function returns the reward gained during
this simulation step.
"""
# Initialize step parameters
global recdataflag
reward = 0
self.ep = self.ep + 1
done = False
# Forward action and let bluesky run a simulation step.
# Normalize action to be between 0 and 1
action_tot = normalizer(action[0], 'NormToHDG', self.min_hdg,
self.max_hdg)
self.myclient.send_event(
b'STACKCMD',
'HDG SUP0 ' + np.array2string(action_tot) + '; MLSTEP')
# Wait for server to respond
while not recdataflag:
self.myclient.receive(1000)
# Do some intermediate state update calculations.
dist_plane = tools.geo.kwikdist(recdata['lat'][0], recdata['lon'][0],
recdata['lat'][1], recdata['lon'][1])
dist_wpt = tools.geo.kwikdist(recdata['lat'][0], recdata['lon'][0],
recdata['actwplat'][0],
recdata['actwplon'][0])
# Assign rewards if goals states are met or any other arbitary rewards.
if dist_plane <= self.los:
reward = reward - 100
done = True
if dist_wpt <= self.wpt_reached:
reward = reward + 100
done = True
if self.ep >= 500:
done = True
reward = reward + 3 / dist_wpt
# Update state to state(t + step)
self.state = np.array([
recdata['lat'][0], recdata['lon'][0],
normalizer(recdata['hdg'][0], 'HDGToNorm', self.min_hdg,
self.max_hdg),
normalizer(dist_plane, 'DistToNorm', self.min_dist_plane,
self.max_dist_plane),
normalizer(dist_wpt, 'DistToNorm', self.min_dist_waypoint,
self.max_dist_waypoint)
])
self.state_object = np.array(
[recdata['lat'][1], recdata['lon'][1], recdata['hdg'][1]])
# Check if state is a terminal state, then stop simulation.
if done is True:
self.myclient.send_event(b'STACKCMD', 'HOLD')
# Reset data flag when data has been processed.
recdataflag = False
return self.state, reward, done, {}
def render(self, mode='human'):
"""
Obsolete rendering platform, for debugging purposes.
To render simulation, start bluesky --client and connect to running server.
"""
screen_width = 600
screen_height = 600
scale = screen_width / 120
latplane = self.state[0]
lonplane = self.state[1]
latplane2 = self.state_object[0]
lonplane2 = self.state_object[1]
length_plane = 1 * scale
if self.viewer is None:
#set start position
from gym.envs.classic_control import rendering
self.viewer = rendering.Viewer(screen_width, screen_height)
plane = rendering.FilledPolygon([(-length_plane, -length_plane),
(0, length_plane),
(length_plane, -length_plane)
]) # NOSEUP
x_plane, y_plane = latlon_to_screen(-1, -1, latplane, lonplane,
scale)
plane.add_attr(rendering.Transform(translation=(x_plane, y_plane)))
self.planetrans = rendering.Transform()
plane.add_attr(self.planetrans)
plane.set_color(0, 1, 0)
self.viewer.add_geom(plane)
plane2 = rendering.FilledPolygon([(-length_plane, -length_plane),
(0, length_plane),
(length_plane, -length_plane)
]) # NOSEUP
x_plane2, y_plane2 = latlon_to_screen(-1, -1, latplane2, lonplane2,
scale)
plane2.add_attr(
rendering.Transform(translation=(x_plane2, y_plane2)))
self.planetrans2 = rendering.Transform()
plane2.add_attr(self.planetrans2)
plane.set_color(1, 0, 0)
self.viewer.add_geom(plane2)
waypoint = rendering.make_circle(3)
x_waypoint, y_waypoint = latlon_to_screen(-1, -1,
bs.traf.actwp.lat[0],
bs.traf.actwp.lon[0],
scale)
waypoint.add_attr(
rendering.Transform(translation=(x_waypoint, y_waypoint)))
self.viewer.add_geom(waypoint)
self.latplane_init = latplane
self.lonplane_init = lonplane
self.latplane2_init = latplane2
self.lonplane2_init = lonplane2
x_screen_dx, y_screen_dy = dlatlon_to_screen(self.latplane_init,
self.lonplane_init,
latplane, lonplane, scale)
self.planetrans.set_translation(x_screen_dx, y_screen_dy)
# self.planetrans.set_rotation(1)
x_screen_dx2, y_screen_dy2 = dlatlon_to_screen(self.latplane2_init,
self.lonplane2_init,
latplane2, lonplane2,
scale)
self.planetrans2.set_translation(x_screen_dx2, y_screen_dy2)
# self.planetrans2.set_rotation(self.state_object[2]*0.0174532925)
return self.viewer.render(return_rgb_array=mode == 'rgb_array')
def close(self):
if self.viewer:
self.viewer.close()
self.viewer = None
def __init__(self, **kwargs):
def on_event(eventname, eventdata, sender_id):
print(eventdata)
print(sender_id)
def on_stream(streamname, data, sender_id):
print('Stream data received:', streamname)
self.received_data = True
self.data = data
## Enable client server interface
self.NodeID = kwargs['NodeID']
self.received_data = False
myclient = Client()
myclient.connect(event_port=11000, stream_port=11001)
myclient.receive()
myclient.actnode(
myclient.servers[myclient.host_id]['nodes'][self.NodeID])
myclient.subscribe(b'ACDATA')
myclient.stream_received.connect(on_stream)
myclient.event_received.connect(on_event)
while not self.received_data:
myclient.receive()
self.nm = 1852
self.ep = 0
self.los = 5 * 1.05 # [nm] Horizontal separation minimum for resolution
self.wpt_reached = 5
# observation is a array of 4 collums [latitude,longitude,hdg,dist_plane,dist_waypoint] Real values, so not yet normalized
self.min_hdg = 0 #0
self.max_hdg = 360 #360
self.min_lat = -1
self.max_lat = 1
self.min_lon = -1
self.max_lon = 1
# max values based upon size of latlon box
self.min_dist_plane = self.los * 0.95
self.max_dist_plane = 125
self.min_dist_waypoint = self.wpt_reached * 0.95
self.max_dist_waypoint = 125
# define observation bounds
self.low_obs = np.array([
self.min_lat, self.min_lon,
normalizer(self.min_hdg, 'HDGToNorm', self.min_hdg, self.max_hdg),
normalizer(self.min_dist_plane, 'DistToNorm', self.min_dist_plane,
self.max_dist_plane),
normalizer(self.min_dist_waypoint, 'DistToNorm',
self.min_dist_waypoint, self.max_dist_waypoint)
])
self.high_obs = np.array([
self.max_lat, self.max_lon,
normalizer(self.max_hdg, 'HDGToNorm', self.min_hdg, self.max_hdg),
normalizer(self.max_dist_plane, 'DistToNorm', self.min_dist_plane,
self.max_dist_plane),
normalizer(self.max_dist_waypoint, 'DistToNorm',
self.min_dist_waypoint, self.max_dist_waypoint)
])
self.viewer = None
# Initialize bluesky
self.mode = 'sim-detached'
self.discovery = ('--discoverable' in sys.argv
or self.mode[-8:] == 'headless')
# Check if alternate config file is passed or a default scenfile
self.cfgfile = ''
self.scnfile = '/synthetics/super/super3.scn'
bs.init(self.mode,
discovery=self.discovery,
cfgfile=self.cfgfile,
scnfile=self.scnfile)
bs.sim.fastforward()
self.observation_space = spaces.Box(low=self.low_obs,
high=self.high_obs,
dtype=np.float32)
# self.action_space = spaces.Discrete(360)
self.action_space = spaces.Box(low=-1,
high=1,
shape=(1, ),
dtype=np.float32)