def step(self, carstate: State, command: Command):
if carstate.gear <= 0:
carstate.gear = 1
input = self.processInput(carstate)
if np.isnan(input).any():
return False
try:
output = self.model.activate(input)
for i in range(len(output)):
if np.isnan(output[i]):
output[i] = 0.0
print('Out = ' + str(output))
self.accelerate(output[0], output[1], carstate, command)
if len(output) == 3:
# use this if the steering is just one output
self.steer(output[2], 0, carstate, command)
else:
# use this command if there are 2 steering outputs
self.steer(output[2], output[3], carstate, command)
except:
print('Error!')
raise
return True
def drive(self, carstate: State) -> Command:
print('Drive')
input = carstate.to_input_array()
if np.isnan(input).any():
if not self.stopped:
self.saveResults()
print(input)
print('NaN INPUTS!!! STOP WORKING')
return Command()
self.stopped = np.isnan(input).any()
self.print_log(carstate)
self.update(carstate)
try:
output = self.net.activate(input)
command = Command()
if self.unstuck and self.isStuck(carstate):
print('Ops! I got STUCK!!!')
self.reverse(carstate, command)
else:
if self.unstuck and carstate.gear <= 0:
carstate.gear = 1
for i in range(len(output)):
if np.isnan(output[i]):
output[i] = 0.0
print('Out = ' + str(output))
self.accelerate(output[0], output[1], 0, carstate, command)
if len(output) == 3:
# use this if the steering is just one output
self.steer(output[2], 0, carstate, command)
else:
# use this command if there are 2 steering outputs
self.steer(output[2], output[3], carstate, command)
except:
print('Error!')
self.saveResults()
raise
if self.data_logger:
self.data_logger.log(carstate, command)
return command
def step(self, carstate: State, command: Command):
if carstate.gear <= 0:
carstate.gear = 1
input = self.processInput(carstate)
if np.isnan(input).any():
return False
try:
output = self.model.activate(input)
for i in range(len(output)):
if np.isnan(output[i]):
output[i] = 0.0
print('Out = ' + str(output))
accelerator = 0
brake = 0
if len(output) == 2:
if output[0] > 0:
accelerator = output[0]
else:
brake = -1 * output[0]
else:
accelerator = output[0]
brake = output[1]
self.accelerate(accelerator, brake, carstate, command)
self.steer(output[-1], 0, carstate, command)
except:
print('Error!')
raise
return True
def drive(self, carstate: State) -> Command:
if not self.race_started and carstate.distance_from_start < 3:
self.race_started = True
try:
position_team = int(open("mjv_partner1.txt", 'r').read())
open("mjv_partner2.txt", 'w').write(str(carstate.race_position))
self.number = 2
self.partner = 1
except:
open("mjv_partner1.txt", "w").write(str(carstate.race_position))
self.number = 1
self.partner = 2
elif self.race_started:
position_other = int(open("mjv_partner{}.txt".format(self.partner), 'r').read())
open("mjv_partner{}.txt".format(self.number), "w").write(str(carstate.race_position))
if carstate.race_position > position_other:
self.leader = False
else:
self.leader = True
# print("Distance: {}".format(carstate.distance_from_start))
# Select the sensors we need for our model
self.speeds.append(carstate.speed_x)
command = Command()
distances = list(carstate.distances_from_edge)
sensors = [carstate.speed_x, carstate.distance_from_center,
carstate.angle, *(carstate.distances_from_edge)]
# CRASHED
off_road = all(distance == -1.0 for distance in distances)
standing_still = self.recovers == 0 and all(abs(s) < 1.0 for s in self.speeds[-5:])
if self.race_started and (off_road or self.recovers > 0):
command = self.recover(carstate, command)
# NOT CRASHED
else:
if carstate.gear == -1:
carstate.gear = 2
if self.norm:
# Sensor normalization -> ?
sensors = self.normalize_sensors(sensors)
# Forward pass our model
y = self.model(Variable(torch.Tensor(sensors)))
# Create command from model output
command.accelerator = np.clip(y.data[0], 0, 1)
command.brake = np.clip(y.data[1], 0, 1)
command.steering = np.clip(y.data[2], -1, 1)
if self.race_started and self.is_leader and carstate.distance_from_start > 50: # TODO include not leader
command = self.helper.drive(command, distances, carstate)
# Naive switching of gear
self.switch_gear(carstate, command)
# print("Accelerate: {}".format(command.accelerator))
# print("Brake: {}".format(command.brake))
# print("Steer: {}".format(command.steering))
if self.record is True:
sensor_string = ",".join([str(x) for x in sensors]) + "\n"
self.file_handler.write(str(y.data[0]) + "," + str(y.data[1]) + "," + str(y.data[2]) + "," + sensor_string)
return command
def drive(self, carstate: State) -> Command:
if carstate.distance_raced < 3:
try:
self.dict["position"] = carstate.race_position
with open("mjv_partner{}.txt".format(self.port),
"wb") as handle:
pickle.dump(self.dict,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
except:
pass
path = os.path.abspath(os.path.dirname(__file__))
for i in os.listdir(path):
if os.path.isfile(os.path.join(
path,
i)) and 'mjv_partner' in i and not str(self.port) in i:
self.partner = i.strip('mjv_partner').strip('.txt')
else:
self.race_started = True
try:
with open("mjv_partner{}.txt".format(self.partner),
'rb') as handle:
self.dict_teammate = pickle.load(handle)
self.dict["position"] = carstate.race_position
with open("mjv_partner{}.txt".format(self.port),
"wb") as handle:
pickle.dump(self.dict,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
# print(self.port, self.dict_teammate["position"])
# print("RESULT", carstate.race_position > int(self.dict_teammate["position"]))
if carstate.race_position > int(
self.dict_teammate["position"]):
self.is_leader = False
else:
self.is_leader = True
except:
print("Not able to read port", self.port)
pass
# print(self.dict_teammate)
# print(self.port, self.leader)
# print("Distance: {}".format(carstate.distance_from_start))
# Select the sensors we need for our model
self.speeds.append(carstate.speed_x)
command = Command()
distances = list(carstate.distances_from_edge)
sensors = [
carstate.speed_x, carstate.distance_from_center, carstate.angle,
*(carstate.distances_from_edge)
]
# CRASHED
off_road = all(distance == -1.0 for distance in distances)
standing_still = self.recovers == 0 and all(
abs(s) < 1.0 for s in self.speeds[-5:])
if self.race_started and (off_road or self.recovers > 0):
command = self.recover(carstate, command)
if not self.crash_recorded:
self.crash_recorded = True
self.dict["crashes"].append(carstate.distance_raced)
# NOT CRASHED
else:
self.crash_recorded = False
if carstate.gear == -1:
carstate.gear = 2
if self.norm:
# Sensor normalization -> ?
sensors = self.normalize_sensors(sensors)
# Forward pass our model
y = self.model(Variable(torch.Tensor(sensors)))[0]
# Create command from model output
command.accelerator = np.clip(y.data[0], 0, 1)
command.brake = np.clip(y.data[1], 0, 1)
command.steering = np.clip(y.data[2], -1, 1)
command = self.smooth_commands(command)
# print(self.race_started and not self.is_leader)
# print("LEADER", self.is_leader)
if self.race_started and not self.is_leader and carstate.distance_from_start > 50:
command = self.check_swarm(command, carstate)
# Naive switching of gear
self.switch_gear(carstate, command)
# print("Accelerate: {}".format(command.accelerator))
# print("Brake: {}".format(command.brake))
# print("Steer: {}".format(command.steering))
if self.record is True:
sensor_string = ",".join([str(x) for x in sensors]) + "\n"
self.file_handler.write(
str(y.data[0]) + "," + str(y.data[1]) + "," + str(y.data[2]) +
"," + sensor_string)
return command