def __init__(self, stage):
'''Constructor'''
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.steer_lock = 0.785398
self.max_speed = 100
self.prev_rpm = None
# Initialize the joysticks
pygame.init()
pygame.joystick.init()
assert pygame.joystick.get_count() > 0
self.joystick = pygame.joystick.Joystick(0)
self.joystick.init()
print "Using joystick", self.joystick.get_name()
self.csv_logger = CSVLogger('states.csv')
def __init__(self, args):
'''Constructor'''
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = args.stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.state_size = 5
self.action_size = 3
self.model = ActorCriticModel(self.state_size, self.action_size)
self.steer_lock = 0.785398
self.max_speed = 100
self.enable_training = args.enable_training
self.enable_exploration = args.enable_exploration
self.show_sensors = args.show_sensors
self.total_train_steps = 0
self.exploration_decay_steps = args.exploration_decay_steps
self.exploration_rate_start = args.exploration_rate_start
self.exploration_rate_end = args.exploration_rate_end
self.episode = 0
self.onRestart()
def __init__(self, args):
'''Constructor'''
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.state_size = 20
self.action_size = 3
self.model = LinearModel(args.replay_size, self.state_size,
self.action_size)
self.enable_training = args.enable_training
self.enable_exploration = args.enable_exploration
self.show_sensors = args.show_sensors
self.exploration_decay_steps = args.exploration_decay_steps
self.exploration_rate_start = args.exploration_rate_start
self.exploration_rate_end = args.exploration_rate_end
from wheel import Wheel
self.wheel = Wheel(args.joystick_nr, args.autocenter, args.gain,
args.min_level, args.max_level, args.min_force)
self.steer_lock = 0.785398
self.max_speed = 100
#from plotlinear import PlotLinear
#self.plot = PlotLinear(self.model, ['Speed', 'Angle', 'TrackPos'], ['Steer', 'Accel', 'Brake'])
if self.show_sensors:
from sensorstats import Stats
self.stats = Stats(inevery=8)
self.reset()
def __init__(self, args):
'''Constructor'''
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = args.stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.steers = [-1.0, -0.8, -0.6, -0.5, -0.4, -0.3, -0.2, -0.15, -0.1, -0.05, 0.0, 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 1.0]
self.speeds = [-1.0, -0.5, 0.0, 0.5, 1.0]
self.num_inputs = 19
self.num_steers = len(self.steers)
self.num_speeds = len(self.speeds)
self.num_actions = self.num_steers + self.num_speeds
self.net = DeepQNetwork(self.num_inputs, self.num_steers, self.num_speeds, args)
self.mem = ReplayMemory(args.replay_size, self.num_inputs, args)
self.minibatch_size = args.batch_size
if args.load_replay:
self.mem.load(args.load_replay)
if args.load_weights:
self.net.load_weights(args.load_weights)
self.save_weights_prefix = args.save_weights_prefix
self.save_interval = args.save_interval
self.save_replay = args.save_replay
self.enable_training = args.enable_training
self.enable_exploration = args.enable_exploration
self.save_csv = args.save_csv
if self.save_csv:
self.csv_file = open(args.save_csv, "wb")
self.csv_writer = csv.writer(self.csv_file)
self.csv_writer.writerow(['episode', 'distFormStart', 'distRaced', 'curLapTime', 'lastLapTime', 'racePos', 'epsilon', 'replay_memory', 'train_steps'])
self.total_train_steps = 0
self.exploration_decay_steps = args.exploration_decay_steps
self.exploration_rate_start = args.exploration_rate_start
self.exploration_rate_end = args.exploration_rate_end
self.skip = args.skip
self.show_sensors = args.show_sensors
self.show_qvalues = args.show_qvalues
self.episode = 0
self.distances = []
self.onRestart()
if self.show_sensors:
from sensorstats import Stats
self.stats = Stats(inevery=8)
if self.show_qvalues:
from plotq import PlotQ
self.plotq = PlotQ(self.num_steers, self.num_speeds)
def __init__(self, accel = 0.0, brake = 0.0, gear = 1, steer = 0.0, clutch = 0.0, focus = 0, meta = 0):
'''Constructor'''
self.parser = msgParser.MsgParser()
self.actions = None
self._accel = accel
self._brake = brake
self._gear = gear
self._steer = steer
self._clutch = clutch
self._focus = focus
self._meta = meta
def __init__(self, stage):
'''Constructor'''
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.steer_lock = 0.785398
self.max_speed = 100
self.prev_rpm = None
def __init__(self, stage):
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.steer_lock = 0.785398
self.max_speed = 130
self.prev_rpm = None
self.table = pandas.read_csv("../input_path/Qtable.csv")
self.Accelerate = 0
self.Gearshift = 0
self.steerval = 0
def __init__(self, args):
'''Constructor'''
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
assert args.load_weights is not None, "Use --load_weights"
self.state_fields = args.state_fields
self.state_size = len(self.state_fields)
self.action_size = 3
self.model = LinearModel(args.replay_size, self.state_size, self.action_size)
self.model.load(args.load_weights)
self.episode = 0
self.onRestart()
self.show_sensors = args.show_sensors
if self.show_sensors:
from sensorstats import Stats
self.stats = Stats(inevery=8)
def __init__(self,
accel=0.0,
brake=0.0,
gear=1,
steer=0.0,
clutch=0.0,
focus=0,
meta=0):
self.parser = msgParser.MsgParser()
self.actions = None
self.accel = accel
self.brake = brake
self.gear = gear
self.steer = steer
self.clutch = clutch
self.focus = focus
self.meta = meta
def __init__(self, args):
'''Constructor'''
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.states = []
self.actions = []
self.filename = args.save_replay
assert self.filename is not None, "Use --save_replay <filename>"
from wheel import Wheel
self.wheel = Wheel(args.joystick_nr, args.autocenter, args.gain, args.min_level, args.max_level, args.min_force)
self.episode = 0
self.onRestart()
self.show_sensors = args.show_sensors
if self.show_sensors:
from sensorstats import Stats
self.stats = Stats(inevery=8)
def __init__(self, stage):
'''Constructor'''
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
# the max angle in rad that the car can turn left or right
self.steer_lock = 0.785398
self.max_speed = 100
self.prev_rpm = None
self.rl = learner.DriverLearner()
def __init__(self, stage):
'''Constructor'''
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.steer_lock = 0.785398
self.max_speed = 100
self.prev_rpm = None
# Initialize the joysticks
sdl2.SDL_Init(sdl2.SDL_INIT_JOYSTICK | sdl2.SDL_INIT_HAPTIC)
assert sdl2.SDL_NumJoysticks() > 0
assert sdl2.SDL_NumHaptics() > 0
self.joystick = sdl2.SDL_JoystickOpen(0)
self.haptic = sdl2.SDL_HapticOpen(0)
assert sdl2.SDL_JoystickNumAxes(self.joystick) == 3
assert sdl2.SDL_HapticQuery(self.haptic) & sdl2.SDL_HAPTIC_CONSTANT
# Initialize force feedback
efx = sdl2.SDL_HapticEffect(type=sdl2.SDL_HAPTIC_CONSTANT, constant= \
sdl2.SDL_HapticConstant(type=sdl2.SDL_HAPTIC_CONSTANT, direction= \
sdl2.SDL_HapticDirection(type=sdl2.SDL_HAPTIC_CARTESIAN, dir=(0,0,0)), \
length=sdl2.SDL_HAPTIC_INFINITY, level=0, attack_length=0, fade_length=0))
self.effect_id = sdl2.SDL_HapticNewEffect(self.haptic, efx)
sdl2.SDL_HapticRunEffect(self.haptic, self.effect_id, 1)
sdl2.SDL_HapticSetAutocenter(self.haptic, 0)
sdl2.SDL_HapticSetGain(self.haptic, 100)
self.stats = sensorstats.Stats(inevery=8)
def __init__(self):
self.parser = msgParser.MsgParser()
self.sensors = None
self.angle = None
self.curLapTime = None
self.damage = None
self.distFromStart = None
self.distRaced = None
self.focus = None
self.fuel = None
self.gear = None
self.lastLapTime = None
self.opponents = None
self.racePos = None
self.rpm = None
self.speedX = None
self.speedY = None
self.speedZ = None
self.track = None
self.trackPos = None
self.wheelSpinVel = None
self.z = None
def __init__(self, args):
'''Constructor'''
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = args.stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.state_size = 3
self.action_size = 3
self.model = LinearModel(args.replay_size, self.state_size,
self.action_size)
self.steer_lock = 0.785398
self.max_speed = 100
self.enable_training = args.enable_training
self.enable_exploration = args.enable_exploration
self.show_sensors = args.show_sensors
self.total_train_steps = 0
self.exploration_decay_steps = args.exploration_decay_steps
self.exploration_rate_start = args.exploration_rate_start
self.exploration_rate_end = args.exploration_rate_end
self.episode = 0
self.onRestart()
#from plotlinear import PlotLinear
#self.plot = PlotLinear(self.model, ['Speed', 'Angle', 'TrackPos'], ['Steer', 'Accel', 'Brake'])
if self.show_sensors:
from sensorstats import Stats
self.stats = Stats(inevery=8)
def __init__(self, stage, lanes_str, seed):
'''Constructor'''
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.steer_lock = 0.366519
self.max_speed = 100
self.prev_rpm = None
self.stuckCounter = 0
self.bringingCartBack = 0
self.lanes = [Direction[x] for x in lanes_str.split(',')]
random.seed(a=seed) # Set a random seed
def __init__(self):
"""Constructor"""
self.parser = msgParser.MsgParser()
self.sensors = None
self._angle = None
self._curLapTime = None
self._damage = None
self._distFromStart = None
self._distRaced = None
self._focus = None
self._fuel = None
self._gear = None
self._lastLapTime = None
self._opponents = None
self._racePos = None
self._rpm = None
self._speedX = None
self._speedY = None
self._speedZ = None
self._track = None
self._trackPos = None
self._wheelSpinVel = None
self._z = None
def __init__(self, replay_memory, deep_q_network, args):
'''Constructor'''
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.mem = replay_memory
self.net = deep_q_network
self.num_actions = 5
self.prev_rpm = None
self.prev_distance = 0
self.exploration_rate = args.exploration_rate_start
self.exploration_rate_start = args.exploration_rate_start
self.exploration_rate_end = args.exploration_rate_end
self.exploration_decay_steps = args.exploration_decay_steps
self.step_count = 0
self.prev_action = [0, 0]
self.stat = 0
def __init__(self):
'''Constructor'''
self.parser = msgParser.MsgParser()
self.sensors = None
# angle of the car from the direction of the track (radians)
# [-pi, +pi]
self.angle = None
self.curLapTime = None
self.damage = None
# distance from the start line, starting with 0 at the start line.
# Not very useful
self.distFromStart = None
# the distance going forward along the track that the car has gone
# since the start of the race
self.distRaced = None
self.focus = None
self.fuel = None
self.gear = None
# lap time is in seconds
self.lastLapTime = None
self.opponents = None
self.racePos = None
self.rpm = None
# speed is in kph
# forwards
self.speedX = None
# sideways
self.speedY = None
# airborne
self.speedZ = None
self.track = None
# how you are from the edge of the track
# [-1,1] - left edge to right edge
self.trackPos = None
self.wheelSpinVel = None
self.z = None
def __init__(self, stage):
'''Constructor'''
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.steer_lock = 0.785398
self.max_speed = 160
self.prev_rpm = None
#self.table=Qtable.maketable()
#after first iteration
self.table = pandas.read_csv("Qtable.csv")
self.Accelerate = 0
self.Gearshift = 0
self.steerval = 0
def __init__(self, args):
'''Constructor'''
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = args.stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.steers = [
-1.0, -0.8, -0.6, -0.5, -0.4, -0.3, -0.2, -0.15, -0.1, -0.05, 0.0,
0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 1.0
]
#self.speeds = [-1.0, -0.5, 0.0, 0.5, 1.0]
self.speeds = [0.0, 0.5, 1.0]
self.num_inputs = 3
self.num_steers = len(self.steers)
self.num_speeds = len(self.speeds)
self.learn = args.learn
if self.learn == 'both':
self.net = LinearModel(self.num_inputs,
(self.num_steers, self.num_speeds))
self.num_actions = 2
elif self.learn == 'steer':
self.net = LinearModel(self.num_inputs, (self.num_steers, ))
self.num_actions = 1
elif self.learn == 'speed':
self.net = LinearModel(self.num_inputs, (self.num_speeds, ))
self.num_actions = 1
else:
assert False
self.mem = ReplayMemory(args.replay_size, self.num_inputs,
self.num_actions)
if args.load_replay:
self.mem.load(args.load_replay)
if args.load_weights:
self.net.load_weights(args.load_weights)
self.save_weights_prefix = args.save_weights_prefix
self.save_interval = args.save_interval
self.save_replay = args.save_replay
self.enable_training = args.enable_training
self.enable_exploration = args.enable_exploration
self.save_csv = args.save_csv
if self.save_csv:
self.csv_file = open(args.save_csv + '.csv', "wb")
self.csv_writer = csv.writer(self.csv_file)
self.csv_writer.writerow([
'episode', 'distFormStart', 'distRaced', 'curLapTime',
'lastLapTime', 'racePos', 'epsilon', 'replay_memory',
'train_steps', 'avgmaxQ', 'avgloss'
])
self.total_train_steps = 0
self.exploration_decay_steps = args.exploration_decay_steps
self.exploration_rate_start = args.exploration_rate_start
self.exploration_rate_end = args.exploration_rate_end
self.skip = args.skip
self.repeat_train = args.repeat_train
self.show_sensors = args.show_sensors
self.show_qvalues = args.show_qvalues
self.steer_lock = 0.785398
self.max_speed = 100
self.loss_sum = self.loss_steps = 0
self.maxQ_sum = self.maxQ_steps = 0
self.episode = 0
self.distances = []
self.onRestart()
if self.show_sensors:
from sensorstats import Stats
self.stats = Stats(inevery=8)
if self.show_qvalues:
from plotq import PlotQ
self.plotq = PlotQ(self.num_steers, self.num_speeds,
args.update_qvalues_interval)
def __init__(self, args):
'''Constructor'''
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = args.stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.steers = [-1.0, -0.8, -0.6, -0.5, -0.4, -0.3, -0.2, -0.15, -0.1, -0.05, 0.0, 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 1.0]
self.speeds = [-1.0, -0.5, 0.0, 0.5, 1.0]
self.num_inputs = 19
self.num_steers = len(self.steers)
self.num_speeds = len(self.speeds)
self.num_actions = self.num_steers + self.num_speeds
self.net = DeepQNetwork(self.num_inputs, self.num_steers, self.num_speeds, args)
self.mem = ReplayMemory(args.replay_size, self.num_inputs, args)
self.minibatch_size = args.batch_size
if args.load_weights:
self.net.load_weights(args.load_weights)
self.save_weights_prefix = args.save_weights_prefix
self.pretrained_network = args.pretrained_network
self.steer_lock = 0.785398
self.max_speed = 100
self.algorithm = args.algorithm
self.device = args.device
self.mode = args.mode
self.maxwheelsteps = args.maxwheelsteps
self.enable_training = args.enable_training
self.enable_exploration = args.enable_exploration
self.total_train_steps = 0
self.exploration_decay_steps = args.exploration_decay_steps
self.exploration_rate_start = args.exploration_rate_start
self.exploration_rate_end = args.exploration_rate_end
self.show_sensors = args.show_sensors
self.show_qvalues = args.show_qvalues
self.episode = 0
self.onRestart()
if self.show_sensors:
from sensorstats import Stats
self.stats = Stats(inevery=8)
if self.show_qvalues:
from plotq import PlotQ
self.plotq = PlotQ(self.num_steers, self.num_speeds)
if self.device == 'wheel':
from wheel import Wheel
self.wheel = Wheel(args.joystick_nr, args.autocenter, args.gain, args.min_force, args.max_force)
def __init__(self, args):
'''Constructor'''
self.WARM_UP = 0
self.QUALIFYING = 1
self.RACE = 2
self.UNKNOWN = 3
self.stage = args.stage
self.parser = msgParser.MsgParser()
self.state = carState.CarState()
self.control = carControl.CarControl()
self.learn = args.learn
self.num_actions = 2 if self.learn == 'both' else 1
self.num_inputs = 20
self.net = DeepQNetwork(self.num_inputs, self.num_actions, args)
self.mem = ReplayMemory(args.replay_size,
self.num_inputs,
self.num_actions,
action_dtype=np.float)
self.minibatch_size = args.batch_size
if args.load_replay:
self.mem.load(args.load_replay)
if args.load_weights:
self.net.load_weights(args.load_weights)
self.save_weights_prefix = args.save_weights_prefix
self.save_interval = args.save_interval
self.save_replay = args.save_replay
self.enable_training = args.enable_training
self.enable_exploration = args.enable_exploration
self.save_csv = args.save_csv
if self.save_csv:
self.csv_file = open(args.save_csv + '.csv', "wb")
self.csv_writer = csv.writer(self.csv_file)
self.csv_writer.writerow([
'episode', 'distFormStart', 'distRaced', 'curLapTime',
'lastLapTime', 'racePos', 'replay_memory', 'train_steps',
'avgmaxQ', 'avgloss'
])
self.skip = args.skip
self.repeat_train = args.repeat_train
self.show_sensors = args.show_sensors
self.show_qvalues = args.show_qvalues
self.steer_lock = 0.785398
self.max_speed = 100
self.loss_sum = self.loss_steps = 0
self.maxQ_sum = self.maxQ_steps = 0
self.episode = 0
self.distances = []
self.onRestart()
if self.show_sensors:
from sensorstats import Stats
self.stats = Stats(inevery=8)
if self.show_qvalues:
from plotq import PlotQ
self.plotq = PlotQ(self.num_steers, self.num_speeds,
args.update_qvalues_interval)
