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)
class Driver(object):
'''
A driver object for the SCRC
'''
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 = 20
self.num_steers = len(self.steers)
self.num_speeds = len(self.speeds)
self.learn = args.learn
if self.learn == 'both':
self.net = DeepQNetwork(self.num_inputs, (self.num_steers, self.num_speeds), args)
self.num_actions = 2
elif self.learn == 'steer':
self.net = DeepQNetwork(self.num_inputs, (self.num_steers,), args)
self.num_actions = 1
elif self.learn == 'speed':
self.net = DeepQNetwork(self.num_inputs, (self.num_speeds,), args)
self.num_actions = 1
else:
assert False
self.mem = ReplayMemory(args.replay_size, self.num_inputs, self.num_actions)
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', '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):
'''Return init string with rangefinder angles'''
self.angles = [0 for x in range(19)]
for i in range(5):
self.angles[i] = -90 + i * 15
self.angles[18 - i] = 90 - i * 15
for i in range(5, 9):
self.angles[i] = -20 + (i-5) * 5
self.angles[18 - i] = 20 - (i-5) * 5
return self.parser.stringify({'init': self.angles})
def getState(self):
#state = np.array([self.state.getSpeedX() / 200.0, self.state.getAngle(), self.state.getTrackPos()])
#state = np.array(self.state.getTrack() + [self.state.getSpeedX()]) / 200.0
#state = np.array(self.state.getTrack()) / 200.0
state = np.array(self.state.getTrack() + [self.state.getSpeedX()])
assert state.shape == (self.num_inputs,)
return state
def getReward(self, terminal):
if terminal:
reward = -1000
else:
dist = self.state.getDistFromStart()
if self.prev_dist is not None:
reward = max(0, dist - self.prev_dist) * 10
assert reward >= 0, "reward: %f" % reward
else:
reward = 0
self.prev_dist = dist
reward -= abs(10 * self.state.getTrackPos())
#print "reward:", reward
return reward
def getTerminal(self):
return np.all(np.array(self.state.getTrack()) == -1)
def getEpsilon(self):
# calculate decaying exploration rate
if self.total_train_steps < self.exploration_decay_steps:
return self.exploration_rate_start - self.total_train_steps * (self.exploration_rate_start - self.exploration_rate_end) / self.exploration_decay_steps
else:
return self.exploration_rate_end
def drive(self, msg):
# parse incoming message
self.state.setFromMsg(msg)
# show sensors
if self.show_sensors:
self.stats.update(self.state)
# training
if self.enable_training and self.mem.count > 0:
for i in xrange(self.repeat_train):
minibatch = self.mem.getMinibatch(self.minibatch_size)
self.loss_sum += self.net.train(minibatch)
self.loss_steps += 1
# skip frame and use the same action as previously
if self.skip > 0:
self.frame = (self.frame + 1) % self.skip
if self.frame != 0:
return self.control.toMsg()
# fetch state, calculate reward and terminal indicator
state = self.getState()
terminal = self.getTerminal()
reward = self.getReward(terminal)
#print "reward:", reward
# store new experience in replay memory
if self.enable_training and self.prev_state is not None and self.prev_action is not None:
self.mem.add(self.prev_state, self.prev_action, reward, state, terminal)
# if terminal state (out of track), then restart game
if self.enable_training and terminal:
#print "terminal state, restarting"
self.control.setMeta(1)
return self.control.toMsg()
else:
self.control.setMeta(0)
# use broadcasting to efficiently produce minibatch of desired size
minibatch = state + np.zeros((self.minibatch_size, 1))
Q = self.net.predict(minibatch)
#print "Q:", Q[0]
if self.show_qvalues:
self.plotq.update(Q[0])
self.maxQ_sum += np.max(Q[0])
self.maxQ_steps += 1
# choose actions for wheel and speed
epsilon = self.getEpsilon()
#print "epsilon:", epsilon
if self.learn == 'steer' or self.learn == 'both':
if self.enable_exploration and random.random() < epsilon:
#print "random steer"
steer = random.randrange(self.num_steers)
else:
#print "steer Q: ", Q[0,:self.num_steers]
steer = np.argmax(Q[0, :self.num_steers])
#steer = np.argmax(Q[0])
self.setSteerAction(steer)
else:
self.steer()
if self.learn == 'speed' or self.learn == 'both':
if self.enable_exploration and random.random() < epsilon:
#speed = random.randrange(self.num_speeds)
# don't do braking
speed = random.randint(2, self.num_speeds-1)
else:
#print "speed Q:", Q[0,-self.num_speeds:]
speed = np.argmax(Q[0, -self.num_speeds:])
#speed = np.argmax(Q[0])
self.setSpeedAction(speed)
else:
self.speed()
#print "steer:", steer, "speed:", speed
#print "speed:", speed
#print "steer:", steer
# gears are always automatic
gear = self.gear()
self.setGearAction(gear)
# remember state and actions
self.prev_state = state
if self.learn == 'both':
self.prev_action = np.array([steer, speed])
elif self.learn == 'steer':
self.prev_action = np.array([steer])
elif self.learn == 'speed':
self.prev_action = np.array([speed])
else:
assert False
self.total_train_steps += 1
#print "total_train_steps:", self.total_train_steps
#print "total_train_steps:", self.total_train_steps, "mem_count:", self.mem.count
#print "reward:", reward, "epsilon:", epsilon
return self.control.toMsg()
def steer(self):
angle = self.state.angle
dist = self.state.trackPos
self.control.setSteer((angle - dist*0.5)/self.steer_lock)
def gear(self):
speed = self.state.getSpeedX()
gear = self.state.getGear()
if speed < 25:
gear = 1
elif 30 < speed < 55:
gear = 2
elif 60 < speed < 85:
gear = 3
elif 90 < speed < 115:
gear = 4
elif 120 < speed < 145:
gear = 5
elif speed > 150:
gear = 6
return gear
def speed(self):
speed = self.state.getSpeedX()
accel = self.control.getAccel()
if speed < self.max_speed:
accel += 0.1
if accel > 1:
accel = 1.0
else:
accel -= 0.1
if accel < 0:
accel = 0.0
self.control.setAccel(accel)
def setSteerAction(self, steer):
assert 0 <= steer <= self.num_steers
self.control.setSteer(self.steers[steer])
def setGearAction(self, gear):
assert -1 <= gear <= 6
self.control.setGear(gear)
def setSpeedAction(self, speed):
assert 0 <= speed <= self.num_speeds
accel = self.speeds[speed]
if accel >= 0:
#print "accel", accel
self.control.setAccel(accel)
self.control.setBrake(0)
else:
#print "brake", -accel
self.control.setAccel(0)
self.control.setBrake(-accel)
def onShutDown(self):
#if self.save_weights_prefix:
# self.net.save_weights(self.save_weights_prefix + "_" + str(self.episode - 1) + ".pkl")
if self.save_replay:
self.mem.save(self.save_replay)
if self.save_csv:
self.csv_file.close()
def onRestart(self):
self.prev_rpm = None
self.prev_dist = None
self.prev_state = None
self.prev_action = None
self.frame = -1
if self.episode > 0:
dist = self.state.getDistRaced()
self.distances.append(dist)
epsilon = self.getEpsilon()
avgloss = self.loss_sum / max(self.loss_steps, 1)
self.loss_sum = self.loss_steps = 0
avgmaxQ = self.maxQ_sum / max(self.maxQ_steps, 1)
self.maxQ_sum = self.maxQ_steps = 0
print "Episode:", self.episode, "\tDistance:", dist, "\tMax:", max(self.distances), "\tMedian10:", np.median(self.distances[-10:]), \
"\tEpsilon:", epsilon, "\tReplay memory:", self.mem.count, "\tAverage loss:", avgloss, "\tAverage maxQ", avgmaxQ
if self.save_weights_prefix and self.save_interval > 0 and self.episode % self.save_interval == 0:
self.net.save_weights(self.save_weights_prefix + "_" + str(self.episode) + ".pkl")
#self.mem.save(self.save_weights_prefix + "_" + str(self.episode) + "_replay.pkl")
if self.save_csv:
self.csv_writer.writerow([
self.episode,
self.state.getDistFromStart(),
self.state.getDistRaced(),
self.state.getCurLapTime(),
self.state.getLastLapTime(),
self.state.getRacePos(),
epsilon,
self.mem.count,
self.total_train_steps,
avgmaxQ,
avgloss
])
self.csv_file.flush()
self.episode += 1
class Driver(object):
'''
A driver object for the SCRC
'''
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 = 20
self.num_steers = len(self.steers)
self.num_speeds = len(self.speeds)
self.learn = args.learn
if self.learn == 'both':
self.net = DeepQNetwork(self.num_inputs,
(self.num_steers, self.num_speeds), args)
self.num_actions = 2
elif self.learn == 'steer':
self.net = DeepQNetwork(self.num_inputs, (self.num_steers, ), args)
self.num_actions = 1
elif self.learn == 'speed':
self.net = DeepQNetwork(self.num_inputs, (self.num_speeds, ), args)
self.num_actions = 1
else:
assert False
self.mem = ReplayMemory(args.replay_size, self.num_inputs,
self.num_actions)
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', '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):
'''Return init string with rangefinder angles'''
self.angles = [0 for x in range(19)]
for i in range(5):
self.angles[i] = -90 + i * 15
self.angles[18 - i] = 90 - i * 15
for i in range(5, 9):
self.angles[i] = -20 + (i - 5) * 5
self.angles[18 - i] = 20 - (i - 5) * 5
return self.parser.stringify({'init': self.angles})
def getState(self):
#state = np.array([self.state.getSpeedX() / 200.0, self.state.getAngle(), self.state.getTrackPos()])
#state = np.array(self.state.getTrack() + [self.state.getSpeedX()]) / 200.0
#state = np.array(self.state.getTrack()) / 200.0
state = np.array(self.state.getTrack() + [self.state.getSpeedX()])
assert state.shape == (self.num_inputs, )
return state
def getReward(self, terminal):
if terminal:
reward = -1000
else:
dist = self.state.getDistFromStart()
if self.prev_dist is not None:
reward = max(0, dist - self.prev_dist) * 10
assert reward >= 0, "reward: %f" % reward
else:
reward = 0
self.prev_dist = dist
reward -= abs(10 * self.state.getTrackPos())
#print "reward:", reward
return reward
def getTerminal(self):
return np.all(np.array(self.state.getTrack()) == -1)
def getEpsilon(self):
# calculate decaying exploration rate
if self.total_train_steps < self.exploration_decay_steps:
return self.exploration_rate_start - self.total_train_steps * (
self.exploration_rate_start -
self.exploration_rate_end) / self.exploration_decay_steps
else:
return self.exploration_rate_end
def drive(self, msg):
# parse incoming message
self.state.setFromMsg(msg)
# show sensors
if self.show_sensors:
self.stats.update(self.state)
# training
if self.enable_training and self.mem.count > 0:
for i in xrange(self.repeat_train):
minibatch = self.mem.getMinibatch(self.minibatch_size)
self.loss_sum += self.net.train(minibatch)
self.loss_steps += 1
# skip frame and use the same action as previously
if self.skip > 0:
self.frame = (self.frame + 1) % self.skip
if self.frame != 0:
return self.control.toMsg()
# fetch state, calculate reward and terminal indicator
state = self.getState()
terminal = self.getTerminal()
reward = self.getReward(terminal)
#print "reward:", reward
# store new experience in replay memory
if self.enable_training and self.prev_state is not None and self.prev_action is not None:
self.mem.add(self.prev_state, self.prev_action, reward, state,
terminal)
# if terminal state (out of track), then restart game
if self.enable_training and terminal:
#print "terminal state, restarting"
self.control.setMeta(1)
return self.control.toMsg()
else:
self.control.setMeta(0)
# use broadcasting to efficiently produce minibatch of desired size
minibatch = state + np.zeros((self.minibatch_size, 1))
Q = self.net.predict(minibatch)
#print "Q:", Q[0]
if self.show_qvalues:
self.plotq.update(Q[0])
self.maxQ_sum += np.max(Q[0])
self.maxQ_steps += 1
# choose actions for wheel and speed
epsilon = self.getEpsilon()
#print "epsilon:", epsilon
if self.learn == 'steer' or self.learn == 'both':
if self.enable_exploration and random.random() < epsilon:
#print "random steer"
steer = random.randrange(self.num_steers)
else:
#print "steer Q: ", Q[0,:self.num_steers]
steer = np.argmax(Q[0, :self.num_steers])
#steer = np.argmax(Q[0])
self.setSteerAction(steer)
else:
self.steer()
if self.learn == 'speed' or self.learn == 'both':
if self.enable_exploration and random.random() < epsilon:
#speed = random.randrange(self.num_speeds)
# don't do braking
speed = random.randint(2, self.num_speeds - 1)
else:
#print "speed Q:", Q[0,-self.num_speeds:]
speed = np.argmax(Q[0, -self.num_speeds:])
#speed = np.argmax(Q[0])
self.setSpeedAction(speed)
else:
self.speed()
#print "steer:", steer, "speed:", speed
#print "speed:", speed
#print "steer:", steer
# gears are always automatic
gear = self.gear()
self.setGearAction(gear)
# remember state and actions
self.prev_state = state
if self.learn == 'both':
self.prev_action = np.array([steer, speed])
elif self.learn == 'steer':
self.prev_action = np.array([steer])
elif self.learn == 'speed':
self.prev_action = np.array([speed])
else:
assert False
self.total_train_steps += 1
#print "total_train_steps:", self.total_train_steps
#print "total_train_steps:", self.total_train_steps, "mem_count:", self.mem.count
#print "reward:", reward, "epsilon:", epsilon
return self.control.toMsg()
def steer(self):
angle = self.state.angle
dist = self.state.trackPos
self.control.setSteer((angle - dist * 0.5) / self.steer_lock)
def gear(self):
speed = self.state.getSpeedX()
gear = self.state.getGear()
if speed < 25:
gear = 1
elif 30 < speed < 55:
gear = 2
elif 60 < speed < 85:
gear = 3
elif 90 < speed < 115:
gear = 4
elif 120 < speed < 145:
gear = 5
elif speed > 150:
gear = 6
return gear
def speed(self):
speed = self.state.getSpeedX()
accel = self.control.getAccel()
if speed < self.max_speed:
accel += 0.1
if accel > 1:
accel = 1.0
else:
accel -= 0.1
if accel < 0:
accel = 0.0
self.control.setAccel(accel)
def setSteerAction(self, steer):
assert 0 <= steer <= self.num_steers
self.control.setSteer(self.steers[steer])
def setGearAction(self, gear):
assert -1 <= gear <= 6
self.control.setGear(gear)
def setSpeedAction(self, speed):
assert 0 <= speed <= self.num_speeds
accel = self.speeds[speed]
if accel >= 0:
#print "accel", accel
self.control.setAccel(accel)
self.control.setBrake(0)
else:
#print "brake", -accel
self.control.setAccel(0)
self.control.setBrake(-accel)
def onShutDown(self):
#if self.save_weights_prefix:
# self.net.save_weights(self.save_weights_prefix + "_" + str(self.episode - 1) + ".pkl")
if self.save_replay:
self.mem.save(self.save_replay)
if self.save_csv:
self.csv_file.close()
def onRestart(self):
self.prev_rpm = None
self.prev_dist = None
self.prev_state = None
self.prev_action = None
self.frame = -1
if self.episode > 0:
dist = self.state.getDistRaced()
self.distances.append(dist)
epsilon = self.getEpsilon()
avgloss = self.loss_sum / max(self.loss_steps, 1)
self.loss_sum = self.loss_steps = 0
avgmaxQ = self.maxQ_sum / max(self.maxQ_steps, 1)
self.maxQ_sum = self.maxQ_steps = 0
print "Episode:", self.episode, "\tDistance:", dist, "\tMax:", max(self.distances), "\tMedian10:", np.median(self.distances[-10:]), \
"\tEpsilon:", epsilon, "\tReplay memory:", self.mem.count, "\tAverage loss:", avgloss, "\tAverage maxQ", avgmaxQ
if self.save_weights_prefix and self.save_interval > 0 and self.episode % self.save_interval == 0:
self.net.save_weights(self.save_weights_prefix + "_" +
str(self.episode) + ".pkl")
#self.mem.save(self.save_weights_prefix + "_" + str(self.episode) + "_replay.pkl")
if self.save_csv:
self.csv_writer.writerow([
self.episode,
self.state.getDistFromStart(),
self.state.getDistRaced(),
self.state.getCurLapTime(),
self.state.getLastLapTime(),
self.state.getRacePos(), epsilon, self.mem.count,
self.total_train_steps, avgmaxQ, avgloss
])
self.csv_file.flush()
self.episode += 1
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)
class Driver(object):
'''
A driver object for the SCRC
'''
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):
'''Return init string with rangefinder angles'''
self.angles = [0 for x in range(19)]
for i in range(5):
self.angles[i] = -90 + i * 15
self.angles[18 - i] = 90 - i * 15
for i in range(5, 9):
self.angles[i] = -20 + (i-5) * 5
self.angles[18 - i] = 20 - (i-5) * 5
return self.parser.stringify({'init': self.angles})
def getState(self):
#state = np.array([self.state.getSpeedX() / 200.0, self.state.getAngle(), self.state.getTrackPos()])
#state = np.array(self.state.getTrack() + [self.state.getSpeedX()]) / 200.0
state = np.array(self.state.getTrack()) / 200.0
assert state.shape == (self.num_inputs,)
return state
def getReward(self, terminal):
if terminal:
reward = -1000
else:
dist = self.state.getDistFromStart()
if self.prev_dist is not None:
reward = max(0, dist - self.prev_dist) * 10
assert reward >= 0, "reward: %f" % reward
else:
reward = 0
self.prev_dist = dist
#reward -= self.state.getTrackPos()
#print "reward:", reward
return reward
def getTerminal(self):
return np.all(np.array(self.state.getTrack()) == -1)
def getEpsilon(self):
# calculate decaying exploration rate
if self.total_train_steps < self.exploration_decay_steps:
return self.exploration_rate_start - self.total_train_steps * (self.exploration_rate_start - self.exploration_rate_end) / self.exploration_decay_steps
else:
return self.exploration_rate_end
def drive(self, msg):
# parse incoming message
self.state.setFromMsg(msg)
# show sensors
if self.show_sensors:
self.stats.update(self.state)
# fetch state, calculate reward and terminal indicator
state = self.getState()
terminal = self.getTerminal()
reward = self.getReward(terminal)
#print "reward:", reward
# store new experience in replay memory
if self.enable_training and self.prev_state is not None and self.prev_steer is not None and self.prev_speed is not None:
self.mem.add(self.prev_state, self.prev_steer, self.prev_speed, reward, state, terminal)
# if terminal state (out of track), then restart game
if terminal:
print "terminal state, restarting"
self.control.setMeta(1)
return self.control.toMsg()
else:
self.control.setMeta(0)
# choose actions for wheel and speed
if self.enable_exploration and random.random() < self.getEpsilon():
#print "random move"
steer = random.randrange(self.num_steers)
#speed = random.randrange(self.num_speeds)
speed = random.randint(2, self.num_speeds-1)
elif self.algorithm == 'network':
# use broadcasting to efficiently produce minibatch of desired size
minibatch = state + np.zeros((self.minibatch_size, 1))
Q = self.net.predict(minibatch)
assert Q.shape == (self.minibatch_size, self.num_actions), "Q.shape: %s" % str(Q.shape)
#print "steer Q: ", Q[0,:21]
#print "speed Q:", Q[0,-5:]
steer = np.argmax(Q[0, :self.num_steers])
speed = np.argmax(Q[0, -self.num_speeds:])
if self.show_qvalues:
self.plotq.update(Q[0])
elif self.algorithm == 'hardcoded':
steer = self.getSteerAction(self.steer())
speed = self.getSpeedActionAccel(self.speed())
else:
assert False, "Unknown algorithm"
#print "steer:", steer, "speed:", speed
# gears are always automatic
gear = self.gear()
# check for manual override
# might be partial, so we always need to choose algorithmic actions first
events = self.wheel.getEvents()
if self.mode == 'override' and self.wheel.supportsDrive():
# wheel
for event in events:
if self.wheel.isWheelMotion(event):
self.wheelsteps = self.maxwheelsteps
if self.wheelsteps > 0:
wheel = self.wheel.getWheel()
steer = self.getSteerAction(wheel)
self.wheelsteps -= 1
# gas pedal
accel = self.wheel.getAccel()
if accel > 0:
speed = self.getSpeedActionAccel(accel)
# brake pedal
brake = self.wheel.getBrake()
if brake > 0:
speed = self.getSpeedActionBrake(brake)
# check for wheel buttons always, not only in override mode
for event in events:
if self.wheel.isButtonDown(event, 2):
self.algorithm = 'network'
self.mode = 'override'
self.wheel.generateForce(0)
print "Switched to network algorithm"
elif self.wheel.isButtonDown(event, 3):
self.net.load_weights(self.pretrained_network)
self.algorithm = 'network'
self.mode = 'ff'
self.enable_training = False
print "Switched to pretrained network"
elif self.wheel.isButtonDown(event, 4):
self.enable_training = not self.enable_training
print "Switched training", "ON" if self.enable_training else "OFF"
elif self.wheel.isButtonDown(event, 5):
self.algorithm = 'hardcoded'
self.mode = 'ff'
print "Switched to hardcoded algorithm"
elif self.wheel.isButtonDown(event, 6):
self.enable_exploration = not self.enable_exploration
self.mode = 'override'
self.wheel.generateForce(0)
print "Switched exploration", "ON" if self.enable_exploration else "OFF"
elif self.wheel.isButtonDown(event, 7):
self.mode = 'ff' if self.mode == 'override' else 'override'
if self.mode == 'override':
self.wheel.generateForce(0)
print "Switched force feedback", "ON" if self.mode == 'ff' else "OFF"
elif self.wheel.isButtonDown(event, 0) or self.wheel.isButtonDown(event, 8):
gear = max(-1, gear - 1)
elif self.wheel.isButtonDown(event, 1) or self.wheel.isButtonDown(event, 9):
gear = min(6, gear + 1)
# set actions
self.setSteerAction(steer)
self.setGearAction(gear)
self.setSpeedAction(speed)
# turn wheel using force feedback
if self.mode == 'ff' and self.wheel.supportsForceFeedback():
wheel = self.wheel.getWheel()
self.wheel.generateForce(self.control.getSteer()-wheel)
# remember state and actions
self.prev_state = state
self.prev_steer = steer
self.prev_speed = speed
# training
if self.enable_training and self.mem.count >= self.minibatch_size:
minibatch = self.mem.getMinibatch()
self.net.train(minibatch)
self.total_train_steps += 1
#print "total_train_steps:", self.total_train_steps
#print "total_train_steps:", self.total_train_steps, "mem_count:", self.mem.count
return self.control.toMsg()
def setSteerAction(self, steer):
self.control.setSteer(self.steers[steer])
def setGearAction(self, gear):
assert -1 <= gear <= 6
self.control.setGear(gear)
def setSpeedAction(self, speed):
accel = self.speeds[speed]
if accel >= 0:
#print "accel", accel
self.control.setAccel(accel)
self.control.setBrake(0)
else:
#print "brake", -accel
self.control.setAccel(0)
self.control.setBrake(-accel)
def getSteerAction(self, wheel):
steer = np.argmin(np.abs(np.array(self.steers) - wheel))
return steer
def getSpeedActionAccel(self, accel):
speed = np.argmin(np.abs(np.array(self.speeds) - accel))
return speed
def getSpeedActionBrake(self, brake):
speed = np.argmin(np.abs(np.array(self.speeds) + brake))
return speed
def steer(self):
angle = self.state.angle
dist = self.state.trackPos
steer = (angle - dist*0.5)/self.steer_lock
return steer
def gear(self):
rpm = self.state.getRpm()
gear = self.state.getGear()
if self.prev_rpm == None:
up = True
else:
if (self.prev_rpm - rpm) < 0:
up = True
else:
up = False
if up and rpm > 7000:
gear += 1
if not up and rpm < 3000:
gear -= 1
return gear
def speed(self):
speed = self.state.getSpeedX()
accel = self.prev_accel
if speed < self.max_speed:
accel += 0.1
if accel > 1:
accel = 1.0
else:
accel -= 0.1
if accel < 0:
accel = 0.0
self.prev_accel = accel
return accel
def onShutDown(self):
pass
def onRestart(self):
if self.mode == 'ff':
self.wheel.generateForce(0)
self.prev_rpm = None
self.prev_accel = 0
self.prev_dist = None
self.prev_state = None
self.prev_steer = None
self.prev_speed = None
self.wheelsteps = 0
if self.save_weights_prefix and self.episode > 0:
self.net.save_weights(self.save_weights_prefix + "_" + str(self.episode) + ".pkl")
self.episode += 1
print "Episode", self.episode
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.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)