def drive(self):
# if in first iteration after learning is finished
if(self.first_time):
# make robot drive faster
# self.speed *= 1.5
# make sound to signal that learning is finished
sound.make_sound()
# save neural network
self.save_model()
# indicate end of first iteration after learning is finished
self.first_time = False
# wait for new image(s)
my_imgs, my_img = self.shape_images()
# get new / current state
self.curr_state, reward = self.get_robot_state(my_img)
# stop robot
stop_arr=[self.lost_line]
if(self.curr_state in stop_arr):
self.stopRobot()
# get q-values by feeding images to the DQN
# without updating its weights
self.q_values = self.policy_net.use_network(state=my_imgs)
# choose action by selecting highest q -value
action = np.argmax(self.q_values)
print("Action: " + self.action_strings.get(action))
# execute action
self.execute_action(action)
# stop if line is lost (hopefully never, if
# robot learned properly)
if (self.curr_state == self.lost_line):
self.reset_environment()
def __init__(self):
self.turtle_init()
self.left_rocket = Rocket(
'left', CONFIGS.ROCKET_SPEED,
(-CONFIGS.WIDTH / 2 + CONFIGS.ROCKETS_DXPOS, 0),
CONFIGS.ROCKET1_COLOR)
self.right_rocket = Rocket(
'right', CONFIGS.ROCKET_SPEED,
(CONFIGS.WIDTH / 2 - CONFIGS.ROCKETS_DXPOS, 0),
CONFIGS.ROCKET2_COLOR)
self.ball = Ball(CONFIGS.BALL_SPEED, (0, 0),
(self.left_rocket, self.right_rocket))
self.message_turtle = None
t.update()
make_sound(4)
self.activate_keys()
def check_boundaries(self):
# reflection from the obstacles
for obs in self.obstacles:
if -CONFIGS.BALL_SPEED / 2 < obs.x + obs.dx0 - self.x < CONFIGS.BALL_SPEED / 2 \
and -CONFIGS.ROCKET_HEIGHT * CONFIGS.TKTURTLE_DX / 2 < self.y - obs.y < CONFIGS.ROCKET_HEIGHT * CONFIGS.TKTURTLE_DX / 2:
self.dx = -self.dx
make_sound(4)
# reach from the left or right sides
if self.x + self.dx < -CONFIGS.WIDTH / 2 or self.x + self.dx > CONFIGS.WIDTH / 2:
make_sound(3)
for obs in self.obstacles:
if self.x * obs.x < 0:
obs.score += 1 # give a score ball
self.dx = -self.dx
self.printscore() # update a score
# return a ball to a start position
self.x = 0
self.y = 0
# reflection from the upper and bottom sides
if self.y + self.dy < -CONFIGS.HEIGHT / 2 or self.y + self.dy > CONFIGS.HEIGHT / 2:
make_sound(1)
self.dy = -self.dy
def __init__(self, task_settings, user_settings, debug=False, fmake=True):
self.DEBUG = debug
make = True
self.IBLRIG_FOLDER = 'C:\\iblrig'
self.IBLRIG_DATA_FOLDER = None # ..\\iblrig_data if None
# =====================================================================
# IMPORT task_settings, user_settings, and SessionPathCreator params
# =====================================================================
ts = {
i: task_settings.__dict__[i]
for i in [x for x in dir(task_settings) if '__' not in x]
}
self.__dict__.update(ts)
us = {
i: user_settings.__dict__[i]
for i in [x for x in dir(user_settings) if '__' not in x]
}
self.__dict__.update(us)
self = iotasks.deserialize_pybpod_user_settings(self)
spc = SessionPathCreator(self.IBLRIG_FOLDER,
self.IBLRIG_DATA_FOLDER,
self.PYBPOD_SUBJECTS[0],
protocol=self.PYBPOD_PROTOCOL,
board=self.PYBPOD_BOARD,
make=make)
self.__dict__.update(spc.__dict__)
# =====================================================================
# SETTINGS
# =====================================================================
self.RECORD_SOUND = True
self.RECORD_AMBIENT_SENSOR_DATA = True
self.RECORD_VIDEO = True
self.OPEN_CAMERA_VIEW = True # Always True if RECORD_VIDEO is True
self.NTRIALS = 2000 # Number of trials for the current session
self.USE_AUTOMATIC_STOPPING_CRITERIONS = True # Weather to check for the Automatic stopping criterions or not # noqa
self.REPEAT_ON_ERROR = False # not used
self.INTERACTIVE_DELAY = 0.0
self.RESPONSE_WINDOW = 60
self.ITI_CORRECT = 1
self.ITI_ERROR = 2
self.CONTRAST_SET = [1., 0.25, 0.125, 0.0625, 0.] # Full contrast set
self.CONTRAST_SET_PROBABILITY_TYPE = 'biased'
self.STIM_FREQ = 0.10 # Probably constant - NOT IN USE
self.STIM_ANGLE = 0. # Vertical orientation of Gabor patch
self.STIM_SIGMA = 7. # (azimuth_degree) Size of Gabor patch
self.STIM_GAIN = 4. # (azimuth_degree/mm) Gain of the RE
# =====================================================================
# SUBJECT
# =====================================================================
self.SUBJECT_WEIGHT = self.get_subject_weight()
self.POOP_COUNT = True
# =====================================================================
# OSC CLIENT
# =====================================================================
self.OSC_CLIENT_PORT = 7110
self.OSC_CLIENT_IP = '127.0.0.1'
self.OSC_CLIENT = udp_client.SimpleUDPClient(self.OSC_CLIENT_IP,
self.OSC_CLIENT_PORT)
# =====================================================================
# PREVIOUS DATA FILES
# =====================================================================
self.LAST_TRIAL_DATA = iotasks.load_data(self.PREVIOUS_SESSION_PATH)
self.LAST_SETTINGS_DATA = iotasks.load_settings(
self.PREVIOUS_SESSION_PATH)
self.SESSION_ORDER = []
self.SESSION_IDX = None
self = iotasks.load_session_order_and_idx(self)
# Load from file
self.POSITIONS = None
self.CONTRASTS = None
self.QUIESCENT_PERIOD = None
self.STIM_PHASE = None
self.LEN_BLOCKS = None
self = iotasks.load_session_pcqs(self)
# =====================================================================
# ADAPTIVE STUFF
# =====================================================================
self.AUTOMATIC_CALIBRATION = True
self.CALIBRATION_VALUE = 0.067
self.REWARD_AMOUNT = 3.
self.REWARD_TYPE = 'Water 10% Sucrose'
self.CALIB_FUNC = adaptive.init_calib_func(self)
self.CALIB_FUNC_RANGE = adaptive.init_calib_func_range(self)
self.REWARD_VALVE_TIME = adaptive.init_reward_valve_time(self)
# =====================================================================
# ROTARY ENCODER
# =====================================================================
self.STIM_POSITIONS = [-35, 35] # All possible positions (deg)
self.QUIESCENCE_THRESHOLDS = [-2, 2] # degree
self.ALL_THRESHOLDS = (self.STIM_POSITIONS +
self.QUIESCENCE_THRESHOLDS)
self.ROTARY_ENCODER = MyRotaryEncoder(self.ALL_THRESHOLDS,
self.STIM_GAIN,
self.COM['ROTARY_ENCODER'])
# =====================================================================
# SOUNDS
# =====================================================================
self.SOFT_SOUND = None
self.SOUND_SAMPLE_FREQ = sound.sound_sample_freq(self.SOFT_SOUND)
self.SOUND_BOARD_BPOD_PORT = 'Serial3'
self.WHITE_NOISE_DURATION = float(0.5)
self.WHITE_NOISE_AMPLITUDE = float(0.05)
self.GO_TONE_DURATION = float(0.1)
self.GO_TONE_FREQUENCY = int(5000)
self.GO_TONE_AMPLITUDE = float(0.1)
self.SD = sound.configure_sounddevice(
output=self.SOFT_SOUND, samplerate=self.SOUND_SAMPLE_FREQ)
# Create sounds and output actions of state machine
self.GO_TONE = sound.make_sound(rate=self.SOUND_SAMPLE_FREQ,
frequency=self.GO_TONE_FREQUENCY,
duration=self.GO_TONE_DURATION,
amplitude=self.GO_TONE_AMPLITUDE,
fade=0.01,
chans='stereo')
self.WHITE_NOISE = sound.make_sound(
rate=self.SOUND_SAMPLE_FREQ,
frequency=-1,
duration=self.WHITE_NOISE_DURATION,
amplitude=self.WHITE_NOISE_AMPLITUDE,
fade=0.01,
chans='stereo')
self.GO_TONE_IDX = 2
self.WHITE_NOISE_IDX = 3
sound.configure_sound_card(
sounds=[self.GO_TONE, self.WHITE_NOISE],
indexes=[self.GO_TONE_IDX, self.WHITE_NOISE_IDX],
sample_rate=self.SOUND_SAMPLE_FREQ)
# =====================================================================
# VISUAL STIM
# =====================================================================
self.SYNC_SQUARE_X = 0.95
self.SYNC_SQUARE_Y = 0.17
self.USE_VISUAL_STIMULUS = True # Run the visual stim in bonsai
self.BONSAI_EDITOR = False # Open the Bonsai editor of visual stim
bonsai.start_visual_stim(self)
self.get_recording_site_data()
# =====================================================================
# SAVE SETTINGS FILE AND TASK CODE
# =====================================================================
if not self.DEBUG:
iotasks.save_session_settings(self)
iotasks.copy_task_code(self)
iotasks.save_task_code(self)
self.bpod_lights(0)
self.display_logs()