def __init__(self, lifespan=None):
"""
Initialize the world.
Parameters
----------
lifespan : int
The number of time steps to continue the world.
"""
BaseWorld.__init__(self, lifespan)
self.reward_magnitude = 1.
self.energy_cost = 0.05 * self.reward_magnitude
self.jump_fraction = 0.1
self.name = 'grid_2D'
self.name_long = 'two dimensional grid world'
print "Entering", self.name_long
self.num_actions = 8
self.world_size = 5
self.num_sensors = self.world_size**2
self.world_state = np.array([1., 1.])
# Reward positions (2,2) and (4,4)
self.targets = [(1, 1), (3, 3)]
# Punish positions (2,4) and (4,2)
self.obstacles = [(1, 3), (3, 1)]
self.world_visualize_period = 1e6
self.brain_visualize_period = 1e3
def __init__(self, lifespan=None):
"""
Initialize the world.
Parameters
----------
lifespan : int
The number of time steps to continue the world.
"""
BaseWorld.__init__(self, lifespan)
self.reward_magnitude = 1.
self.energy_cost = 0.05 * self.reward_magnitude
self.jump_fraction = 0.1
self.name = 'grid_2D'
self.name_long = 'two dimensional grid world'
print "Entering", self.name_long
self.num_actions = 8
self.world_size = 5
self.num_sensors = self.world_size ** 2
self.world_state = np.array([1., 1.])
# Reward positions (2,2) and (4,4)
self.targets = [(1,1), (3,3)]
# Punish positions (2,4) and (4,2)
self.obstacles = [(1,3), (3,1)]
self.world_visualize_period = 1e6
self.brain_visualize_period = 1e3
def __init__(self, lifespan=None, test=False):
"""
Set up the world.
Parameters
----------
lifespan : int
The number of time steps to continue the world.
"""
BaseWorld.__init__(self, lifespan)
self.reward_magnitude = 1.
self.jump_fraction = .05
self.name = 'image_2D'
self.name_long = 'two dimensional visual world'
print "Entering", self.name_long
self.fov_span = 5
# Initialize the image_data to be used as the environment
self.image_filename = os.path.join(mod_path, 'images',
'block_test.png')
self.image_data = plt.imread(self.image_filename)
# Convert it to grayscale if it's in color
if self.image_data.shape[2] == 3:
# Collapse the three RGB matrices into one b/w value matrix
self.image_data = np.sum(self.image_data, axis=2) / 3.0
# Define the size of the field of view, its range of
# allowable positions, and its initial position.
(im_height, im_width) = self.image_data.shape
im_size = np.minimum(im_height, im_width)
self.max_step_size = im_size / 2
self.target_column = im_width / 2
self.target_row = im_height / 2
self.reward_region_width = im_size / 8
self.noise_magnitude = 0.1
self.fov_fraction = 0.5
self.fov_height = im_size * self.fov_fraction
self.fov_width = self.fov_height
self.column_min = np.ceil(self.fov_width / 2)
self.column_max = np.floor(im_width - self.column_min)
self.row_min = np.ceil(self.fov_height / 2)
self.row_max = np.floor(im_height - self.row_min)
self.column_position = np.random.random_integers(self.column_min,
self.column_max)
self.row_position = np.random.random_integers(self.row_min,
self.row_max)
self.num_sensors = 2 * self.fov_span ** 2
self.num_actions = 16
self.sensors = np.zeros(self.num_sensors)
self.column_history = []
self.row_history = []
self.world_visualize_period = 1e3
self.brain_visualize_period = 1e3
self.print_features = False
def __init__(self, lifespan=None, test=False):
"""
Set up the world
"""
BaseWorld.__init__(self, lifespan)
self.REWARD_MAGNITUDE = 1.
self.JUMP_FRACTION = .1
self.print_feature_set = True
self.animate = False
self.name = 'image_2D'
self.name_long = 'two dimensional visual world'
print "Entering", self.name_long
self.fov_span = 10
# Initialize the block_image_data to be used as the environment
self.block_image_filename = os.path.join(mod_path, 'images',
'block_test.png')
self.block_image_data = plt.imread(self.block_image_filename)
# Convert it to grayscale if it's in color
if self.block_image_data.shape[2] == 3:
# Collapse the three RGB matrices into one b/w value matrix
self.block_image_data = np.sum(self.block_image_data, axis=2) / 3.0
# Define the size of the field of view, its range of
# allowable positions, and its initial position.
(im_height, im_width) = self.block_image_data.shape
im_size = np.minimum(im_height, im_width)
self.MAX_STEP_SIZE = im_size / 2
self.TARGET_COLUMN = im_width / 2
self.TARGET_ROW = im_height / 2
self.REWARD_REGION_WIDTH = im_size / 8
self.NOISE_MAGNITUDE = 0.1
self.FIELD_OF_VIEW_FRACTION = 0.5
self.fov_height = im_size * self.FIELD_OF_VIEW_FRACTION
self.fov_width = self.fov_height
self.column_min = np.ceil(self.fov_width / 2)
self.column_max = np.floor(im_width - self.column_min)
self.row_min = np.ceil(self.fov_height / 2)
self.row_max = np.floor(im_height - self.row_min)
self.column_position = np.random.random_integers(self.column_min,
self.column_max)
self.row_position = np.random.random_integers(self.row_min,
self.row_max)
self.num_sensors = 2 * self.fov_span ** 2
self.num_actions = 16
self.sensors = np.zeros(self.num_sensors)
self.column_history = []
self.row_history = []
self.last_feature_vizualized = 0
self.step_counter = 0
self.world_visualize_period = 1e6
self.brain_visualize_period = 1e3
def __init__(self, lifespan=None, test=False):
BaseWorld.__init__(self, lifespan)
self.REWARD_MAGNITUDE = 1.0
self.ENERGY_COST = self.REWARD_MAGNITUDE / 100.0
self.JUMP_FRACTION = 0.1
self.name = "grid_1D"
self.name_long = "one dimensional grid world"
print "Entering", self.name_long
self.num_sensors = 9
self.num_actions = 8
self.action = np.zeros((self.num_actions, 1))
self.world_state = 0
self.simple_state = 0
self.world_visualize_period = 1e6
self.brain_visualize_period = 1e3
def __init__(self, lifespan=None):
BaseWorld.__init__(self, lifespan)
self.VISUALIZE_PERIOD = 10 ** 4
self.REWARD_MAGNITUDE = 1.
self.ENERGY_COST = self.REWARD_MAGNITUDE / 100.
self.JUMP_FRACTION = 0.1
self.name = 'grid_1D'
self.name_long = 'one dimensional grid world'
print "Entering", self.name_long
self.num_sensors = 9
self.num_actions = 8
self.action = np.zeros((self.num_actions,1))
self.world_state = 0
self.simple_state = 0
self.display_state = False
def __init__(self, lifespan=None):
BaseWorld.__init__(self, lifespan)
self.VISUALIZE_PERIOD = 10**4
self.REWARD_MAGNITUDE = 100.
self.ENERGY_COST = self.REWARD_MAGNITUDE / 100.
self.JUMP_FRACTION = 0.1
self.name = 'grid_1D'
self.name_long = 'one dimensional grid world'
print "Entering", self.name_long
self.num_sensors = 9
self.num_actions = 9
self.action = np.zeros((self.num_actions, 1))
self.world_state = 0
self.simple_state = 0
self.display_state = False
def __init__(self, lifespan=None):
"""
Set up the world
Parameters
----------
lifespan : int
The number of time steps to continue the world.
"""
BaseWorld.__init__(self, lifespan)
self.reward_magnitude = 1.
self.jump_fraction = .1
self.step_cost = .1 * self.reward_magnitude
self.name_long = 'one dimensional visual world'
self.name = 'image_1D'
print "Entering", self.name_long
self.fov_span = 5
self.num_sensors = 2 * self.fov_span**2
self.num_actions = 8
self.world_visualize_period = 1e3
self.brain_visualize_period = 1e3
self.print_features = False
# Initialize the image to be used as the environment
self.image_filename = os.path.join(mod_path, 'images', 'bar_test.png')
self.data = plt.imread(self.image_filename)
# Convert it to grayscale if it's in color
if self.data.shape[2] == 3:
# Collapse the three RGB matrices into one b/w value matrix
self.data = np.sum(self.data, axis=2) / 3.0
# Define the size of the field of view, its range of
# allowable positions, and its initial position.
image_width = self.data.shape[1]
self.max_step_size = image_width / 2
self.target_column = image_width / 2
self.reward_region_width = image_width / 8
self.noise_magnitude = 0.1
self.column_history = []
self.fov_height = np.min(self.data.shape)
self.fov_width = self.fov_height
self.column_min = np.ceil(self.fov_width / 2)
self.column_max = np.floor(self.data.shape[1] - self.column_min)
self.column_position = np.random.random_integers(
self.column_min, self.column_max)
self.block_width = self.fov_width / (self.fov_span + 2)
self.sensors = np.zeros(self.num_sensors)
def __init__(self, lifespan=None):
BaseWorld.__init__(self, lifespan)
self.VISUALIZE_PERIOD = 10**4
self.REWARD_MAGNITUDE = 100.
self.JUMP_FRACTION = 1. / 10.
self.print_feature_set = True
self.animate = False
self.name = 'image_2D'
self.name_long = 'two dimensional visual world'
print "Entering", self.name_long
self.fov_span = 10
# Initialize the block_image_data to be used as the environment
self.block_image_filename = os.path.join(mod_path, 'images',
'block_test.png')
self.block_image_data = plt.imread(self.block_image_filename)
# Convert it to grayscale if it's in color
if self.block_image_data.shape[2] == 3:
# Collapse the three RGB matrices into one b/w value matrix
self.block_image_data = np.sum(self.block_image_data, axis=2) / 3.0
# Define the size of the field of view, its range of
# allowable positions, and its initial position.
(im_height, im_width) = self.block_image_data.shape
im_size = np.minimum(im_height, im_width)
self.MAX_STEP_SIZE = im_size / 2
self.TARGET_COLUMN = im_width / 2
self.TARGET_ROW = im_height / 2
self.REWARD_REGION_WIDTH = im_size / 8
self.NOISE_MAGNITUDE = 0.1
self.FIELD_OF_VIEW_FRACTION = 0.5
self.fov_height = im_size * self.FIELD_OF_VIEW_FRACTION
self.fov_width = self.fov_height
self.column_min = np.ceil(self.fov_width / 2)
self.column_max = np.floor(im_width - self.column_min)
self.row_min = np.ceil(self.fov_height / 2)
self.row_max = np.floor(im_height - self.row_min)
self.column_position = np.random.random_integers(
self.column_min, self.column_max)
self.row_position = np.random.random_integers(self.row_min,
self.row_max)
self.num_sensors = 2 * self.fov_span**2
self.num_actions = 17
self.sensors = np.zeros(self.num_sensors)
self.column_history = []
self.row_history = []
self.last_feature_vizualized = 0
self.step_counter = 0
def __init__(self, lifespan=None, test=False):
"""
Set up the world
"""
BaseWorld.__init__(self, lifespan)
self.REWARD_MAGNITUDE = 1.
self.JUMP_FRACTION = 1. / 10.
self.STEP_COST = 0.1 * self.REWARD_MAGNITUDE
self.print_feature_set = True
self.animate = False
self.graphing = True
self.name_long = 'one dimensional visual world'
self.name = 'image_1D'
print "Entering", self.name_long
self.step_counter = 0
self.fov_span = 7
self.num_sensors = 2 * self.fov_span ** 2
self.num_actions = 8
self.world_visualize_period = 1e6
self.brain_visualize_period = 1e3
# Initialize the image to be used as the environment
self.block_image_filename = os.path.join(mod_path, 'images',
'bar_test.png')
self.data = plt.imread(self.block_image_filename)
# Convert it to grayscale if it's in color
if self.data.shape[2] == 3:
# Collapse the three RGB matrices into one b/w value matrix
self.data = np.sum(self.data, axis=2) / 3.0
# Define the size of the field of view, its range of
# allowable positions, and its initial position.
image_width = self.data.shape[1]
self.MAX_STEP_SIZE = image_width / 2
self.TARGET_COLUMN = image_width / 2
self.REWARD_REGION_WIDTH = image_width / 8
self.NOISE_MAGNITUDE = 0.1
self.column_history = []
self.fov_height = np.min(self.data.shape)
self.fov_width = self.fov_height
self.column_min = np.ceil(self.fov_width / 2)
self.column_max = np.floor(self.data.shape[1] - self.column_min)
self.column_position = np.random.random_integers(self.column_min,
self.column_max)
self.block_width = self.fov_width / (self.fov_span + 2)
self.sensors = np.zeros(self.num_sensors)
def __init__(self, lifespan=None):
BaseWorld.__init__(self, lifespan)
self.VISUALIZE_PERIOD = 10 ** 4
self.REWARD_MAGNITUDE = 100.
self.ENERGY_COST = 0.05 * self.REWARD_MAGNITUDE
self.JUMP_FRACTION = 0.1
self.display_state = False
self.name = 'grid_2D'
self.name_long = 'two dimensional grid world'
print "Entering", self.name_long
self.num_actions = 9
self.world_size = 5
self.num_sensors = self.world_size ** 2
self.world_state = np.array([1, 1])
# Reward positions (2,2) and (4,4)
self.targets = [(1,1), (3,3)]
# Punish positions (2,4) and (4,2)
self.obstacles = [(1,3), (3,1)]
def __init__(self, lifespan=None):
BaseWorld.__init__(self, lifespan)
self.VISUALIZE_PERIOD = 10**4
self.REWARD_MAGNITUDE = 100.
self.ENERGY_COST = 0.05 * self.REWARD_MAGNITUDE
self.JUMP_FRACTION = 0.1
self.display_state = False
self.name = 'grid_2D'
self.name_long = 'two dimensional grid world'
print "Entering", self.name_long
self.num_actions = 9
self.world_size = 5
self.num_sensors = self.world_size**2
self.world_state = np.array([1, 1])
# Reward positions (2,2) and (4,4)
self.targets = [(1, 1), (3, 3)]
# Punish positions (2,4) and (4,2)
self.obstacles = [(1, 3), (3, 1)]
def __init__(self, lifespan=None):
""" Set up the world """
BaseWorld.__init__(self, lifespan)
self.VISUALIZE_PERIOD = 10 ** 4
self.REWARD_MAGNITUDE = 100.
self.ENERGY_COST = 0.01 * self.REWARD_MAGNITUDE
self.JUMP_FRACTION = 0.1
self.display_state = True
self.name = 'grid_1D_noise'
self.name_long = 'noisy one dimensional grid world'
print "Entering", self.name_long
self.num_real_sensors = 3
# Number of sensors that have no basis in the world.
# These are noise meant to distract.
self.num_noise_sensors = 15
self.num_sensors = self.num_noise_sensors + self.num_real_sensors
self.num_actions = 3
self.action = np.zeros((self.num_actions,1))
self.world_state = 0
self.simple_state = 0
def __init__(self, lifespan=None, test=False):
"""
Set up the world
"""
BaseWorld.__init__(self, lifespan)
self.reward_magnitude = 1.
self.energy_cost = 0.01 * self.reward_magnitude
self.jump_fraction = 0.1
self.name = 'grid_1D_noise'
self.name_long = 'noisy one dimensional grid world'
print "Entering", self.name_long
self.num_real_sensors = 3
# Number of sensors that have no basis in the world.
# These are noise meant to distract.
self.num_noise_sensors = 15
self.num_sensors = self.num_noise_sensors + self.num_real_sensors
self.num_actions = 2
self.action = np.zeros((self.num_actions, 1))
self.world_state = 0
self.simple_state = 0
self.world_visualize_period = 1e6
self.brain_visualize_period = 1e3
def __init__(self, lifespan=None, test=False):
"""
Set up the world
"""
BaseWorld.__init__(self, lifespan)
self.reward_magnitude = 1.0
self.energy_cost = 0.01 * self.reward_magnitude
self.jump_fraction = 0.1
self.name = "grid_1D_noise"
self.name_long = "noisy one dimensional grid world"
print "Entering", self.name_long
self.num_real_sensors = 3
# Number of sensors that have no basis in the world.
# These are noise meant to distract.
self.num_noise_sensors = 15
self.num_sensors = self.num_noise_sensors + self.num_real_sensors
self.num_actions = 2
self.action = np.zeros((self.num_actions, 1))
self.world_state = 0
self.simple_state = 0
self.world_visualize_period = 1e6
self.brain_visualize_period = 1e3
def __init__(self, lifespan=None, test=False):
"""
Set up the world
"""
BaseWorld.__init__(self, lifespan)
self.REWARD_MAGNITUDE = 1.
self.ENERGY_COST = 0.01 * self.REWARD_MAGNITUDE
self.JUMP_FRACTION = 0.1
self.name = 'grid_1D_noise'
self.name_long = 'noisy one dimensional grid world'
print "Entering", self.name_long
self.num_real_sensors = 3
# Number of sensors that have no basis in the world.
# These are noise meant to distract.
self.num_noise_sensors = 15
self.num_sensors = self.num_noise_sensors + self.num_real_sensors
self.num_actions = 2
self.action = np.zeros((self.num_actions,1))
self.world_state = 0
self.simple_state = 0
self.world_visualize_period = 1e6
self.brain_visualize_period = 1e3
def __init__(self, lifespan=None):
"""
Initialize the world.
Parameters
----------
lifespan : int
The number of time steps to continue the world.
"""
BaseWorld.__init__(self, lifespan)
self.reward_magnitude = 1.
self.energy_cost = self.reward_magnitude / 1e10
self.name = 'grid_1D_chase'
self.name_long = 'one dimensional chase grid world'
print "Entering", self.name_long
self.size = 5
self.num_sensors = self.size + 2 * (self.size - 1)
self.num_actions = 2 * (self.size - 1)
self.action = np.zeros(self.num_actions)
self.position = 2
self.target_position = 1
self.world_visualize_period = 1e3
self.brain_visualize_period = 1e3
def __init__(self, lifespan=None):
"""
Initialize the world.
Parameters
----------
lifespan : int
The number of time steps to continue the world.
"""
BaseWorld.__init__(self, lifespan)
self.reward_magnitude = 1.
self.energy_cost = self.reward_magnitude / 1e10
self.name = 'grid_1D_chase'
self.name_long = 'one dimensional chase grid world'
print "Entering", self.name_long
self.size = 5
self.num_sensors = self.size + 2 * (self.size - 1)
self.num_actions = 2 * (self.size - 1)
self.action = np.zeros(self.num_actions)
self.position = 2
self.target_position = 1
self.world_visualize_period = 1e3
self.brain_visualize_period = 1e3
def __init__(self, lifespan=None):
"""
Initialize the world.
Parameters
----------
lifespan : int
The number of time steps to continue the world.
"""
BaseWorld.__init__(self, lifespan)
self.reward_magnitude = 1.
self.energy_cost = self.reward_magnitude / 100.
self.jump_fraction = 0.1
self.name = 'grid_1D'
self.name_long = 'one dimensional grid world'
print "Entering", self.name_long
self.num_sensors = 9
self.num_actions = 8
self.action = np.zeros(self.num_actions)
self.world_state = 0
self.simple_state = 0
self.world_visualize_period = 1e6
self.brain_visualize_period = 1e3
def __init__(self, lifespan=None):
"""
Initialize the world.
Parameters
----------
lifespan : int
The number of time steps to continue the world.
"""
BaseWorld.__init__(self, lifespan)
self.reward_magnitude = 1.
self.energy_cost = self.reward_magnitude / 100.
self.jump_fraction = 0.1
self.name = 'grid_1D'
self.name_long = 'one dimensional grid world'
print "Entering", self.name_long
self.num_sensors = 9
self.num_actions = 8
self.action = np.zeros(self.num_actions)
self.world_state = 0
self.simple_state = 0
self.world_visualize_period = 1e6
self.brain_visualize_period = 1e3