def state_initializer():
"""State initializer method to be fed into environment."""
# Get targets and covers
sprite_positions = 0.5 + 0.35 * _get_polygon(num_targets, 0.7)
target_factors = [
target_factor_distrib.sample() for _ in range(num_targets)
]
targets = [
sprite.Sprite(x=pos[0], y=pos[1], **factors)
for pos, factors in zip(sprite_positions, target_factors)
]
covers = [
sprite.Sprite(x=pos[0], y=pos[1], **cover_factors)
for pos in sprite_positions
]
# Tag the cover metadata based on whether they are prey or not
for i, s in enumerate(covers):
if i == 0:
s.metadata = {'prey': True}
else:
s.metadata = {'prey': False}
# Make cue have the same factors as the first target, except slightly
# smaller
cue_factors = copy.deepcopy(target_factors[0])
cue_factors['scale'] = 0.7 * target_factors[0]['scale']
cue = sprite.Sprite(x=0.5,
y=0.501,
opacity=0,
mass=np.inf,
**cue_factors)
agent = sprite.Sprite(x=0.5,
y=0.5,
shape='circle',
scale=0.1,
c0=0.4,
c1=0.,
c2=1.,
mass=np.inf)
annulus_verts = shapes.annulus_vertices(0.34, 0.36)
annulus = sprite.Sprite(x=0.5,
y=0.5,
shape=annulus_verts,
scale=1.,
c0=0.,
c1=0.,
c2=0.3)
state = collections.OrderedDict([
('annulus', [annulus]),
('targets', targets),
('covers', covers),
('agent', [agent]),
('cue', [cue]),
('screen', [screen]),
])
return state
def _generate(disjoint=False, without_overlapping=[]):
"""Return a list of sprites.
Args:
disjoint: Boolean. If true, all generated sprites will be disjoint.
without_overlapping: Optional iterable of ../sprite/Sprite
instances. If specified, all generated sprites will not overlap
any sprites in without_overlapping.
"""
n = num_sprites() if callable(num_sprites) else num_sprites
sprites = []
for _ in range(n):
s = sprite.Sprite(**factor_dist.sample())
count = 0
while _overlaps(s, without_overlapping):
if count > max_recursion_depth:
if fail_gracefully:
return sprites
else:
raise RecursionError(
'max_recursion_depth exceeded trying to initialize '
'a non-overlapping sprite.')
count += 1
s = sprite.Sprite(**factor_dist.sample())
sprites.append(s)
if disjoint:
without_overlapping = without_overlapping + [s]
return sprites
def get_state():
"""Get initial state."""
sprite_0 = sprite.Sprite(x=0.5,
y=0.7,
scale=0.1,
shape='triangle',
x_vel=0.04,
y_vel=-0.02,
c0=255,
angle=2.)
sprite_1 = sprite.Sprite(x=0.2,
y=0.6,
scale=0.1,
shape='triangle',
x_vel=0.,
y_vel=0.,
c1=255,
angle=1.)
sprite_2 = sprite.Sprite(x=0.6,
y=0.3,
scale=0.1,
shape='triangle',
x_vel=0.,
y_vel=0.,
c2=255)
sprites = [sprite_0, sprite_1, sprite_2]
walls = shapes.border_walls(visible_thickness=0.05, c0=128, c1=128, c2=128)
state = collections.OrderedDict([('walls', walls), ('sprites', sprites)])
return state
def state_initializer():
agent = sprite.Sprite(x=0.5,
y=0.5,
shape='circle',
scale=0.04,
c0=0.33,
c1=1.,
c2=0.66)
annulus_shape = shapes.annulus_vertices(0.15, 2.)
agent_annulus = sprite.Sprite(x=0.5,
y=0.5,
shape=annulus_shape,
scale=1.,
c0=0.6,
c1=1.,
c2=1.)
prey = get_prey(
get_parallelogram(min_axis_ratio=0.5),
scale=0.4,
max_vel=max_vel,
sprite_scale=0.075,
)
state = collections.OrderedDict([
('grid', grid),
('prey', prey),
('agent', [agent]),
('agent_annulus', [agent_annulus]),
])
return state
def to_sprites(self, **color):
"""Turn a maze matrix into a list of sprites.
Each sprite is a single square for one brick in the maze walls.
Args:
color: Dict. May contain keys {'c0', 'c1', 'c2'} for the wall sprite
color factors.
Returns:
sprites: List of maze wall sprites.
"""
maze_size = self.maze_size
vertex_linspace = np.linspace(0., 1., maze_size + 1)
sprites = []
for x in range(maze_size):
for y in range(maze_size):
if self.maze[y, x]:
shape = np.array([
[vertex_linspace[x], vertex_linspace[y]],
[vertex_linspace[x], vertex_linspace[y + 1]],
[vertex_linspace[x + 1], vertex_linspace[y + 1]],
[vertex_linspace[x + 1], vertex_linspace[y]],
])
new_sprite = sprite.Sprite(x=0.,
y=0.,
shape=shape,
**color)
sprites.append(new_sprite)
return sprites
def get_prey(centered_vertices, scale=1., max_vel=0., sprite_scale=0.1):
"""Get prey sprites.
Args:
centered_vertices: Numpy array of shape [num_vertices, 2] containing
vertex positions.
scale: Re-scaling factor of centered_vertices for the global space.
max_vel: Maximum velocity of the sprites.
sprite_scale: Re-scaling factor of centered_vertices for the individual
sprite shapes.
"""
sprite_shape = sprite_scale * centered_vertices
sprite_positions = scale * centered_vertices
sprite_positions += np.array([0.5, 0.5]) - sprite_positions[0]
# We sample each sprite's velocity independently so that the entire tethered
# configuration may rotate
prey = [
sprite.Sprite(x=pos[0],
y=pos[1],
shape=sprite_shape,
scale=1.,
x_vel=np.random.uniform(-1 * max_vel, max_vel),
y_vel=np.random.uniform(-1 * max_vel, max_vel),
c0=0.2,
c1=1.,
c2=1.) for pos in sprite_positions
]
return prey
def _create_new_sprite(sprite_kwargs, vertices=None):
"""Create new sprite from factors.
Args:
sprite_kwargs: Dict. Keyword arguments for sprite_lib.Sprite.__init__().
All of the strings in sprite_lib.Sprite.FACTOR_NAMES must be keys of
sprite_kwargs.
vertices: Optional numpy array of vertices. If provided, are used to
define the shape of the sprite. Otherwise, sprite_kwargs['shape'] is
used.
Returns:
Instance of sprite_lib.Sprite.
"""
if vertices is not None:
# Have vertices, so must invert the translation, rotation, and
# scaling transformations to get the original sprite shape.
center_translate = mpl_transforms.Affine2D().translate(
-sprite_kwargs['x'], -sprite_kwargs['y'])
x_y_scale = 1. / np.array([
sprite_kwargs['scale'],
sprite_kwargs['scale'] * sprite_kwargs['aspect_ratio']
])
transform = (
center_translate +
mpl_transforms.Affine2D().rotate(-sprite_kwargs['angle']) +
mpl_transforms.Affine2D().scale(*x_y_scale)
)
vertices = mpl_path.Path(vertices)
vertices = transform.transform_path(vertices).vertices
sprite_kwargs['shape'] = vertices
return sprite_lib.Sprite(**sprite_kwargs)
def state_initializer():
agent = sprite.Sprite(x=0.5,
y=0.1,
shape='square',
aspect_ratio=0.2,
scale=0.1,
c0=0.33,
c1=1.,
c2=0.66)
state = collections.OrderedDict([
('walls', walls),
('prey', [sprite.Sprite(**prey_factors.sample())]),
('agent', [agent]),
('occluder', [occluder]),
])
return state
def state_initializer():
"""Callable returning state at every episode reset."""
agent = sprite.Sprite(**agent_factors.sample())
predators = predator_generator(without_overlapping=(agent,))
prey = prey_generator(without_overlapping=(agent,))
state = collections.OrderedDict([
('prey', prey),
('predators', predators),
('agent', [agent]),
])
return state
def state_initializer():
maze = maze_lib.generate_random_maze_matrix(size=maze_size,
ambient_size=12)
maze = maze_lib.Maze(np.flip(maze, axis=0))
walls = maze.to_sprites(c0=0., c1=0., c2=0.8)
# Sample positions in maze grid of agent and ghosts
n_ghosts = num_ghosts()
points = maze.sample_distinct_open_points(1 + n_ghosts)
positions = [maze.grid_side * (0.5 + np.array(x)) for x in points]
# Agent
agent_position = positions[0]
agent = [
sprite.Sprite(x=agent_position[1],
y=agent_position[0],
**agent_factors)
]
# ghosts
ghosts = []
for i in range(n_ghosts):
position = positions[i + 1]
ghosts.append(
sprite.Sprite(x=position[1], y=position[0], **ghost_factors))
# Place prey at every open maze location
prey = []
open_maze_points = np.argwhere(maze.maze == 0)
for p in open_maze_points:
pos = maze.grid_side * (0.5 + np.array(p))
prey.append(sprite.Sprite(x=pos[1], y=pos[0], **prey_factors))
state = collections.OrderedDict([
('walls', walls),
('prey', prey),
('ghosts', ghosts),
('agent', agent),
])
return state
def state_initializer():
"""Callable returning state ordereddict each episode reset."""
agent = sprite.Sprite(x=0.5,
y=0.05,
scale=0.03,
shape='spoke_4',
c0=255,
c1=255,
c2=255)
target_0 = sprite.Sprite(y=1.4, **target_factors.sample())
target_1 = sprite.Sprite(y=np.random.uniform(1.7, 2.4),
**target_factors.sample())
screen = sprite.Sprite(x=0.5,
y=0.5,
shape='square',
c0=128,
c1=128,
c2=128)
state = collections.OrderedDict([
('targets', [target_0, target_1]),
('occluders', [occluder]),
('walls', walls),
('response_boxes', response_boxes),
('response_tokens', response_tokens),
('agent', [agent]),
('screen', [screen]),
])
# Predict whether targets will contact, putting this information in
# agent metadata
orig_pos = [np.copy(s.position) for s in state['targets']]
orig_vel = [np.copy(s.velocity) for s in state['targets']]
agent.metadata = {'will_contact': _predict_contact(state)}
for s, pos, vel in zip(state['targets'], orig_pos, orig_vel):
s.position = pos
s.velocity = vel
return state
def border_walls(visible_thickness=0.05,
total_thickness=0.5,
c0=0,
c1=0,
c2=0,
opacity=255):
"""Get four sprites forming a border around the [0, 1] x [0, 1] frame.
This can be used to (i) create the visual effect of a border at the edges of
the screen, and/or (ii) create walls around the border that can be used to
contain sprites inside the interior of the screen.
Args:
visible_thickness: Float. How thick the borders within the frame should
be.
total_thickness: Float. How thick the border wall is in total. Depending
on visible_thickness, much of this may lie outside of the frame. As
long as total_thickness is greater than visible_thickness, it is not
important. However, if visible_thickness is very small then it can
be good to have total_thickness non-negligibly greater than zero,
otherwise the wall sprites are extremely narrow and collisions can
be a little unstable since their vertices and centers of mass are
nearly collinear.
c0: Scalar. First coordinate of color of wall sprites.
c1: Scalar. Second coordinate of color of wall sprites.
c2: Scalar. Third coordinate of color of wall sprites.
opacity: Integer in [0, 255]. Opacity of wall sprites.
Returns:
walls: List of four sprites, the walls.
"""
boundary_wall_shape_0 = np.array([
[0., visible_thickness],
[1., visible_thickness],
[1., visible_thickness - total_thickness],
[0., visible_thickness - total_thickness],
])
distance_across_frame = 1 + total_thickness - 2 * visible_thickness
wall_shapes = [
boundary_wall_shape_0,
boundary_wall_shape_0 + np.array([[0., distance_across_frame]]),
np.flip(boundary_wall_shape_0, axis=1),
np.flip(boundary_wall_shape_0, axis=1) +
np.array([[distance_across_frame, 0.]]),
]
sprite_factors = dict(x=0., y=0., c0=c0, c1=c1, c2=c2, opacity=opacity)
walls = [
sprite.Sprite(shape=wall_shape, **sprite_factors)
for wall_shape in wall_shapes
]
return walls
def state_initializer():
fixation = sprite.Sprite(x=0.5,
y=0.5,
shape=cross_shape,
scale=0.1,
c0=0.,
c1=0.,
c2=0.)
screen = sprite.Sprite(x=0.5,
y=0.5,
shape='square',
scale=2.,
c0=0.,
c1=0.,
c2=1.)
agent = sprite.Sprite(x=0.5,
y=0.5,
scale=0.04,
shape=cross_shape,
c0=0.33,
c1=1.,
c2=1.)
occluder_shape = shapes.annulus_vertices(0.13, 2.)
occluder = sprite.Sprite(shape=occluder_shape, **occluder_factors)
targets = [
sprite.Sprite(**target_factors.sample())
for _ in range(num_targets)
]
bar_angles = 0.5 * np.pi * np.random.binomial(1, 0.5, (num_targets))
bars = [
sprite.Sprite(x=s.x,
y=s.y,
x_vel=s.x_vel,
y_vel=s.y_vel,
angle=angle,
**bar_factors)
for s, angle in zip(targets, bar_angles)
]
state = collections.OrderedDict([
('walls', walls),
('targets', targets),
('bars', bars),
('occluder', [occluder]),
('screen', [screen]),
('fixation', [fixation]),
('agent', [agent]),
])
return state
def _state_initializer():
agent = sprite.Sprite(x=0.5, y=0.5, scale=0.1, c0=128)
target = sprite.Sprite(x=0.75, y=0.5, scale=0.1, c1=128)
state = collections.OrderedDict([('agent', [agent]),
('target', [target])])
return state
def get_config(_):
"""Get environment config"""
############################################################################
# Sprite initialization
############################################################################
# Ball generator
ball_factors = distribs.Product(
[
distribs.Continuous('x', 0.25, 0.75),
distribs.Continuous('y', 0.5, 0.9),
distribs.Continuous('x_vel', -0.01, 0.01)
],
scale=0.1,
shape='circle',
c0=0,
c1=0,
c2=255,
mass=1.,
)
ball_generator = sprite_generators.generate_sprites(ball_factors,
num_sprites=4)
# Walls
bottom_wall = [[-1, 0.1], [2, 0.1], [2, -1], [-1, -1]]
left_wall = [[0.05, -0.1], [0.05, 1.1], [-1, 1.1], [-1, -0.1]]
right_wall = [[0.95, -0.1], [0.95, 1.1], [2, 1.1], [2, -0.1]]
divider = [[0.45, -1], [0.45, 0.3], [0.55, 0.3], [0.55, -1]]
walls = [
sprite_lib.Sprite(shape=np.array(v), x=0, y=0, c0=128, c1=128, c2=128)
for v in [bottom_wall, left_wall, right_wall, divider]
]
def state_initializer():
"""Callable returning new state at each episode reset."""
state = collections.OrderedDict([
('walls', walls),
('balls', ball_generator(disjoint=True)),
('agent', []),
])
return state
############################################################################
# Physics
############################################################################
# Setting max_recursion_depth > 0 can increase stability
# Setting update_angle_vel = False is recommended for stability
collision = physics_lib.Collision(
elasticity=0.6,
symmetric=False,
update_angle_vel=False,
max_recursion_depth=2,
)
physics = physics_lib.Physics(
(collision, 'balls', ['balls', 'walls']),
(physics_lib.DownGravity(g=-0.001), 'balls'),
updates_per_env_step=20,
)
############################################################################
# Task
############################################################################
task = tasks.CompositeTask(timeout_steps=100)
############################################################################
# Action space
############################################################################
# Need an action space, so let it control an empty agent layer
action_space = action_spaces.Grid(action_layers='agent')
############################################################################
# Observer
############################################################################
observer = observers.PILRenderer(image_size=(64, 64), anti_aliasing=1)
############################################################################
# Final config
############################################################################
config = {
'state_initializer': state_initializer,
'physics': physics,
'task': task,
'action_space': action_space,
'observers': {
'image': observer
},
'game_rules': (),
}
return config
def get_config(_):
"""Get environment config."""
############################################################################
# Sprite initialization
############################################################################
# Occluder
occluder_shape = np.array([[-0.1, 0.2], [1.1, 0.2], [1.1, 0.6],
[-0.1, 0.6]])
occluder = sprite.Sprite(x=0.,
y=0.,
shape=occluder_shape,
scale=1.,
c0=0.6,
c1=1.,
c2=1.)
# Prey
prey_factors = distribs.Product(
[
distribs.Continuous('x', 0.1, 0.8),
distribs.Continuous('x_vel', -0.01, 0.01)
],
y=1.2,
y_vel=-0.007,
shape='circle',
scale=0.07,
c0=0.2,
c1=1.,
c2=1.,
)
# Walls
left_wall = [[0.05, -0.2], [0.05, 2], [-1, 2], [-1, -0.2]]
right_wall = [[0.95, -0.2], [0.95, 2], [2, 2], [2, -0.2]]
walls = [
sprite.Sprite(shape=np.array(v), x=0, y=0, c0=0., c1=0., c2=0.5)
for v in [left_wall, right_wall]
]
def state_initializer():
agent = sprite.Sprite(x=0.5,
y=0.1,
shape='square',
aspect_ratio=0.2,
scale=0.1,
c0=0.33,
c1=1.,
c2=0.66)
state = collections.OrderedDict([
('walls', walls),
('prey', [sprite.Sprite(**prey_factors.sample())]),
('agent', [agent]),
('occluder', [occluder]),
])
return state
############################################################################
# Physics
############################################################################
agent_friction_force = physics_lib.Drag(coeff_friction=0.25)
asymmetric_collision = physics_lib.Collision(elasticity=1.,
symmetric=False,
update_angle_vel=False)
inelastic_collision = physics_lib.Collision(elasticity=0.,
symmetric=False,
update_angle_vel=False)
physics = physics_lib.Physics(
(agent_friction_force, 'agent'),
(inelastic_collision, 'agent', 'walls'),
(asymmetric_collision, 'prey', 'walls'),
updates_per_env_step=10,
)
############################################################################
# Task
############################################################################
contact_task = tasks.ContactReward(1., layers_0='agent', layers_1='prey')
reset_task = tasks.Reset(
condition=lambda state: all([s.y < 0. for s in state['prey']]),
steps_after_condition=15,
)
task = tasks.CompositeTask(contact_task, reset_task)
############################################################################
# Action space
############################################################################
action_space = action_spaces.Joystick(scaling_factor=0.002,
action_layers='agent',
constrained_lr=True)
############################################################################
# Observer
############################################################################
observer = observers.PILRenderer(image_size=(64, 64),
anti_aliasing=1,
color_to_rgb='hsv_to_rgb')
############################################################################
# Game rules
############################################################################
prey_vanish = game_rules.VanishOnContact(
vanishing_layer='prey',
contacting_layer='agent',
)
rules = (prey_vanish, )
############################################################################
# Final config
############################################################################
config = {
'state_initializer': state_initializer,
'physics': physics,
'task': task,
'action_space': action_space,
'observers': {
'image': observer
},
'game_rules': rules,
}
return config
def _add_sprite(min_x, max_x, min_y, max_y):
shape = np.array([[min_x, min_y], [max_x, min_y], [max_x, max_y],
[min_x, max_y]])
grid_sprites.append(sprite.Sprite(shape=shape, **sprite_factors))
def get_config(_):
"""Get environment config."""
############################################################################
# Sprite initialization
############################################################################
# Agent
agent = sprite.Sprite(x=0.5,
y=0.5,
shape='circle',
scale=0.04,
c0=0.33,
c1=1.,
c2=0.66)
annulus_vertices = shapes.annulus_vertices(inner_radius=0.08,
outer_radius=0.3)
agent_annulus = sprite.Sprite(x=0.5,
y=0.5,
shape=annulus_vertices,
scale=1.,
c0=0.6,
c1=1.,
c2=1.)
# Predator generator
max_predator_vel = 0.02
predator_pos = _get_boundary_pos_distribution(_FIELD_BUFFER)
predator_vel = _get_vel_distribution(0.5 * max_predator_vel,
max_predator_vel)
predator_factors = distribs.Product(
[predator_pos, predator_vel,
distribs.Continuous('scale', 0.07, 0.13)],
shape='circle',
c0=0.,
c1=1.,
c2=0.8,
)
# Prey generator
max_prey_vel = 0.01
prey_pos = _get_boundary_pos_distribution(_FIELD_BUFFER)
prey_vel = _get_vel_distribution(0.5 * max_prey_vel, max_prey_vel)
prey_factors = distribs.Product(
[prey_pos, prey_vel,
distribs.Continuous('scale', 0.07, 0.13)],
shape='circle',
c0=0.2,
c1=1.,
c2=1.,
)
# Grid
grid = shapes.grid_lines(grid_x=_GRID_SIZE,
grid_y=_GRID_SIZE,
buffer_border=1.,
c0=0.,
c1=0.,
c2=0.5)
def state_initializer():
state = collections.OrderedDict([
('grid', grid),
('prey', []),
('agent', [agent]),
('predators', []),
('agent_annulus', [agent_annulus]),
])
return state
############################################################################
# Physics
############################################################################
agent_friction_force = physics_lib.Drag(coeff_friction=0.25)
physics = physics_lib.Physics(
(agent_friction_force, ['agent', 'agent_annulus']),
updates_per_env_step=10,
)
############################################################################
# Task
############################################################################
def _predator_reward_fn(_, predator_sprite):
return -2. * predator_sprite.scale
predator_task = tasks.ContactReward(
reward_fn=_predator_reward_fn,
layers_0='agent',
layers_1='predators',
reset_steps_after_contact=0,
)
def _prey_reward_fn(_, prey_sprite):
return prey_sprite.scale
prey_task = tasks.ContactReward(
reward_fn=_prey_reward_fn,
layers_0='agent',
layers_1='prey',
)
task = tasks.CompositeTask(predator_task, prey_task)
############################################################################
# Action space
############################################################################
action_space = action_spaces.Joystick(
scaling_factor=0.003,
action_layers=('agent', 'agent_annulus'),
constrained_lr=False,
)
############################################################################
# Observer
############################################################################
_polygon_modifier = observers.polygon_modifiers.FirstPersonAgent(
agent_layer='agent')
observer = observers.PILRenderer(
image_size=(64, 64),
anti_aliasing=1,
color_to_rgb='hsv_to_rgb',
polygon_modifier=_polygon_modifier,
)
############################################################################
# Game rules
############################################################################
# Make predators appear randomly
predator_appear_generator = sprite_generators.generate_sprites(
predator_factors, num_sprites=1)
predator_appear = game_rules.ConditionalRule(
condition=lambda state: np.random.binomial(1, p=0.5),
rules=game_rules.CreateSprites('predators', predator_appear_generator),
)
# Make prey appear randomly
prey_appear_generator = sprite_generators.generate_sprites(prey_factors,
num_sprites=1)
prey_appear = game_rules.ConditionalRule(
condition=lambda state: np.random.binomial(1, p=0.2),
rules=game_rules.CreateSprites('prey', prey_appear_generator),
)
# Make predators and prey vanish when they are distant enough and moving
# away.
vanish_range = [-1. * _VANISH_DIST, 1. + _VANISH_DIST]
def _should_vanish(s):
pos_too_small = (s.position < vanish_range[0]) * (s.velocity < 0.)
pos_too_large = (s.position > vanish_range[1]) * (s.velocity > 0.)
return any(pos_too_small) or any(pos_too_large)
predator_vanish = game_rules.VanishByFilter('predators', _should_vanish)
prey_vanish = game_rules.VanishByFilter('prey', _should_vanish)
# Keep agent near center
keep_near_center = game_rules.KeepNearCenter(
agent_layer='agent',
layers_to_center=['agent_annulus', 'predators', 'prey'],
grid_x=_GRID_SIZE,
)
# Make prey vanish when contacted by agent
prey_caught = game_rules.VanishOnContact(vanishing_layer='prey',
contacting_layer='agent')
rules = (predator_appear, prey_appear, prey_vanish, predator_vanish,
keep_near_center, prey_caught)
############################################################################
# Final config
############################################################################
config = {
'state_initializer': state_initializer,
'physics': physics,
'task': task,
'action_space': action_space,
'observers': {
'image': observer
},
'game_rules': rules,
}
return config
def get_config(num_targets):
"""Get environment config.
Args:
num_targets: Int. Number of targets.
"""
if num_targets == 0 or not isinstance(num_targets, int):
raise ValueError(
f'num_targets is {num_targets}, but must be a positive integer')
############################################################################
# State initialization
############################################################################
screen = sprite.Sprite(x=0.5,
y=0.5,
shape='square',
scale=2.,
c0=0.6,
c1=0.7,
c2=0.7)
target_factor_distrib = distribs.Product(
[distribs.Continuous('c0', 0., 1.)],
shape='circle',
scale=0.085,
c1=1.,
c2=1.,
)
cover_factors = dict(mass=0.,
shape='circle',
scale=0.1,
c0=0.,
c1=0.,
c2=0.5,
opacity=0)
def state_initializer():
"""State initializer method to be fed into environment."""
# Get targets and covers
sprite_positions = 0.5 + 0.35 * _get_polygon(num_targets, 0.7)
target_factors = [
target_factor_distrib.sample() for _ in range(num_targets)
]
targets = [
sprite.Sprite(x=pos[0], y=pos[1], **factors)
for pos, factors in zip(sprite_positions, target_factors)
]
covers = [
sprite.Sprite(x=pos[0], y=pos[1], **cover_factors)
for pos in sprite_positions
]
# Tag the cover metadata based on whether they are prey or not
for i, s in enumerate(covers):
if i == 0:
s.metadata = {'prey': True}
else:
s.metadata = {'prey': False}
# Make cue have the same factors as the first target, except slightly
# smaller
cue_factors = copy.deepcopy(target_factors[0])
cue_factors['scale'] = 0.7 * target_factors[0]['scale']
cue = sprite.Sprite(x=0.5,
y=0.501,
opacity=0,
mass=np.inf,
**cue_factors)
agent = sprite.Sprite(x=0.5,
y=0.5,
shape='circle',
scale=0.1,
c0=0.4,
c1=0.,
c2=1.,
mass=np.inf)
annulus_verts = shapes.annulus_vertices(0.34, 0.36)
annulus = sprite.Sprite(x=0.5,
y=0.5,
shape=annulus_verts,
scale=1.,
c0=0.,
c1=0.,
c2=0.3)
state = collections.OrderedDict([
('annulus', [annulus]),
('targets', targets),
('covers', covers),
('agent', [agent]),
('cue', [cue]),
('screen', [screen]),
])
return state
################################################################################
# Physics
################################################################################
drag = (physics_lib.Drag(coeff_friction=0.25), ['agent', 'cue'])
tether_covers = physics_lib.TetherZippedLayers(('targets', 'covers'),
anchor=np.array([0.5, 0.5]))
physics = physics_lib.Physics(
drag,
updates_per_env_step=1,
corrective_physics=[tether_covers],
)
################################################################################
# Task
################################################################################
contact_task = tasks.ContactReward(
reward_fn=lambda _, s: 1 if s.metadata['prey'] else -1,
layers_0='agent',
layers_1='covers',
)
def _should_reset(state, meta_state):
should_reset = (state['covers'][0].opacity == 0
and meta_state['phase'] == 'response')
return should_reset
reset_task = tasks.Reset(
condition=_should_reset,
steps_after_condition=15,
)
task = tasks.CompositeTask(contact_task, reset_task, timeout_steps=800)
################################################################################
# Action Space
################################################################################
action_space = action_spaces.Joystick(scaling_factor=0.01,
action_layers=['agent', 'cue'])
################################################################################
# Observer
################################################################################
_polygon_modifier = observers.polygon_modifiers.FirstPersonAgent(
agent_layer='agent')
observer = observers.PILRenderer(
image_size=(64, 64),
anti_aliasing=1,
color_to_rgb='hsv_to_rgb',
polygon_modifier=_polygon_modifier,
)
############################################################################
# Game rules
############################################################################
def _make_opaque(s):
s.opacity = 255
def _make_transparent(s):
s.opacity = 0
# Screen Phase
screen_phase = gr.Phase(duration=1, name='screen')
# Visible Phase
disappear_screen = gr.ModifySprites('screen', _make_transparent)
visible_phase = gr.Phase(one_time_rules=disappear_screen,
duration=2,
name='visible')
# Motion Phase
def _move(s):
s.velocity = np.random.uniform(-0.25, 0.25, size=(2, ))
cover_targets = gr.ModifySprites('covers', _make_opaque)
begin_motion = BeginMotion(angle_vel_range=(0.1, 0.3))
motion_phase = gr.Phase(
one_time_rules=[cover_targets, begin_motion],
duration=100,
name='motion',
)
# Response Phase
def _stop(s):
s.angle_vel = 0.
s.velocity = np.zeros(2)
def _unglue(s):
s.mass = 1.
appear_cue = gr.ModifySprites('cue', _make_opaque)
stop_targets = gr.ModifySprites(('targets', 'covers'), _stop)
unglue_agent = gr.ModifySprites(('agent', 'cue'), _unglue)
make_targets_discoverable = gr.ModifyOnContact(
layers_0='agent', layers_1='covers', modifier_1=_make_transparent)
response_phase = gr.Phase(
one_time_rules=[appear_cue, stop_targets, unglue_agent],
continual_rules=make_targets_discoverable,
name='response',
)
phase_sequence = gr.PhaseSequence(
screen_phase,
visible_phase,
motion_phase,
response_phase,
meta_state_phase_name_key='phase',
)
############################################################################
# Final config
############################################################################
config = {
'state_initializer': state_initializer,
'physics': physics,
'task': task,
'action_space': action_space,
'observers': {
'image': observer
},
'game_rules': (phase_sequence, ),
'meta_state_initializer': lambda: {
'phase': ''
}
}
return config
def state_initializer():
"""Callable returning new state at each episode reset."""
obstacles = obstacle_generator(disjoint=True)
ball = ball_generator(without_overlapping=obstacles)
if len(obstacles) < num_obstacles + 2 or not ball:
# Max recursion depth failed trying to generate without overlapping
return state_initializer()
red = obstacles[0]
green = obstacles[1]
obstacles = obstacles[2:]
# Set the colors of the red and green boxes
red.c0 = 255
red.c1 = 0
red.c2 = 0
green.c0 = 0
green.c1 = 255
green.c2 = 0
# Create agent and response tokens at the bottom of the sreen
agent = sprite.Sprite(x=0.5,
y=0.06,
shape='spoke_4',
scale=0.03,
c0=255,
c1=255,
c2=255)
responses = [
sprite.Sprite(x=0.6,
y=0.06,
shape='square',
scale=0.03,
c0=255,
c1=0,
c2=0),
sprite.Sprite(x=0.4,
y=0.06,
shape='square',
scale=0.03,
c0=0,
c1=255,
c2=0),
]
state = collections.OrderedDict([
('walls', walls + obstacles),
('red', [red]),
('green', [green]),
('ball', ball),
('responses', responses),
('agent', [agent]),
])
# Rejection sampling if trial won't finish in valid step range
original_ball_position = np.copy(ball[0].position)
original_ball_velocity = np.copy(ball[0].velocity)
valid_trial, contact_red = _predict_trial_end(state)
if valid_trial:
ball[0].position = original_ball_position
ball[0].velocity = original_ball_velocity
agent.metadata = {'true_contact_color': contact_red}
else:
return state_initializer()
return state
def _state_initializer():
agent = sprite.Sprite(x=0.5, y=0.5, scale=0.1, c0=128)
return collections.OrderedDict([('agent', [agent])])
def _get_config(num_obstacles, valid_step_range):
"""Get environment config.
Args:
num_obstacles: Int. Number of obstacles.
valid_step_range: 2-iterable of ints. (min_num_steps, max_num_steps).
All trials must have duration in this step range.
Returns:
config: Config dictionary to pass to environment constructor.
"""
############################################################################
# Physics
############################################################################
elastic_collision = physics_lib.Collision(elasticity=1.,
symmetric=False,
update_angle_vel=False)
physics = physics_lib.Physics(
(elastic_collision, 'ball', 'walls'),
updates_per_env_step=10,
)
def _predict_trial_end(state):
"""Predict whether a trial will end in step range and true response.
Args:
state: OrderedDict of sprite layers. Initial state of environment.
Returns:
valid_trial: Bool. Whether trial will end with number of steps in
valid_step_range.
contact_color: Binary. 0 if ball will contact red first, 1 if it
will contact green first.
"""
for step in range(valid_step_range[1]):
red_overlap = state['ball'][0].overlaps_sprite(state['red'][0])
green_overlap = state['ball'][0].overlaps_sprite(state['green'][0])
if red_overlap or green_overlap:
if step < valid_step_range[0]:
return False, None
else:
contact_color = 0 if red_overlap else 1
return True, contact_color
physics.step(state)
return False, None
############################################################################
# Sprite initialization
############################################################################
# Ball generator
ball_factors = distribs.Product(
[
distribs.Continuous('x', 0.15, 0.85),
distribs.Continuous('y', 0.15, 0.85),
RadialVelocity(speed=0.03)
],
scale=0.05,
shape='circle',
c0=64,
c1=64,
c2=255,
)
ball_generator = sprite_generators.generate_sprites(
ball_factors,
num_sprites=1,
max_recursion_depth=100,
fail_gracefully=True)
# Obstacle generator
obstacle_factors = distribs.Product(
[
distribs.Continuous('x', 0.2, 0.8),
distribs.Continuous('y', 0.2, 0.8)
],
scale=0.2,
shape='square',
c0=128,
c1=128,
c2=128,
)
obstacle_generator = sprite_generators.generate_sprites(
obstacle_factors,
num_sprites=2 + num_obstacles,
max_recursion_depth=100,
fail_gracefully=True)
# Walls
bottom_wall = [[-1, 0.1], [2, 0.1], [2, -1], [-1, -1]]
top_wall = [[-1, 0.95], [2, 0.95], [2, 2], [-1, 2]]
left_wall = [[0.05, -1], [0.05, 4], [-1, 4], [-1, -1]]
right_wall = [[0.95, -1], [0.95, 4], [2, 4], [2, -1]]
walls = [
sprite.Sprite(shape=np.array(v), x=0, y=0, c0=128, c1=128, c2=128)
for v in [bottom_wall, top_wall, left_wall, right_wall]
]
def state_initializer():
"""Callable returning new state at each episode reset."""
obstacles = obstacle_generator(disjoint=True)
ball = ball_generator(without_overlapping=obstacles)
if len(obstacles) < num_obstacles + 2 or not ball:
# Max recursion depth failed trying to generate without overlapping
return state_initializer()
red = obstacles[0]
green = obstacles[1]
obstacles = obstacles[2:]
# Set the colors of the red and green boxes
red.c0 = 255
red.c1 = 0
red.c2 = 0
green.c0 = 0
green.c1 = 255
green.c2 = 0
# Create agent and response tokens at the bottom of the sreen
agent = sprite.Sprite(x=0.5,
y=0.06,
shape='spoke_4',
scale=0.03,
c0=255,
c1=255,
c2=255)
responses = [
sprite.Sprite(x=0.6,
y=0.06,
shape='square',
scale=0.03,
c0=255,
c1=0,
c2=0),
sprite.Sprite(x=0.4,
y=0.06,
shape='square',
scale=0.03,
c0=0,
c1=255,
c2=0),
]
state = collections.OrderedDict([
('walls', walls + obstacles),
('red', [red]),
('green', [green]),
('ball', ball),
('responses', responses),
('agent', [agent]),
])
# Rejection sampling if trial won't finish in valid step range
original_ball_position = np.copy(ball[0].position)
original_ball_velocity = np.copy(ball[0].velocity)
valid_trial, contact_red = _predict_trial_end(state)
if valid_trial:
ball[0].position = original_ball_position
ball[0].velocity = original_ball_velocity
agent.metadata = {'true_contact_color': contact_red}
else:
return state_initializer()
return state
############################################################################
# Task
############################################################################
def _reward_fn(sprite_agent, sprite_response):
response_green = sprite_response.c0 < 128
if sprite_agent.metadata['true_contact_color'] == response_green:
return 1.
else:
return -1.
contact_reward = tasks.ContactReward(
reward_fn=_reward_fn,
layers_0='agent',
layers_1='responses',
reset_steps_after_contact=10,
)
task = tasks.CompositeTask(contact_reward, timeout_steps=400)
############################################################################
# Action space
############################################################################
action_space = action_spaces.Grid(
scaling_factor=0.015,
action_layers='agent',
control_velocity=True,
)
############################################################################
# Observer
############################################################################
observer = observers.PILRenderer(image_size=(64, 64), anti_aliasing=1)
############################################################################
# Game rules
############################################################################
# Stop ball on contact with red or green box
def _stop_ball(s):
s.velocity = np.zeros(2)
stop_ball = game_rules.ModifyOnContact(layers_0='ball',
layers_1=('red', 'green'),
modifier_0=_stop_ball)
############################################################################
# Final config
############################################################################
config = {
'state_initializer': state_initializer,
'physics': physics,
'task': task,
'action_space': action_space,
'observers': {
'image': observer
},
'game_rules': (stop_ball, ),
}
return config
def _add_sprite(min_x, max_x, min_y, max_y):
shape = np.array([[min_x, min_y], [max_x, min_y], [max_x, max_y],
[min_x, max_y]])
grid_sprites.append(sprite.Sprite(x=0., y=0., shape=shape,
**color))
def _get_config(translucent_occluder):
"""Get environment config."""
############################################################################
# Physics
############################################################################
elastic_collision = physics_lib.Collision(elasticity=1.,
symmetric=False,
update_angle_vel=False)
physics = physics_lib.Physics(
(elastic_collision, 'targets', 'walls'),
updates_per_env_step=10,
)
def _predict_contact(state):
"""Predict whether targets will contact."""
while True:
if state['targets'][0].overlaps_sprite(state['targets'][1]):
return True
if all(s.y > 1.1 and s.y_vel > 0 for s in state['targets']):
# Both targets above screen and moving up
break
physics.step(state)
return False
############################################################################
# Sprite initialization
############################################################################
# Targets
target_y_speed = 0.02
target_factors = distribs.Product(
[
distribs.Continuous('x', 0.15, 0.85),
distribs.Continuous('x_vel', -target_y_speed, target_y_speed)
],
y_vel=-target_y_speed,
scale=0.16,
shape='circle',
opacity=192,
c0=255,
c1=0,
c2=0,
)
# Occluder
occluder = sprite.Sprite(x=0.5,
y=0.2,
shape='square',
scale=1.,
c0=192,
c1=192,
c2=128,
opacity=128 if translucent_occluder else 255)
# Walls
bottom_wall = [[-1, 0.1], [2, 0.1], [2, -1], [-1, -1]]
left_wall = [[0.05, -1], [0.05, 4], [-1, 4], [-1, -1]]
right_wall = [[0.95, -1], [0.95, 4], [2, 4], [2, -1]]
walls = [
sprite.Sprite(shape=np.array(v), x=0, y=0, c0=128, c1=128, c2=128)
for v in [bottom_wall, left_wall, right_wall]
]
# Make response boxes and tokens
response_box_factors = dict(y=0.05,
scale=0.12,
shape='square',
aspect_ratio=0.5,
c0=0,
c1=0,
c2=0)
response_boxes = [
sprite.Sprite(x=0.4, **response_box_factors),
sprite.Sprite(x=0.6, **response_box_factors),
]
response_token_factors = dict(y=0.05,
scale=0.03,
shape='circle',
c0=255,
c1=0,
c2=0,
opacity=192)
response_tokens = [
sprite.Sprite(x=x, **response_token_factors)
for x in [0.37, 0.43, 0.59, 0.61]
]
def state_initializer():
"""Callable returning state ordereddict each episode reset."""
agent = sprite.Sprite(x=0.5,
y=0.05,
scale=0.03,
shape='spoke_4',
c0=255,
c1=255,
c2=255)
target_0 = sprite.Sprite(y=1.4, **target_factors.sample())
target_1 = sprite.Sprite(y=np.random.uniform(1.7, 2.4),
**target_factors.sample())
screen = sprite.Sprite(x=0.5,
y=0.5,
shape='square',
c0=128,
c1=128,
c2=128)
state = collections.OrderedDict([
('targets', [target_0, target_1]),
('occluders', [occluder]),
('walls', walls),
('response_boxes', response_boxes),
('response_tokens', response_tokens),
('agent', [agent]),
('screen', [screen]),
])
# Predict whether targets will contact, putting this information in
# agent metadata
orig_pos = [np.copy(s.position) for s in state['targets']]
orig_vel = [np.copy(s.velocity) for s in state['targets']]
agent.metadata = {'will_contact': _predict_contact(state)}
for s, pos, vel in zip(state['targets'], orig_pos, orig_vel):
s.position = pos
s.velocity = vel
return state
############################################################################
# Task
############################################################################
def _reward_fn(state):
agent = state['agent'][0]
if agent.overlaps_sprite(state['response_boxes'][0]):
# Collision response
return -1 if agent.metadata['will_contact'] else 1
elif agent.overlaps_sprite(state['response_boxes'][1]):
# No collision response
return 1 if agent.metadata['will_contact'] else -1
else:
return 0
conditional_task = tasks.Reset(
condition=lambda state: _reward_fn(state) != 0,
reward_fn=_reward_fn,
steps_after_condition=5,
)
task = tasks.CompositeTask(conditional_task, timeout_steps=1000)
############################################################################
# Action space
############################################################################
action_space = action_spaces.Grid(
scaling_factor=0.015,
action_layers='agent',
control_velocity=True,
)
############################################################################
# Observer
############################################################################
observer = observers.PILRenderer(image_size=(64, 64), anti_aliasing=1)
############################################################################
# Game rules
############################################################################
screen_vanish = game_rules.VanishByFilter('screen')
screen_vanish = game_rules.TimedRule(step_interval=(15, 16),
rules=(screen_vanish, ))
rules = (screen_vanish, )
############################################################################
# Final config
############################################################################
config = {
'state_initializer': state_initializer,
'physics': physics,
'task': task,
'action_space': action_space,
'observers': {
'image': observer
},
'game_rules': rules,
}
return config
def get_config(_):
"""Get environment config."""
############################################################################
# Sprite initialization
############################################################################
# Agents
agent_factors = distribs.Product(
[distribs.Continuous('x', 0., 1.),
distribs.Continuous('y', 0.35, 0.65)],
shape='circle', scale=0.1, c1=1., c2=0.7,
)
agent_0_factors = distribs.Product([agent_factors], c0=0.2)
agent_1_factors = distribs.Product([agent_factors], c0=0.1)
agent_2_factors = distribs.Product([agent_factors], c0=0.)
# Walls
walls = shapes.border_walls(visible_thickness=0.05, c0=0., c1=0., c2=0.5)
# Fountains
fountain_factors = {
'shape': 'circle', 'scale': 0.05, 'c0': 0.6, 'c1': 1., 'c2': _BAD_VALUE}
fountains_across = np.linspace(0.1, 0.9, 6)
fountains_up = np.linspace(0.75, 0.9, 2)
fountains_grid_x, fountains_grid_y = np.meshgrid(fountains_across,
fountains_up)
fountains_positions = zip(np.ravel(fountains_grid_x),
np.ravel(fountains_grid_y))
fountain_sprites = [
sprite.Sprite(x=x, y=y, **fountain_factors)
for (x, y) in fountains_positions
]
# Fruits
fruit_factors = {
'shape': 'circle', 'scale': 0.05, 'c0': 0.3, 'c1': 1., 'c2': _BAD_VALUE}
fruits_across = np.linspace(0.1, 0.9, 6)
fruits_up = np.linspace(0.1, 0.25, 2)
fruits_grid_x, fruits_grid_y = np.meshgrid(fruits_across, fruits_up)
fruits_positions = zip(np.ravel(fruits_grid_x), np.ravel(fruits_grid_y))
fruit_sprites = [
sprite.Sprite(x=x, y=y, **fruit_factors)
for (x, y) in fruits_positions
]
# Create callable initializer returning entire state
agent_0_generator = sprite_generators.generate_sprites(
agent_0_factors, num_sprites=1)
agent_1_generator = sprite_generators.generate_sprites(
agent_1_factors, num_sprites=1)
agent_2_generator = sprite_generators.generate_sprites(
agent_2_factors, num_sprites=1)
def state_initializer():
agent_0 = agent_0_generator(without_overlapping=walls)
agent_1 = agent_1_generator(without_overlapping=walls)
agent_2 = agent_2_generator(without_overlapping=walls)
state = collections.OrderedDict([
('walls', walls),
('fountains', fountain_sprites),
('fruits', fruit_sprites),
('agent_2', agent_2),
('agent_1', agent_1),
('agent_0', agent_0),
])
return state
############################################################################
# Physics
############################################################################
agent_friction_force = physics_lib.Drag(coeff_friction=0.25)
asymmetric_collision = physics_lib.Collision(
elasticity=0.25, symmetric=False)
forces = (
(agent_friction_force, ['agent_0', 'agent_1', 'agent_2']),
(asymmetric_collision, ['agent_0', 'agent_1', 'agent_2'], 'walls'),
)
physics = physics_lib.Physics(*forces, updates_per_env_step=5)
############################################################################
# Task
############################################################################
task = tasks.ContactReward(
1, layers_0='agent_0', layers_1='fruits',
condition=lambda s_0, s_1: s_1.c2 > _VALUE_THRESHOLD,
)
############################################################################
# Action space
############################################################################
action_space = action_spaces.Composite(
agent_0=action_spaces.Joystick(
scaling_factor=0.005, action_layers='agent_0'),
agent_1=action_spaces.Joystick(
scaling_factor=0.005, action_layers='agent_1'),
agent_2=action_spaces.Joystick(
scaling_factor=0.005, action_layers='agent_2'),
)
############################################################################
# Observer
############################################################################
image_observer = observers.PILRenderer(
image_size=(64, 64),
anti_aliasing=1,
color_to_rgb='hsv_to_rgb',
)
raw_state_observer = observers.RawState() # needed by hand-crafted agents
############################################################################
# Game rules
############################################################################
def _spoil_fruit(sprite):
sprite.c2 = _BAD_VALUE
def _ripen_fruit(sprite):
sprite.c2 = _GOOD_VALUE
def _poison_fountain(sprite):
sprite.c2 = _BAD_VALUE
def _clean_fountain(sprite):
sprite.c2 = _GOOD_VALUE
def agents_contacting_layer(state, layer, value):
n_contact = 0
for s in state[layer]:
if s.c2 != value:
continue
n_contact += (
s.overlaps_sprite(state['agent_0'][0]) or
s.overlaps_sprite(state['agent_1'][0]) or
s.overlaps_sprite(state['agent_2'][0])
)
return n_contact
poison_fountains = game_rules.ModifySprites(
layers='fountains', modifier=_poison_fountain, sample_one=True,
filter_fn=lambda s: s.c2 > _VALUE_THRESHOLD)
poison_fountains = game_rules.ConditionalRule(
condition=lambda s: agents_contacting_layer(s, 'fruits', _GOOD_VALUE),
rules=poison_fountains,
)
ripen_fruits = game_rules.ModifySprites(
layers='fruits', modifier=_ripen_fruit, sample_one=True,
filter_fn=lambda s: s.c2 < _VALUE_THRESHOLD)
ripen_fruits = game_rules.ConditionalRule(
condition=lambda s: agents_contacting_layer(s, 'fountains', _BAD_VALUE),
rules=ripen_fruits,
)
spoil_fruits = game_rules.ModifyOnContact(
layers_0='fruits',
layers_1=('agent_0', 'agent_1', 'agent_2'),
modifier_0=_spoil_fruit,
filter_0=lambda s: s.c2 > _VALUE_THRESHOLD)
clean_fountains = game_rules.ModifyOnContact(
layers_0='fountains',
layers_1=('agent_0', 'agent_1', 'agent_2'),
modifier_0=_clean_fountain,
filter_0=lambda s: s.c2 < _VALUE_THRESHOLD)
rules = (poison_fountains, spoil_fruits, ripen_fruits, clean_fountains)
############################################################################
# Final config
############################################################################
config = {
'state_initializer': state_initializer,
'physics': physics,
'task': task,
'action_space': action_space,
'observers': {'image': image_observer, 'state': raw_state_observer},
'game_rules': rules,
}
return config
def get_config(_):
"""Get environment config."""
############################################################################
# Sprite initialization
############################################################################
# Agent
agent_factors = distribs.Product(
[
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9)
],
shape='circle',
scale=0.1,
c0=0.33,
c1=1.,
c2=0.7,
)
# Predators
predator_factors = distribs.Product(
[
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9)
],
shape='circle',
scale=0.1,
c0=0.,
c1=1.,
c2=0.8,
)
# Prey
prey_factors = distribs.Product(
[
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9)
],
shape='circle',
scale=0.1,
c0=0.2,
c1=1.,
c2=1.,
)
# Boosters
booster_factors = distribs.Product(
[
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9)
],
shape='triangle',
scale=0.1,
c0=0.6,
c1=1.,
c2=1.,
)
# Portals
portal_factors = dict(shape='square', scale=0.1, c0=0., c1=0., c2=0.95)
portal_sprites = [
sprite.Sprite(x=0.125, y=0.125, **portal_factors),
sprite.Sprite(x=0.875, y=0.875, **portal_factors),
]
# Walls
wall_color = dict(c0=0., c1=0., c2=0.5)
island_wall_shape_0 = np.array([[0.2, 0.2], [0.4, 0.2], [0.4, 0.4],
[0.2, 0.4]])
island_wall_shapes = [
island_wall_shape_0,
island_wall_shape_0 + np.array([[0., 0.4]]),
island_wall_shape_0 + np.array([[0.4, 0.4]]),
island_wall_shape_0 + np.array([[0.4, 0.]]),
]
island_walls = [
sprite.Sprite(shape=shape, x=0., y=0., **wall_color)
for shape in island_wall_shapes
]
boundary_walls = shapes.border_walls(visible_thickness=0.05, **wall_color)
walls = boundary_walls + island_walls
# Callable sprite generators
agent_generator = sprite_generators.generate_sprites(agent_factors,
num_sprites=1)
predator_generator = sprite_generators.generate_sprites(predator_factors,
num_sprites=1)
prey_generator = sprite_generators.generate_sprites(
prey_factors, num_sprites=lambda: np.random.randint(2, 5))
booster_generator = sprite_generators.generate_sprites(booster_factors,
num_sprites=2)
# Create callable initializer returning entire state
def state_initializer():
portals = portal_sprites
agent = agent_generator(without_overlapping=walls)
predators = predator_generator(without_overlapping=walls + agent)
boosters = booster_generator(without_overlapping=walls + agent)
prey = prey_generator(without_overlapping=walls)
state = collections.OrderedDict([
('walls', walls),
('portals', portals),
('boosters', boosters),
('prey', prey),
('predators', predators),
('agent', agent),
])
return state
############################################################################
# Physics
############################################################################
agent_friction_force = physics_lib.Drag(coeff_friction=0.25)
predator_friction_force = physics_lib.Drag(coeff_friction=0.05)
predator_random_force = physics_lib.RandomForce(max_force_magnitude=0.02)
prey_friction_force = physics_lib.Drag(coeff_friction=0.02)
prey_random_force = physics_lib.RandomForce(max_force_magnitude=0.02)
predator_attraction = physics_lib.DistanceForce(
force_fn=physics_lib.linear_force_fn(zero_intercept=-0.002,
slope=0.001))
asymmetric_collision = physics_lib.Collision(elasticity=0.25,
symmetric=False,
update_angle_vel=False)
forces = (
(agent_friction_force, 'agent'),
(predator_friction_force, 'predators'),
(predator_random_force, 'predators'),
(prey_friction_force, 'prey'),
(prey_random_force, 'prey'),
(predator_attraction, 'agent', 'predators'),
(asymmetric_collision, ['agent', 'predators', 'prey'], 'walls'),
)
physics = physics_lib.Physics(*forces, updates_per_env_step=5)
############################################################################
# Task
############################################################################
predator_task = tasks.ContactReward(-5,
layers_0='agent',
layers_1='predators',
reset_steps_after_contact=0)
prey_task = tasks.ContactReward(1, layers_0='agent', layers_1='prey')
reset_task = tasks.Reset(
condition=lambda state: len(state['prey']) == 0,
steps_after_condition=5,
)
task = tasks.CompositeTask(predator_task,
prey_task,
reset_task,
timeout_steps=400)
############################################################################
# Action space
############################################################################
action_space = action_spaces.Joystick(
scaling_factor=0.01,
action_layers='agent',
)
############################################################################
# Observer
############################################################################
observer = observers.PILRenderer(
image_size=(64, 64),
anti_aliasing=1,
color_to_rgb='hsv_to_rgb',
)
############################################################################
# Game rules
############################################################################
disappear_rule = game_rules.VanishOnContact(vanishing_layer='prey',
contacting_layer='agent')
portal_rule = game_rules.Portal(teleporting_layer='agent',
portal_layer='portals')
rules = (disappear_rule, portal_rule, Booster())
############################################################################
# Final config
############################################################################
config = {
'state_initializer': state_initializer,
'physics': physics,
'task': task,
'action_space': action_space,
'observers': {
'image': observer
},
'game_rules': rules,
}
return config
def get_config(num_targets):
"""Get environment config.
Args:
num_targets: Int. Number of targets.
"""
############################################################################
# Sprite initialization
############################################################################
# Target circles
target_factors = distribs.Product(
[
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
RadialVelocity(speed=0.01),
],
scale=0.1,
shape='circle',
c0=0.,
c1=0.,
c2=0.9,
)
# Target bars
bar_factors = dict(scale=0.1,
shape='square',
aspect_ratio=0.3,
c0=0.,
c1=0.,
c2=0.2)
# Walls
bottom_wall = [[-1, 0], [2, 0], [2, -1], [-1, -1]]
top_wall = [[-1, 1], [2, 1], [2, 2], [-1, 2]]
left_wall = [[0, -1], [0, 4], [-1, 4], [-1, -1]]
right_wall = [[1, -1], [1, 4], [2, 4], [2, -1]]
walls = [
sprite.Sprite(shape=np.array(v), x=0, y=0, c0=0., c1=0., c2=0.5)
for v in [bottom_wall, top_wall, left_wall, right_wall]
]
# Occluder
occluder_factors = dict(x=0.5, y=0.5, c0=0.6, c1=0.25, c2=0.5, opacity=0)
# Cross shape for agent and fixation cross
cross_shape = 0.1 * np.array([[-5, 1], [-1, 1], [-1, 5], [1, 5], [1, 1],
[5, 1], [5, -1], [1, -1], [1, -5], [-1, -5],
[-1, -1], [-5, -1]])
def state_initializer():
fixation = sprite.Sprite(x=0.5,
y=0.5,
shape=cross_shape,
scale=0.1,
c0=0.,
c1=0.,
c2=0.)
screen = sprite.Sprite(x=0.5,
y=0.5,
shape='square',
scale=2.,
c0=0.,
c1=0.,
c2=1.)
agent = sprite.Sprite(x=0.5,
y=0.5,
scale=0.04,
shape=cross_shape,
c0=0.33,
c1=1.,
c2=1.)
occluder_shape = shapes.annulus_vertices(0.13, 2.)
occluder = sprite.Sprite(shape=occluder_shape, **occluder_factors)
targets = [
sprite.Sprite(**target_factors.sample())
for _ in range(num_targets)
]
bar_angles = 0.5 * np.pi * np.random.binomial(1, 0.5, (num_targets))
bars = [
sprite.Sprite(x=s.x,
y=s.y,
x_vel=s.x_vel,
y_vel=s.y_vel,
angle=angle,
**bar_factors)
for s, angle in zip(targets, bar_angles)
]
state = collections.OrderedDict([
('walls', walls),
('targets', targets),
('bars', bars),
('occluder', [occluder]),
('screen', [screen]),
('fixation', [fixation]),
('agent', [agent]),
])
return state
############################################################################
# Physics
############################################################################
elastic_collision = physics_lib.Collision(elasticity=1.,
symmetric=False,
update_angle_vel=False)
tether = physics_lib.TetherZippedLayers(layer_names=('targets', 'bars'),
update_angle_vel=False)
physics = physics_lib.Physics(
(elastic_collision, 'targets', 'walls'),
updates_per_env_step=10,
corrective_physics=[tether],
)
############################################################################
# Task
############################################################################
def _reward_condition(_, meta_state):
return meta_state['phase'] == 'reward'
task = tasks.Reset(
condition=_reward_condition,
reward_fn=lambda _: 1,
steps_after_condition=10,
)
############################################################################
# Action space
############################################################################
action_space = action_spaces.SetPosition(action_layers=('agent',
'occluder'))
############################################################################
# Observer
############################################################################
observer = observers.PILRenderer(
image_size=(64, 64),
anti_aliasing=1,
color_to_rgb=observers.color_maps.hsv_to_rgb,
)
############################################################################
# Game rules
############################################################################
# Fixation phase
fixation_rule = gr.Fixation('agent', 'fixation', _FIXATION_THRESHOLD,
'fixation_duration')
def _should_end_fixation(_, meta_state):
return meta_state['fixation_duration'] >= 15
fixation_phase = gr.Phase(
continual_rules=fixation_rule,
end_condition=_should_end_fixation,
name='fixation',
)
# Visible Phase
vanish_fixation = gr.VanishByFilter('fixation', lambda _: True)
vanish_screen = gr.VanishByFilter('screen', lambda _: True)
visible_phase = gr.Phase(
one_time_rules=[vanish_fixation, vanish_screen],
duration=5,
name='visible',
)
# Tracking Phase
def _make_opaque(s):
s.opacity = 255
appear_occluder = gr.ModifySprites('occluder', _make_opaque)
tracking_phase = gr.Phase(
one_time_rules=appear_occluder,
duration=lambda: np.random.randint(40, 80),
name='tracking',
)
# Change Phase
fixation_response_rule = gr.Fixation('agent', 'targets',
_FIXATION_THRESHOLD,
'response_duration')
def _should_end_change(_, meta_state):
return meta_state['response_duration'] >= 30
change_phase = gr.Phase(
one_time_rules=ChangeTargetFeature(),
continual_rules=fixation_response_rule,
name='change',
end_condition=_should_end_change,
)
# Reward Phase
def _make_transparent(s):
s.opacity = 0
disappear_occluder = gr.ModifySprites('occluder', _make_transparent)
def _glue(s):
s.velocity = np.zeros(2)
glue_targets = gr.ModifySprites(('targets', 'bars'), _glue)
reward_phase = gr.Phase(
one_time_rules=(disappear_occluder, glue_targets),
name='reward',
)
phase_sequence = gr.PhaseSequence(
fixation_phase,
visible_phase,
tracking_phase,
change_phase,
reward_phase,
meta_state_phase_name_key='phase',
)
############################################################################
# Final config
############################################################################
config = {
'state_initializer': state_initializer,
'physics': physics,
'task': task,
'action_space': action_space,
'observers': {
'image': observer,
'state': observers.RawState()
},
'game_rules': (phase_sequence, ),
'meta_state_initializer': lambda: {
'phase': ''
},
}
return config
