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 _get_config(num_ghosts, maze_size):
"""Get environment config."""
############################################################################
# Sprite initialization
############################################################################
# Agent
agent_factors = dict(shape='circle', scale=0.05, c0=0.33, c1=1., c2=0.66)
# Prey
prey_factors = dict(shape='circle', scale=0.025, c0=0.2, c1=1., c2=1.)
# Ghosts
ghost_factors = dict(shape='circle',
scale=0.05,
mass=np.inf,
c0=0.,
c1=1.,
c2=0.8)
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
############################################################################
# Physics
############################################################################
maze_physics = physics_lib.MazePhysics(
maze_layer='walls',
avatar_layers=('agent', 'prey', 'ghosts'),
constant_speed=0.015,
)
physics = physics_lib.Physics(
(physics_lib.RandomMazeWalk(speed=0.015), ['ghosts']),
updates_per_env_step=1,
corrective_physics=[maze_physics],
)
############################################################################
# Task
############################################################################
ghost_task = tasks.ContactReward(-5,
layers_0='agent',
layers_1='ghosts',
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(ghost_task,
prey_task,
reset_task,
timeout_steps=1000)
############################################################################
# Action space
############################################################################
action_space = action_spaces.Grid(
scaling_factor=0.015,
action_layers='agent',
control_velocity=True,
momentum=0.5, # Value irrelevant, since maze_physics has constant speed
)
############################################################################
# Observer
############################################################################
observer = observers.PILRenderer(
image_size=(256, 256),
anti_aliasing=1,
color_to_rgb='hsv_to_rgb',
)
############################################################################
# Game rules
############################################################################
def _unglue(s):
s.mass = 1.
def _unglue_condition(state):
return not np.all(state['agent'][0].velocity == 0)
unglue = game_rules.ConditionalRule(
condition=_unglue_condition,
rules=game_rules.ModifySprites(('prey', 'ghosts'), _unglue),
)
vanish_on_contact = game_rules.VanishOnContact(vanishing_layer='prey',
contacting_layer='agent')
rules = (vanish_on_contact, unglue)
############################################################################
# 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_prey, num_predators):
"""Get environment config."""
############################################################################
# Sprite initialization
############################################################################
# Agent
agent_factors = distribs.Product(
[distribs.Continuous('x', 0., 1.),
distribs.Continuous('y', 0., 1.)],
scale=0.08, c0=0, c1=255, c2=0,
)
# Predators
predator_factors = distribs.Product(
[distribs.Continuous('x', 0., 1.),
distribs.Continuous('y', 0., 1.),
distribs.Continuous('x_vel', -0.02, 0.02),
distribs.Continuous('y_vel', -0.02, 0.02),],
scale=0.08, shape='circle', opacity=192, c0=255, c1=0, c2=0,
)
# Prey
prey_factors = distribs.Product(
[distribs.Continuous('x', 0., 1.),
distribs.Continuous('y', 0., 1.),
distribs.Continuous('x_vel', -0.02, 0.02),
distribs.Continuous('y_vel', -0.02, 0.02),],
scale=0.08, shape='circle', opacity=192, c0=255, c1=255, c2=0,
)
# Create callable initializer returning entire state
predator_generator = sprite_generators.generate_sprites(
predator_factors, num_sprites=num_predators)
prey_generator = sprite_generators.generate_sprites(
prey_factors, num_sprites=num_prey)
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
############################################################################
# Physics
############################################################################
agent_friction_force = physics_lib.Drag(coeff_friction=0.25)
random_force = physics_lib.RandomForce(max_force_magnitude=0.01)
predator_attraction = physics_lib.DistanceForce(
physics_lib.linear_force_fn(zero_intercept=-0.001, slope=0.0005))
prey_avoid = physics_lib.DistanceForce(
physics_lib.linear_force_fn(zero_intercept=0.001, slope=-0.0005))
forces = (
(agent_friction_force, 'agent'),
(random_force, ['predators', 'prey']),
(predator_attraction, 'agent', 'predators'),
(prey_avoid, 'agent', 'prey'),
)
constant_speed = physics_lib.ConstantSpeed(
layer_names=['prey', 'predators'], speed=0.015)
physics = physics_lib.Physics(
*forces,
updates_per_env_step=10,
corrective_physics=[constant_speed],
)
############################################################################
# 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(
reset_task, predator_task, prey_task, timeout_steps=300)
############################################################################
# Action space
############################################################################
action_space = action_spaces.Joystick(
scaling_factor=0.025, action_layers='agent', control_velocity=True)
############################################################################
# Observer
############################################################################
observer = observers.PILRenderer(
image_size=(64, 64),
anti_aliasing=1,
polygon_modifier=polygon_modifiers.TorusGeometry(
['agent', 'predators', 'prey']),
)
############################################################################
# Game rules
############################################################################
prey_vanish = game_rules.VanishOnContact(
vanishing_layer='prey', contacting_layer='agent')
def _torus_position_wrap(s):
s.position = np.remainder(s.position, 1)
torus_position_wrap = game_rules.ModifySprites(
('agent', 'predators', 'prey'), _torus_position_wrap)
rules = (prey_vanish, torus_position_wrap)
############################################################################
# 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
############################################################################
# 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(_):
"""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