def testGridActions(self):
def _state_initializer():
agent = sprite.Sprite(x=0.5, y=0.5, scale=0.1, c0=128)
return collections.OrderedDict([('agent', [agent])])
max_episode_length = 5
task = tasks.Reset(lambda _: True,
steps_after_condition=max_episode_length - 1)
env = environment.Environment(
state_initializer=_state_initializer,
physics=physics_lib.Physics(),
task=task,
action_space=action_spaces.Grid(),
observers={'image': observers.PILRenderer(image_size=(64, 64))})
gym_env = gym_wrapper.GymWrapper(env)
assert (gym_env.observation_space == spaces.Dict({
'image':
spaces.Box(-np.inf, np.inf, shape=(64, 64, 3), dtype=np.uint8)
}))
assert (gym_env.action_space == spaces.Discrete(5))
for _ in range(3):
gym_env.reset()
for _ in range(max_episode_length - 1):
action = gym_env.action_space.sample()
obs, reward, done, _ = gym_env.step(action)
assert (obs['image'].dtype == np.uint8)
assert not done
assert reward == 0.
action = gym_env.action_space.sample()
_, _, done, _ = gym_env.step(action)
assert done
_, _, done, _ = gym_env.step(action)
assert not done
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(max_vel):
"""Get environment config."""
############################################################################
# Sprite initialization
############################################################################
# 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():
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
############################################################################
# Physics
############################################################################
force = (physics_lib.Drag(coeff_friction=0.25), ['agent', 'agent_annulus'])
corrective_physics = physics_lib.Tether(('prey', ), update_angle_vel=True)
physics = physics_lib.Physics(
force,
updates_per_env_step=10,
corrective_physics=corrective_physics,
)
############################################################################
# Task
############################################################################
prey_task = tasks.ContactReward(
1,
layers_0='agent',
layers_1='prey',
condition=lambda s_agent, s_prey: s_prey.c1 > 0.5,
)
reset_trial_task = tasks.Reset(
condition=lambda state: all([s.c1 < 0.5 for s in state['prey']]),
steps_after_condition=10,
)
task = tasks.CompositeTask(prey_task, reset_trial_task, timeout_steps=500)
############################################################################
# Action space
############################################################################
action_space = action_spaces.Joystick(scaling_factor=0.01,
action_layers=('agent',
'agent_annulus'))
############################################################################
# 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 prey gray upon contact
def _make_prey_gray(prey):
prey.c1 = 0.
prey.c2 = 0.6
make_prey_gray = game_rules.ModifyOnContact(
layers_0='agent',
layers_1='prey',
modifier_1=_make_prey_gray,
)
# Keep agent near center
keep_near_center = game_rules.KeepNearCenter(
agent_layer='agent',
layers_to_center=['agent_annulus', 'prey'],
grid_x=_GRID_SIZE,
)
rules = (make_prey_gray, keep_near_center)
############################################################################
# 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_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 _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
############################################################################
# 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_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(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