class PickAndPlaceEnv(MultitaskEnv, Serializable):
"""
A simple env where a 'cursor' robot can push objects around.
TODO: refactor to have base class shared with point2d.py code
TODO: rather than recreating a drawer, just save the previous drawers
"""
def __init__(
self,
num_objects=2,
# Environment dynamics
action_scale=1.0,
ball_radius=.75,
boundary_dist=4,
object_radius=0.50,
min_grab_distance=0.5,
walls=None,
# Rewards
action_l2norm_penalty=0,
reward_type="dense",
success_threshold=0.60,
# Reset settings
fixed_goal=None,
fixed_init_position=None,
init_position_strategy='random',
start_near_object=False,
# Visualization settings
images_are_rgb=True,
render_dt_msec=0,
render_onscreen=False,
render_size=84,
get_image_base_render_size=None,
show_goal=True,
# Goal sampling
goal_samplers=None,
goal_sampling_mode='random',
num_presampled_goals=10000,
object_reward_only=False,
):
"""
:param get_image_base_render_size: If set, then the drawer will always
generate images according to this size, and (smoothly) downsampled
images will be returned by `get_image`
"""
walls = walls or []
if fixed_goal is not None:
fixed_goal = np.array(fixed_goal)
if action_scale <= 0:
raise ValueError("Invalid action scale: {}".format(action_scale))
if init_position_strategy not in {
'random', 'on_random_object', 'fixed'
}:
raise ValueError('Invalid init position strategy: {}'.format(
init_position_strategy))
self.quick_init(locals())
self.render_dt_msec = render_dt_msec
self.action_l2norm_penalty = action_l2norm_penalty
self.render_onscreen = render_onscreen
self.render_size = render_size
self.reward_type = reward_type
self.action_scale = action_scale
self.success_threshold = success_threshold
self.boundary_dist = boundary_dist
self.ball_radius = ball_radius
self.walls = walls
self.fixed_goal = fixed_goal
self.init_position_strategy = init_position_strategy
self.images_are_rgb = images_are_rgb
self._show_goal = show_goal
self._all_objects = [
Object(
position=np.zeros((2, )),
color=Object.IDX_TO_COLOR[i],
radius=ball_radius if i == 0 else object_radius,
min_pos=-self.boundary_dist,
max_pos=self.boundary_dist,
) for i in range(num_objects + 1)
]
self.min_grab_distance = min_grab_distance
u = np.ones(3)
self.action_space = spaces.Box(-u, u, dtype=np.float32)
o = self.boundary_dist * np.ones(2 * (num_objects + 1))
self.num_objects = num_objects
self.obs_range = spaces.Box(-o, o, dtype='float32')
self.observation_space = spaces.Dict([
('observation', self.obs_range),
('desired_goal', self.obs_range),
('achieved_goal', self.obs_range),
('state_observation', self.obs_range),
('state_desired_goal', self.obs_range),
('state_achieved_goal', self.obs_range),
])
if get_image_base_render_size:
base_width, base_height = get_image_base_render_size
self._drawer = PygameViewer(
screen_width=base_width,
screen_height=base_height,
x_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
y_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
render_onscreen=self.render_onscreen,
)
self._fixed_get_image_render_size = True
else:
self._drawer = None
self._fixed_get_image_render_size = False
self._render_drawer = None
if fixed_init_position is None:
fixed_init_position = np.zeros_like(self.obs_range.low)
self._fixed_init_position = fixed_init_position
self._presampled_goals = None
goal_samplers = goal_samplers or {}
goal_samplers['fixed'] = PickAndPlaceEnv._sample_fixed_goal
goal_samplers['presampled'] = PickAndPlaceEnv._sample_presampled_goals
goal_samplers['random'] = PickAndPlaceEnv._sample_random_feasible_goals
self._custom_goal_sampler = goal_samplers
self._num_presampled_goals = num_presampled_goals
if goal_sampling_mode is None:
if fixed_goal:
goal_sampling_mode = 'fixed'
else:
goal_sampling_mode = 'presampled'
self.goal_sampling_mode = goal_sampling_mode
self.object_reward_only = object_reward_only
@property
def cursor(self):
return self._all_objects[0]
@property
def objects(self):
return self._all_objects[1:]
def step(self, raw_action):
velocities = raw_action[:2]
grab = raw_action[2] > 0
velocities = np.clip(velocities, a_min=-1, a_max=1) * self.action_scale
old_position = self.cursor.position.copy()
new_position = old_position + velocities
orig_new_pos = new_position.copy()
for wall in self.walls:
new_position = wall.handle_collision(old_position, new_position)
if sum(new_position != orig_new_pos) > 1:
"""
Hack: sometimes you get caught on two walls at a time. If you
process the input in the other direction, you might only get
caught on one wall instead.
"""
new_position = orig_new_pos.copy()
for wall in self.walls[::-1]:
new_position = wall.handle_collision(old_position,
new_position)
if grab:
grabbed_obj = self._grab_object()
if grabbed_obj:
grabbed_obj.move(velocities)
self.cursor.move(new_position - old_position)
ob = self._get_obs()
reward = self.compute_reward(velocities, ob)
info = self._get_info()
done = False
return ob, reward, done, info
def _get_info(self):
distance_to_target = self.cursor.distance_to_target()
is_success = distance_to_target < self.success_threshold
info = {
'distance_to_target_cursor': distance_to_target,
'success_cursor': is_success,
}
for i, obj in enumerate(self.objects):
obj_distance = obj.distance_to_target()
success = obj_distance < self.success_threshold
info['distance_to_target_obj_{}'.format(i)] = obj_distance
info['success_obj_{}'.format(i)] = success
return info
def reset(self):
goal = self.sample_goal()['state_desired_goal']
self._set_target_positions(goal)
if self.init_position_strategy == 'random':
init_pos = (
self.observation_space.spaces['state_observation'].sample())
elif self.init_position_strategy == 'fixed':
init_pos = self._fixed_init_position.copy()
elif self.init_position_strategy == 'on_random_object':
init_pos = (
self.observation_space.spaces['state_observation'].sample())
start_i = 2 + 2 * random.randint(0, len(self._all_objects) - 2)
end_i = start_i + 2
init_pos[:2] = init_pos[start_i:end_i].copy()
else:
raise ValueError(self.init_position_strategy)
self._set_positions(init_pos)
return self._get_obs()
def _position_inside_wall(self, pos):
for wall in self.walls:
if wall.contains_point(pos):
return True
return False
def _sample_position(self, low, high):
pos = np.random.uniform(low, high)
while self._position_inside_wall(pos) is True:
pos = np.random.uniform(low, high)
return pos
def _get_obs(self):
positions = self._get_positions()
target_positions = self._get_target_positions()
return dict(
observation=positions.copy(),
desired_goal=target_positions.copy(),
achieved_goal=positions.copy(),
state_observation=positions.copy(),
state_desired_goal=target_positions.copy(),
state_achieved_goal=positions.copy(),
)
def compute_rewards(self, actions, obs):
achieved_goals = obs['state_achieved_goal']
desired_goals = obs['state_desired_goal']
if self.object_reward_only:
achieved_goals = achieved_goals[:, 2:]
desired_goals = desired_goals[:, 2:]
d = np.linalg.norm(achieved_goals - desired_goals, axis=-1)
if self.reward_type == "sparse":
return -(d > self.success_threshold *
len(self._all_objects)).astype(np.float32)
elif self.reward_type == "dense":
return -d
elif self.reward_type == "dense_l1":
return -np.linalg.norm(
achieved_goals - desired_goals, axis=-1, ord=1)
elif self.reward_type == 'vectorized_dense':
return -np.abs(achieved_goals - desired_goals)
else:
raise NotImplementedError()
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
for stat_name in [
'distance_to_target',
'is_success',
]:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
for i, obj in enumerate(self.objects):
stat_name = 'distance_to_target_obj_{}'.format(i)
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def get_goal(self):
return {
'desired_goal': self._get_target_positions(),
'state_desired_goal': self._get_target_positions(),
}
def set_goal(self, goal):
self._set_target_positions(goal['state_desired_goal'])
def sample_goals(self, batch_size):
goal_sampler = self._custom_goal_sampler[self.goal_sampling_mode]
return goal_sampler(self, batch_size)
def _sample_random_feasible_goals(self, batch_size):
if len(self.walls) > 0:
goals = np.zeros((batch_size, self.obs_range.low.size))
for b in range(batch_size):
goals[b, :] = self._sample_position(
self.obs_range.low,
self.obs_range.high,
)
else:
goals = np.random.uniform(
self.obs_range.low,
self.obs_range.high,
size=(batch_size, self.obs_range.low.size),
)
state_goals = goals
return {
'desired_goal': goals,
'state_desired_goal': state_goals,
}
def _sample_fixed_goal(self, batch_size):
goals = state_goals = np.repeat(self.fixed_goal.copy()[None],
batch_size, 0)
return {
'desired_goal': goals,
'state_desired_goal': state_goals,
}
def _sample_presampled_goals(self, batch_size):
if self._presampled_goals is None:
self._presampled_goals = self._sample_random_feasible_goals(
self._num_presampled_goals)
random_idxs = np.random.choice(len(
list(self._presampled_goals.values())[0]),
size=batch_size)
goals = self._presampled_goals['desired_goal'][random_idxs]
state_goals = self._presampled_goals['state_desired_goal'][random_idxs]
return {
'desired_goal': goals,
'state_desired_goal': state_goals,
}
def _set_positions(self, positions):
for i, obj in enumerate(self._all_objects):
start_i = i * 2
end_i = i * 2 + 2
obj.position = positions[start_i:end_i]
def _set_target_positions(self, target_positions):
for i, obj in enumerate(self._all_objects):
start_i = i * 2
end_i = i * 2 + 2
obj.target_position = target_positions[start_i:end_i]
def _get_positions(self):
return np.concatenate([obj.position for obj in self._all_objects])
def _get_target_positions(self):
return np.concatenate(
[obj.target_position for obj in self._all_objects])
"""Functions for ImageEnv wrapper"""
def get_image(self, width=None, height=None):
"""Returns a black and white image"""
if self._drawer is None or (not self._fixed_get_image_render_size and
(self._drawer.width != width
or self._drawer.height != height)):
if width != height:
raise NotImplementedError()
self._drawer = PygameViewer(
screen_width=width,
screen_height=height,
x_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
y_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
render_onscreen=self.render_onscreen,
)
self._draw(self._drawer)
if width and height:
wh_size = (width, height)
else:
wh_size = None
img = self._drawer.get_image(wh_size)
if self.images_are_rgb:
return img.transpose((1, 0, 2))
else:
r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
img = (r + g + b).transpose() / 3
return img
def set_to_goal(self, goal_dict):
goal = goal_dict["state_desired_goal"]
self._set_positions(goal)
def get_env_state(self):
return self._get_obs()
def set_env_state(self, state):
self._set_positions(state["state_observation"])
self._set_target_positions(state["state_desired_goal"])
def render(self, mode='human', close=False):
if close:
self._render_drawer = None
return
if self._render_drawer is None or self._render_drawer.terminated:
self._render_drawer = PygameViewer(
self.render_size,
self.render_size,
x_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
y_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
render_onscreen=True,
)
self._draw(self._render_drawer)
self._render_drawer.tick(self.render_dt_msec)
if mode != 'interactive':
self._render_drawer.check_for_exit()
def _draw(self, drawer):
drawer.fill(Color('black'))
for obj in self._all_objects:
obj.draw(drawer, draw_target_position=self._show_goal)
for wall in self.walls:
draw_wall(drawer, wall)
drawer.render()
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
for stat_name in [
'distance_to_target_obj_{}'.format(i)
for i in range(len(self.objects))
] + ['success_obj_{}'.format(i) for i in range(len(self.objects))] + [
'distance_to_target_cursor',
'success_cursor',
]:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def _grab_object(self):
closest_object = None
min_dis = None
for obj in self.objects:
distance = obj.distance(self.cursor.position)
if ((distance <= self.min_grab_distance)
and (closest_object is None or distance < min_dis)):
min_dis = distance
closest_object = obj
return closest_object
def goal_conditioned_diagnostics(self, paths, goals):
statistics = OrderedDict()
stat_to_lists = defaultdict(list)
for path, goal in zip(paths, goals):
difference = path['observations'] - goal
for i in range(len(self._all_objects)):
distance = np.linalg.norm(difference[:, 2 * i:2 * i + 2],
axis=-1)
distance_key = 'distance_to_target_obj_{}'.format(i)
stat_to_lists[distance_key].append(distance)
success_key = 'success_obj_{}'.format(i)
stat_to_lists[success_key].append(
distance < self.success_threshold)
for stat_name, stat_list in stat_to_lists.items():
statistics.update(
create_stats_ordered_dict(
stat_name,
stat_list,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'{}/final'.format(stat_name),
[s[-1:] for s in stat_list],
always_show_all_stats=True,
exclude_max_min=True,
))
statistics.update(
create_stats_ordered_dict(
'{}/initial'.format(stat_name),
[s[:1] for s in stat_list],
always_show_all_stats=True,
exclude_max_min=True,
))
return statistics
class Point2DEnv(MultitaskEnv, Serializable):
"""
A little 2D point whose life goal is to reach a target.
"""
def __init__(
self,
render_dt_msec=0,
action_l2norm_penalty=0, # disabled for now
render_onscreen=False,
render_size=256,
reward_type="dense",
action_scale=1.0,
target_radius=0.5,
boundary_dist=4,
ball_radius=0.15,
walls=None,
init_pos_range=None,
target_pos_range=None,
images_are_rgb=False, # else black and white
show_goal=True,
n_bins=10,
use_count_reward=False,
show_discrete_grid=False,
**kwargs):
if walls is None:
walls = []
if walls is None:
walls = []
# if fixed_goal is not None:
# fixed_goal = np.array(fixed_goal)
self.quick_init(locals())
self.render_dt_msec = render_dt_msec
self.action_l2norm_penalty = action_l2norm_penalty
self.render_onscreen = render_onscreen
self.render_size = render_size
self.reward_type = reward_type
self.action_scale = action_scale
self.target_radius = target_radius
self.boundary_dist = boundary_dist
self.ball_radius = ball_radius
self.walls = walls
self.n_bins = n_bins
self.use_count_reward = use_count_reward
self.show_discrete_grid = show_discrete_grid
self.images_are_rgb = images_are_rgb
self.show_goal = show_goal
self.x_bins = np.linspace(-self.boundary_dist, self.boundary_dist,
self.n_bins)
self.y_bins = np.linspace(-self.boundary_dist, self.boundary_dist,
self.n_bins)
self.bin_counts = np.ones((self.n_bins + 1, self.n_bins + 1))
self.max_target_distance = self.boundary_dist - self.target_radius
self._target_position = None
self._position = np.zeros((2))
u = np.ones(2)
self.action_space = spaces.Box(-u, u, dtype=np.float32)
o = self.boundary_dist * np.ones(2)
self.obs_range = spaces.Box(-o, o, dtype='float32')
if not init_pos_range:
self.init_pos_range = self.obs_range
else:
assert np.all(np.abs(init_pos_range) < boundary_dist), (
f"Init position must be"
"within the boundaries of the environment: ({-boundary_dist}, {boundary_dist})"
)
low, high = init_pos_range
self.init_pos_range = spaces.Box(np.array(low),
np.array(high),
dtype='float32')
if not target_pos_range:
self.target_pos_range = self.obs_range
else:
assert np.all(np.abs(target_pos_range) < boundary_dist), (
f"Goal position must be"
"within the boundaries of the environment: ({-boundary_dist}, {boundary_dist})"
)
low, high = target_pos_range
self.target_pos_range = spaces.Box(np.array(low),
np.array(high),
dtype='float32')
self.observation_space = spaces.Dict([
('observation', self.obs_range),
('onehot_observation',
spaces.Box(0,
1,
shape=(2 * (self.n_bins + 1), ),
dtype=np.float32)),
('desired_goal', self.obs_range),
('achieved_goal', self.obs_range),
('state_observation', self.obs_range),
('state_desired_goal', self.obs_range),
('state_achieved_goal', self.obs_range),
])
self.drawer = None
self.render_drawer = None
self.reset()
def step(self, velocities):
assert self.action_scale <= 1.0
velocities = np.clip(velocities, a_min=-1, a_max=1) * self.action_scale
new_position = self._position + velocities
orig_new_pos = new_position.copy()
for wall in self.walls:
new_position = wall.handle_collision(self._position, new_position)
if sum(new_position != orig_new_pos) > 1:
"""
Hack: sometimes you get caught on two walls at a time. If you
process the input in the other direction, you might only get
caught on one wall instead.
"""
new_position = orig_new_pos.copy()
for wall in self.walls[::-1]:
new_position = wall.handle_collision(self._position,
new_position)
self._position = new_position
self._position = np.clip(
self._position,
a_min=-self.boundary_dist,
a_max=self.boundary_dist,
)
distance_to_target = np.linalg.norm(self._position -
self._target_position)
is_success = distance_to_target < self.target_radius
ob = self._get_obs()
x_d, y_d = ob['discrete_observation']
self.bin_counts[x_d, y_d] += 1
reward = self.compute_reward(velocities, ob)
info = {
'radius': self.target_radius,
'target_position': self._target_position,
'distance_to_target': distance_to_target,
# TODO (kevintli): Make this work for general mazes
# 'manhattan_dist_to_target': self._medium_maze_manhattan_distance(ob['state_achieved_goal'], ob['state_desired_goal']),
'velocity': velocities,
'speed': np.linalg.norm(velocities),
'is_success': is_success,
'ext_reward': self._ext_reward,
}
if self.use_count_reward:
info['count_bonus'] = self._count_bonus
done = False
return ob, reward, done, info
def reset(self):
# TODO: Make this more general
self._target_position = self.sample_goal()['state_desired_goal']
# if self.randomize_position_on_reset:
self._position = self._sample_position(
# self.obs_range.low,
# self.obs_range.high,
self.init_pos_range.low,
self.init_pos_range.high,
)
return self._get_obs()
def _position_inside_wall(self, pos):
for wall in self.walls:
if wall.contains_point(pos):
return True
return False
def _sample_position(self, low, high):
if np.all(low == high):
return low
pos = np.random.uniform(low, high)
while self._position_inside_wall(pos) is True:
pos = np.random.uniform(low, high)
return pos
def clear_bin_counts(self):
self.bin_counts = np.ones((self.n_bins + 1, self.n_bins + 1))
def _discretize_observation(self, obs):
x, y = obs['state_observation'].copy()
x_d, y_d = np.digitize(x, self.x_bins), np.digitize(y, self.y_bins)
return np.array([x_d, y_d])
def _get_obs(self):
obs = collections.OrderedDict(
observation=self._position.copy(),
desired_goal=self._target_position.copy(),
achieved_goal=self._position.copy(),
state_observation=self._position.copy(),
state_desired_goal=self._target_position.copy(),
state_achieved_goal=self._position.copy(),
)
# Update with discretized state
pos_discrete = self._discretize_observation(obs)
pos_onehot = np.eye(self.n_bins + 1)[pos_discrete]
obs['discrete_observation'] = pos_discrete
obs['onehot_observation'] = pos_onehot
return obs
def _medium_maze_manhattan_distance(self, s1, s2):
# Maze wall positions
left_wall_x = -self.boundary_dist / 3
left_wall_bottom = self.inner_wall_max_dist
right_wall_x = self.boundary_dist / 3
right_wall_top = -self.inner_wall_max_dist
s1 = s1.copy()
s2 = s2.copy()
if len(s1.shape) == 1:
s1, s2 = s1[None], s2[None]
# Since maze distances are symmetric, redefine s1,s2 for convenience
# so that points in s1 are to the left of those in s2
combined = np.hstack((s1[:, None], s2[:, None]))
indices = np.argmin((s1[:, 0], s2[:, 0]), axis=0)
s1 = np.take_along_axis(combined, indices[:, None, None],
axis=1).squeeze(axis=1)
s2 = np.take_along_axis(combined, 1 - indices[:, None, None],
axis=1).squeeze(axis=1)
x1 = s1[:, 0]
x2 = s2[:, 0]
# Horizontal movement
x_dist = np.abs(x2 - x1)
# Vertical movement
boundary_ys = [left_wall_bottom, right_wall_top, 0]
boundary_xs = [left_wall_x, right_wall_x, self.boundary_dist, -4.0001]
y_directions = [
1, -1, 0
] # +1 means need to get to bottom, -1 means need to get to top
curr_y, goal_y = s1[:, 1], s2[:, 1]
y_dist = np.zeros(len(s1))
for i in range(3):
# Get all points where s1 and s2 respectively are in the current vertical section of the maze
curr_in_section = x1 <= boundary_xs[i]
goal_in_section = np.logical_and(boundary_xs[i - 1] < x2,
x2 <= boundary_xs[i])
goal_after_section = x2 > boundary_xs[i]
# Both in current section: move directly to goal
mask = np.logical_and(curr_in_section, goal_in_section)
y_dist += mask * np.abs(curr_y - goal_y)
curr_y[mask] = goal_y[mask]
# s2 is further in maze: move to next corner
mask = np.logical_and(
curr_in_section,
np.logical_and(goal_after_section, y_directions[i] *
(boundary_ys[i] - curr_y) > 0))
y_dist += mask * np.clip(
y_directions[i] * (boundary_ys[i] - curr_y), 0, None)
curr_y[mask] = boundary_ys[i]
return x_dist + y_dist
def compute_rewards(self, actions, obs, reward_type=None):
reward_type = reward_type or self.reward_type
achieved_goals = obs['state_achieved_goal']
desired_goals = obs['state_desired_goal']
d = np.linalg.norm(achieved_goals - desired_goals, axis=-1)
if reward_type == "sparse":
r = -(d > self.target_radius).astype(np.float32)
elif reward_type == "dense":
r = -d
elif reward_type == "vectorized_dense":
r = -np.abs(achieved_goals - desired_goals)
elif reward_type == "medium_maze_ground_truth_manhattan":
"""
# Use maze distance from current position to goal as the negative reward.
# This is specifically for the medium Maze-v0 env, which has two vertical walls.
"""
r = -self._medium_maze_manhattan_distance(achieved_goals,
desired_goals)
elif reward_type == "laplace_smoothing":
# Laplace smoothing: 1 within the goal region, 1/(n+2) at all other states
# (where n is the number of visitations)
r = np.zeros(d.shape)
pos_d = obs['discrete_observation']
r = 1 / (self.bin_counts[pos_d[:, 0], pos_d[:, 1]] + 2)
r[d <= self.target_radius] = 1
elif reward_type == "none":
r = np.zeros(d.shape)
else:
raise NotImplementedError(
f"Unrecognized reward type: {reward_type}")
if self.use_count_reward:
# TODO: Add different count based strategies
pos_d = obs['discrete_observation']
self._ext_reward = r.copy()
self._count_bonus = 1 / np.sqrt(self.bin_counts[pos_d[:, 0],
pos_d[:, 1]])
r += self._count_bonus
else:
self._ext_reward = r.copy()
return r
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
for stat_name in [
'radius',
'target_position',
'distance_to_target',
'velocity',
'speed',
'is_success',
'ext_reward',
'count_bonus',
]:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def get_goal(self):
return {
'desired_goal': self._target_position.copy(),
'state_desired_goal': self._target_position.copy(),
}
def sample_goals(self, batch_size):
# if self.fixed_goal:
# goals = np.repeat(
# self.fixed_goal.copy()[None],
# batch_size,
# 0)
# else:
goals = np.zeros((batch_size, self.obs_range.low.size))
for b in range(batch_size):
if batch_size > 1:
logging.warning("This is very slow!")
goals[b, :] = self._sample_position(
# self.obs_range.low,
# self.obs_range.high,
self.target_pos_range.low,
self.target_pos_range.high,
)
return {
'desired_goal': goals,
'state_desired_goal': goals,
}
def set_position(self, pos):
self._position[0] = pos[0]
self._position[1] = pos[1]
"""Functions for ImageEnv wrapper"""
def get_image(self, width=None, height=None):
"""Returns a black and white image"""
if self.drawer is None:
if width != height:
raise NotImplementedError()
self.drawer = PygameViewer(
screen_width=width,
screen_height=height,
# TODO(justinvyu): Action scale = 1 breaks rendering, why?
# x_bounds=(-self.boundary_dist - self.ball_radius,
# self.boundary_dist + self.ball_radius),
# y_bounds=(-self.boundary_dist - self.ball_radius,
# self.boundary_dist + self.ball_radius),
x_bounds=(-self.boundary_dist, self.boundary_dist),
y_bounds=(-self.boundary_dist, self.boundary_dist),
render_onscreen=self.render_onscreen,
)
self.draw(self.drawer)
img = self.drawer.get_image()
if self.images_are_rgb:
return img.transpose((1, 0, 2))
else:
r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
img = (-r + b).transpose().flatten()
return img
def set_to_goal(self, goal_dict):
goal = goal_dict["state_desired_goal"]
self._position = goal
self._target_position = goal
def get_env_state(self):
return self._get_obs()
def set_env_state(self, state):
position = state["state_observation"]
goal = state["state_desired_goal"]
self._position = position
self._target_position = goal
def draw(self, drawer):
drawer.fill(Color('white'))
if self.show_discrete_grid:
for x in self.x_bins:
drawer.draw_segment((x, -self.boundary_dist),
(x, self.boundary_dist),
Color(220, 220, 220, 25),
aa=False)
for y in self.y_bins:
drawer.draw_segment((-self.boundary_dist, y),
(self.boundary_dist, y),
Color(220, 220, 220, 25),
aa=False)
if self.show_goal:
drawer.draw_solid_circle(
self._target_position,
self.target_radius,
Color('green'),
)
try:
drawer.draw_solid_circle(
self._position,
self.ball_radius,
Color('blue'),
)
except ValueError as e:
print('\n\n RENDER ERROR \n\n')
for wall in self.walls:
drawer.draw_segment(
wall.endpoint1,
wall.endpoint2,
Color('black'),
)
drawer.draw_segment(
wall.endpoint2,
wall.endpoint3,
Color('black'),
)
drawer.draw_segment(
wall.endpoint3,
wall.endpoint4,
Color('black'),
)
drawer.draw_segment(
wall.endpoint4,
wall.endpoint1,
Color('black'),
)
drawer.render()
def render(self, mode='human', width=None, height=None, close=False):
if close:
self.render_drawer = None
return
if mode == 'rgb_array':
return self.get_image(width=width, height=height)
if self.render_drawer is None or self.render_drawer.terminated:
self.render_drawer = PygameViewer(
self.render_size,
self.render_size,
# x_bounds=(-self.boundary_dist-self.ball_radius,
# self.boundary_dist+self.ball_radius),
# y_bounds=(-self.boundary_dist-self.ball_radius,
# self.boundary_dist+self.ball_radius),
x_bounds=(-self.boundary_dist, self.boundary_dist),
y_bounds=(-self.boundary_dist, self.boundary_dist),
render_onscreen=True,
)
self.draw(self.render_drawer)
self.render_drawer.tick(self.render_dt_msec)
if mode != 'interactive':
self.render_drawer.check_for_exit()
# def get_diagnostics(self, paths, prefix=''):
# statistics = OrderedDict()
# for stat_name in ('distance_to_target', ):
# stat_name = stat_name
# stat = get_stat_in_paths(paths, 'env_infos', stat_name)
# statistics.update(create_stats_ordered_dict(
# '%s%s' % (prefix, stat_name),
# stat,
# always_show_all_stats=True,
# ))
# statistics.update(create_stats_ordered_dict(
# 'Final %s%s' % (prefix, stat_name),
# [s[-1] for s in stat],
# always_show_all_stats=True,
# ))
# return statistics
"""Static visualization/utility methods"""
@staticmethod
def true_model(state, action):
velocities = np.clip(action, a_min=-1, a_max=1)
position = state
new_position = position + velocities
return np.clip(
new_position,
a_min=-Point2DEnv.boundary_dist,
a_max=Point2DEnv.boundary_dist,
)
@staticmethod
def true_states(state, actions):
real_states = [state]
for action in actions:
next_state = Point2DEnv.true_model(state, action)
real_states.append(next_state)
state = next_state
return real_states
@staticmethod
def plot_trajectory(ax, states, actions, goal=None):
assert len(states) == len(actions) + 1
x = states[:, 0]
y = -states[:, 1]
num_states = len(states)
plasma_cm = plt.get_cmap('plasma')
for i, state in enumerate(states):
color = plasma_cm(float(i) / num_states)
ax.plot(
state[0],
-state[1],
marker='o',
color=color,
markersize=10,
)
actions_x = actions[:, 0]
actions_y = -actions[:, 1]
ax.quiver(x[:-1],
y[:-1],
x[1:] - x[:-1],
y[1:] - y[:-1],
scale_units='xy',
angles='xy',
scale=1,
width=0.005)
ax.quiver(
x[:-1],
y[:-1],
actions_x,
actions_y,
scale_units='xy',
angles='xy',
scale=1,
color='r',
width=0.0035,
)
ax.plot(
[
-Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
[
Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
color='k',
linestyle='-',
)
ax.plot(
[
Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
[
Point2DEnv.boundary_dist,
Point2DEnv.boundary_dist,
],
color='k',
linestyle='-',
)
ax.plot(
[
Point2DEnv.boundary_dist,
Point2DEnv.boundary_dist,
],
[
Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
color='k',
linestyle='-',
)
ax.plot(
[
Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
[
-Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
color='k',
linestyle='-',
)
if goal is not None:
ax.plot(goal[0], -goal[1], marker='*', color='g', markersize=15)
ax.set_ylim(
-Point2DEnv.boundary_dist - 1,
Point2DEnv.boundary_dist + 1,
)
ax.set_xlim(
-Point2DEnv.boundary_dist - 1,
Point2DEnv.boundary_dist + 1,
)
def initialize_camera(self, init_fctn):
pass
class Point2DEnv(MultitaskEnv, Serializable):
"""A little 2D point whose life goal is to reach a target.
Attributes
----------
render_dt_msec : float
seconds before the next frame in the image is rendered
action_l2norm_penalty : float
penalty scale for the actions by the agent
render_onscreen : bool
whether to include the rendering visually (instead of simply using the
image for the observation)
render_size : int
width/length number of pixels in the rendered image
reward_type : str
the reward type. Must be one of: "sparse", "dense", or
"vectorized_dense"
action_scale : float
the multiple from action to velocity
target_radius : float
the radius of the targeted position when being rendered
boundary_dist : float
the distance from the center to the boundary
ball_radius : float
the radius of the agent when being rendered
walls : list of object
a list of wall objects describing the position of the walls and how to
handle collisions with said walls
fixed_goal : [float, float] or None
the goal to use. If set to None, it is picked randomly.
randomize_position_on_reset : bool
whether to initialize the position of the agent randomly
images_are_rgb : bool
specifies whether the image is RGB. Otherwise, it's black and white
show_goal : bool
whether to render the goal(s)
images_in_obs : bool
whether to use the image in the observation
image_size : int
number of elements in the image. Set to 0 if images are not being used.
action_space : gym.spaces.*
the action space of the environment
obs_range : gym.spaces.*
the range of the initial position of the agent
context_space : gym.spaces.*
the context space of the environment
observation_space : gym.spaces.*
the observation space of the environment
drawer : multiworld.envs.pygame.pygame_viewer.PygameViewer or None
The drawer for the images of the environment. Set to None if images are
not being used.
render_drawer : multiworld.envs.pygame.pygame_viewer.PygameViewer or None
The drawer for the images of the environment if the environment is
being rendered. Set to None if images are not being used.
horizon : int
environment time horizon
t : int
number of steps since the start of the most recent episode
"""
def __init__(
self,
render_dt_msec=0,
action_l2norm_penalty=0, # disabled for now
render_onscreen=False,
render_size=32,
reward_type="dense",
action_scale=1.0,
target_radius=0.60,
boundary_dist=4,
ball_radius=0.50,
walls=None,
fixed_goal=None,
randomize_position_on_reset=True,
images_are_rgb=True, # else black and white
show_goal=True,
images_in_obs=True,
**kwargs):
"""Instantiate the environment.
Parameters
----------
render_dt_msec : float
seconds before the next frame in the image is rendered
action_l2norm_penalty : float
penalty scale for the actions by the agent
render_onscreen : bool
whether to include the rendering visually (instead of simply using
the image for the observation)
render_size : int
width/length number of pixels in the rendered image
reward_type : str
the reward type. Must be one of: "sparse", "dense", or
"vectorized_dense"
action_scale : float
the multiple from action to velocity
target_radius : float
the radius of the targeted position when being rendered
boundary_dist : float
the distance from the center to the boundary
ball_radius : float
the radius of the agent when being rendered
walls : list of object
a list of wall objects describing the position of the walls and how
to handle collisions with said walls
fixed_goal : [float, float] or None
the goal to use. If set to None, it is picked randomly.
randomize_position_on_reset : bool
whether to initialize the position of the agent randomly
images_are_rgb : bool
specifies whether the image is RGB. Otherwise, it's black and white
show_goal : bool
whether to render the goal(s)
images_in_obs : bool
whether to use the image in the obsevation
kwargs : dict
additional arguments. Unused here.
"""
if walls is None:
walls = []
if fixed_goal is not None:
fixed_goal = np.array(fixed_goal)
if len(kwargs) > 0:
logger = logging.getLogger(__name__)
logger.log(logging.WARNING, "WARNING, ignoring kwargs:", kwargs)
self.quick_init(locals())
self.render_dt_msec = render_dt_msec
self.action_l2norm_penalty = action_l2norm_penalty
self.render_onscreen = render_onscreen
self.render_size = render_size
self.reward_type = reward_type
self.action_scale = action_scale
self.target_radius = target_radius
self.boundary_dist = boundary_dist
self.ball_radius = ball_radius
self.walls = walls
self.fixed_goal = fixed_goal
self.randomize_position_on_reset = randomize_position_on_reset
self.images_are_rgb = images_are_rgb
self.show_goal = show_goal
self.images_in_obs = images_in_obs
self.image_size = 1024 * (3 if self.images_are_rgb else 1)
if not self.images_in_obs:
self.image_size = 0
self.max_target_distance = self.boundary_dist - self.target_radius
self._target_position = None
self._position = np.zeros(2)
u = np.ones(2)
self.action_space = spaces.Box(-u, u, dtype=np.float32)
o = self.boundary_dist * np.ones(2)
self.obs_range = spaces.Box(-o, o, dtype='float32')
self.context_space = spaces.Box(-o, o, dtype='float32')
self.observation_space = spaces.Box(
np.concatenate([np.zeros([self.image_size]), -o], 0),
np.concatenate([np.ones([self.image_size]), o], 0),
dtype='float32')
self.drawer = None
self.render_drawer = None
self.horizon = 200
self.t = 0
@property
def current_context(self):
"""Return the current goal by the environment."""
return self._target_position
def step(self, velocities):
"""Advance the simulation by one step.
Parameters
----------
velocities : array_like
the action by the agent, defined as its velocities in the x and y
directions
Returns
-------
array_like
agent's observation of the current environment
float
amount of reward associated with the previous state/action pair
bool
indicates whether the episode has ended
dict
contains other diagnostic information from the previous action
"""
assert self.action_scale <= 1.0
velocities = np.clip(velocities, a_min=-1, a_max=1) * self.action_scale
new_position = self._position + velocities
orig_new_pos = new_position.copy()
for wall in self.walls:
new_position = wall.handle_collision(self._position, new_position)
if sum(new_position != orig_new_pos) > 1:
# Hack: sometimes you get caught on two walls at a time. If you
# process the input in the other direction, you might only get
# caught on one wall instead.
new_position = orig_new_pos.copy()
for wall in self.walls[::-1]:
new_position = wall.handle_collision(self._position,
new_position)
self.t += 1
self._position = new_position
self._position = np.clip(
self._position,
a_min=-self.boundary_dist,
a_max=self.boundary_dist,
)
distance_to_target = np.linalg.norm(self._position -
self._target_position)
is_success = distance_to_target < self.target_radius
ob = self._get_obs()
reward = self.compute_reward(velocities, {'ob': ob})
info = {
'radius': self.target_radius,
'target_position': self._target_position,
'distance_to_target': distance_to_target,
'velocity': velocities,
'speed': np.linalg.norm(velocities),
'is_success': is_success,
}
done = self.t >= self.horizon
return ob, reward, done, info
def reset(self):
"""Reset the environment."""
self.t = 0
self._target_position = self.sample_goal()['goals']
if self.randomize_position_on_reset:
self._position = self._sample_position(
self.obs_range.low,
self.obs_range.high,
)
return self._get_obs()
def _position_inside_wall(self, pos):
"""Return True if the agent is in a wall."""
for wall in self.walls:
if wall.contains_point(pos):
return True
return False
def _sample_position(self, low, high):
"""Sample a starting position for the agent."""
pos = np.random.uniform(low, high)
while self._position_inside_wall(pos) is True:
pos = np.random.uniform(low, high)
return pos
def _get_obs(self):
"""Return the observation of the agent.
See States in the description of the environment for more.
"""
if self.images_in_obs:
img = self.get_image(32, 32).reshape([-1]).astype(
np.float32) / 255.0
return np.concatenate([img, self._position.copy()], 0)
else:
return self._position.copy()
def compute_rewards(self, actions, obs):
"""See parent class.
The rewards are described in the Rewards section of the description of
the environment.
"""
achieved_goals = obs['ob'][:, self.image_size:]
desired_goals = self._target_position[np.newaxis, :]
d = np.linalg.norm(achieved_goals - desired_goals, axis=-1)
if self.reward_type == "sparse":
return -(d > self.target_radius).astype(np.float32)
elif self.reward_type == "dense":
return -d
elif self.reward_type == 'vectorized_dense':
return -np.abs(achieved_goals - desired_goals)
else:
raise NotImplementedError()
def get_goal(self):
"""See parent class."""
return self._target_position.copy()
def sample_goals(self, batch_size):
"""See parent class.
The goal is the desired x,y coordinates.
"""
if self.fixed_goal is not None:
goals = np.repeat(self.fixed_goal.copy()[None], batch_size, 0)
else:
goals = np.zeros((batch_size, self.obs_range.low.size))
for b in range(batch_size):
if batch_size > 1:
logging.warning("This is very slow!")
goals[b, :] = self._sample_position(
self.obs_range.low,
self.obs_range.high,
)
return {'goals': goals}
def set_position(self, pos):
"""Set the position of the agent."""
self._position[0] = pos[0]
self._position[1] = pos[1]
# ======================================================================= #
# Functions for ImageEnv wrapper #
# ======================================================================= #
def get_image(self, width=None, height=None):
"""Return a black and white image."""
if self.drawer is None:
if width != height:
raise NotImplementedError()
self.drawer = PygameViewer(
screen_width=width,
screen_height=height,
x_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
y_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
render_onscreen=self.render_onscreen,
)
self.draw(self.drawer)
img = self.drawer.get_image()
if self.images_are_rgb:
return img.transpose((1, 0, 2))
else:
r, b = img[:, :, 0], img[:, :, 2]
img = (-r + b).transpose().flatten()
return img
def draw(self, drawer):
"""Create the image corresponding to the current state."""
drawer.fill(Color('white'))
if self.show_goal:
drawer.draw_solid_circle(
self._target_position,
self.target_radius,
Color('green'),
)
drawer.draw_solid_circle(
self._position,
self.ball_radius,
Color('blue'),
)
for wall in self.walls:
drawer.draw_segment(
wall.endpoint1,
wall.endpoint2,
Color('black'),
)
drawer.draw_segment(
wall.endpoint2,
wall.endpoint3,
Color('black'),
)
drawer.draw_segment(
wall.endpoint3,
wall.endpoint4,
Color('black'),
)
drawer.draw_segment(
wall.endpoint4,
wall.endpoint1,
Color('black'),
)
drawer.render()
def render(self, mode='human', close=False):
"""Render the environment state."""
if mode == 'rgb_array':
return self.get_image(self.render_size, self.render_size)
if close:
self.render_drawer = None
return
if self.render_drawer is None or self.render_drawer.terminated:
self.render_drawer = PygameViewer(
self.render_size,
self.render_size,
x_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
y_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
render_onscreen=True,
)
self.draw(self.render_drawer)
self.render_drawer.tick(self.render_dt_msec)
if mode != 'interactive':
self.render_drawer.check_for_exit()
# ======================================================================= #
# Visualization/utility methods #
# ======================================================================= #
def true_model(self, state, action):
"""Return the next position by the agent.
Parameters
----------
state : array_like
the state by the agent
action : array_like
the action by the agent
Returns
-------
array_like
the next position.
"""
velocities = np.clip(action, a_min=-1, a_max=1)
position = state
new_position = position + velocities
return np.clip(
new_position,
a_min=-self.boundary_dist,
a_max=self.boundary_dist,
)
def true_states(self, state, actions):
"""Return the next states given a set of states and actions.
Parameters
----------
state : array_like
the states by the agent
actions : array_like
the actions by the agent
Returns
-------
list of array_like
the next states
"""
real_states = [state]
for action in actions:
next_state = self.true_model(state, action)
real_states.append(next_state)
state = next_state
return real_states
def plot_trajectory(self, ax, states, actions, goal=None):
"""Plot the trajectory of an agent.
Parameters
----------
ax : matplotlib.axes.Axes
the axis object to plot the figure on
states : array_like
the states by the agent
actions : array_like
the actions by the agent
goal : [int, int]
the x,y coordinates of the goal
"""
assert len(states) == len(actions) + 1
x = states[:, 0]
y = -states[:, 1]
num_states = len(states)
plasma_cm = plt.get_cmap('plasma')
for i, state in enumerate(states):
color = plasma_cm(float(i) / num_states)
ax.plot(
state[0],
-state[1],
marker='o',
color=color,
markersize=10,
)
actions_x = actions[:, 0]
actions_y = -actions[:, 1]
ax.quiver(x[:-1],
y[:-1],
x[1:] - x[:-1],
y[1:] - y[:-1],
scale_units='xy',
angles='xy',
scale=1,
width=0.005)
ax.quiver(
x[:-1],
y[:-1],
actions_x,
actions_y,
scale_units='xy',
angles='xy',
scale=1,
color='r',
width=0.0035,
)
ax.plot(
[
-self.boundary_dist,
-self.boundary_dist,
],
[
self.boundary_dist,
-self.boundary_dist,
],
color='k',
linestyle='-',
)
ax.plot(
[
self.boundary_dist,
-self.boundary_dist,
],
[
self.boundary_dist,
self.boundary_dist,
],
color='k',
linestyle='-',
)
ax.plot(
[
self.boundary_dist,
self.boundary_dist,
],
[
self.boundary_dist,
-self.boundary_dist,
],
color='k',
linestyle='-',
)
ax.plot(
[
self.boundary_dist,
-self.boundary_dist,
],
[
-self.boundary_dist,
-self.boundary_dist,
],
color='k',
linestyle='-',
)
if goal is not None:
ax.plot(goal[0], -goal[1], marker='*', color='g', markersize=15)
ax.set_ylim(
-self.boundary_dist - 1,
self.boundary_dist + 1,
)
ax.set_xlim(
-self.boundary_dist - 1,
self.boundary_dist + 1,
)
class Point2DEnv(MultitaskEnv, Serializable):
"""
A little 2D point whose life goal is to reach a target.
"""
def __init__(
self,
render_dt_msec=0,
action_l2norm_penalty=0, # disabled for now
render_onscreen=False,
render_size=84,
reward_type="dense",
action_scale=1.0,
target_radius=0.60,
boundary_dist=4,
ball_radius=0.50,
walls=None,
fixed_goal=None,
randomize_position_on_reset=True,
images_are_rgb=False, # else black and white
show_goal=True,
**kwargs):
if walls is None:
walls = []
if walls is None:
walls = []
if fixed_goal is not None:
fixed_goal = np.array(fixed_goal)
if len(kwargs) > 0:
LOGGER = logging.getLogger(__name__)
LOGGER.log(logging.WARNING, "WARNING, ignoring kwargs:", kwargs)
self.quick_init(locals())
self.render_dt_msec = render_dt_msec
self.action_l2norm_penalty = action_l2norm_penalty
self.render_onscreen = render_onscreen
self.render_size = render_size
self.reward_type = reward_type
self.action_scale = action_scale
self.target_radius = target_radius
self.boundary_dist = boundary_dist
self.ball_radius = ball_radius
self.walls = walls
self.fixed_goal = fixed_goal
self.randomize_position_on_reset = randomize_position_on_reset
self.images_are_rgb = images_are_rgb
self.show_goal = show_goal
self.max_target_distance = self.boundary_dist - self.target_radius
self._target_position = None
self._position = np.zeros((2))
u = np.ones(2)
self.action_space = spaces.Box(-u, u, dtype=np.float32)
o = self.boundary_dist * np.ones(2)
self.obs_range = spaces.Box(-o, o, dtype='float32')
self.observation_space = spaces.Dict([
('observation', self.obs_range),
('desired_goal', self.obs_range),
('achieved_goal', self.obs_range),
('state_observation', self.obs_range),
('state_desired_goal', self.obs_range),
('state_achieved_goal', self.obs_range),
])
self.drawer = None
self.render_drawer = None
def step(self, velocities):
assert self.action_scale <= 1.0
velocities = np.clip(velocities, a_min=-1, a_max=1) * self.action_scale
new_position = self._position + velocities
orig_new_pos = new_position.copy()
for wall in self.walls:
new_position = wall.handle_collision(self._position, new_position)
if sum(new_position != orig_new_pos) > 1:
"""
Hack: sometimes you get caught on two walls at a time. If you
process the input in the other direction, you might only get
caught on one wall instead.
"""
new_position = orig_new_pos.copy()
for wall in self.walls[::-1]:
new_position = wall.handle_collision(self._position,
new_position)
self._position = new_position
self._position = np.clip(
self._position,
a_min=-self.boundary_dist,
a_max=self.boundary_dist,
)
distance_to_target = np.linalg.norm(self._position -
self._target_position)
is_success = distance_to_target < self.target_radius
ob = self._get_obs()
reward = self.compute_reward(velocities, ob)
info = {
'radius': self.target_radius,
'target_position': self._target_position,
'distance_to_target': distance_to_target,
'velocity': velocities,
'speed': np.linalg.norm(velocities),
'is_success': is_success,
}
done = False
return ob, reward, done, info
def reset(self):
self._target_position = self.sample_goal()['state_desired_goal']
if self.randomize_position_on_reset:
self._position = self._sample_position(
self.obs_range.low,
self.obs_range.high,
)
return self._get_obs()
def _position_inside_wall(self, pos):
for wall in self.walls:
if wall.contains_point(pos):
return True
return False
def _sample_position(self, low, high):
pos = np.random.uniform(low, high)
while self._position_inside_wall(pos) is True:
pos = np.random.uniform(low, high)
return pos
def _get_obs(self):
return dict(
observation=self._position.copy(),
desired_goal=self._target_position.copy(),
achieved_goal=self._position.copy(),
state_observation=self._position.copy(),
state_desired_goal=self._target_position.copy(),
state_achieved_goal=self._position.copy(),
)
def compute_rewards(self, actions, obs):
achieved_goals = obs['state_achieved_goal']
desired_goals = obs['state_desired_goal']
d = np.linalg.norm(achieved_goals - desired_goals, axis=-1)
if self.reward_type == "sparse":
return -(d > self.target_radius).astype(np.float32)
elif self.reward_type == "dense":
return -d
elif self.reward_type == 'vectorized_dense':
return -np.abs(achieved_goals - desired_goals)
else:
raise NotImplementedError()
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
for stat_name in [
'radius',
'target_position',
'distance_to_target',
'velocity',
'speed',
'is_success',
]:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def get_goal(self):
return {
'desired_goal': self._target_position.copy(),
'state_desired_goal': self._target_position.copy(),
}
def sample_goals(self, batch_size):
if not self.fixed_goal is None:
goals = np.repeat(self.fixed_goal.copy()[None], batch_size, 0)
else:
goals = np.zeros((batch_size, self.obs_range.low.size))
for b in range(batch_size):
if batch_size > 1:
logging.warning("This is very slow!")
goals[b, :] = self._sample_position(
self.obs_range.low,
self.obs_range.high,
)
return {
'desired_goal': goals,
'state_desired_goal': goals,
}
def set_position(self, pos):
self._position[0] = pos[0]
self._position[1] = pos[1]
"""Functions for ImageEnv wrapper"""
def get_image(self, width=None, height=None):
"""Returns a black and white image"""
if self.drawer is None:
if width != height:
raise NotImplementedError()
self.drawer = PygameViewer(
screen_width=width,
screen_height=height,
x_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
y_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
render_onscreen=self.render_onscreen,
)
self.draw(self.drawer)
img = self.drawer.get_image()
if self.images_are_rgb:
return img.transpose((1, 0, 2))
else:
r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
img = (-r + b).transpose().flatten()
return img
def set_to_goal(self, goal_dict):
goal = goal_dict["state_desired_goal"]
self._position = goal
self._target_position = goal
def get_env_state(self):
return self._get_obs()
def set_env_state(self, state):
position = state["state_observation"]
goal = state["state_desired_goal"]
self._position = position
self._target_position = goal
def draw(self, drawer):
drawer.fill(Color('white'))
if self.show_goal:
drawer.draw_solid_circle(
self._target_position,
self.target_radius,
Color('green'),
)
drawer.draw_solid_circle(
self._position,
self.ball_radius,
Color('blue'),
)
for wall in self.walls:
drawer.draw_segment(
wall.endpoint1,
wall.endpoint2,
Color('black'),
)
drawer.draw_segment(
wall.endpoint2,
wall.endpoint3,
Color('black'),
)
drawer.draw_segment(
wall.endpoint3,
wall.endpoint4,
Color('black'),
)
drawer.draw_segment(
wall.endpoint4,
wall.endpoint1,
Color('black'),
)
drawer.render()
def render(self, mode='human', close=False):
if close:
self.render_drawer = None
return
if self.render_drawer is None or self.render_drawer.terminated:
self.render_drawer = PygameViewer(
self.render_size,
self.render_size,
x_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
y_bounds=(-self.boundary_dist - self.ball_radius,
self.boundary_dist + self.ball_radius),
render_onscreen=True,
)
self.draw(self.render_drawer)
self.render_drawer.tick(self.render_dt_msec)
if mode != 'interactive':
self.render_drawer.check_for_exit()
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
for stat_name in [
'distance_to_target',
]:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
"""Static visualization/utility methods"""
@staticmethod
def true_model(state, action):
velocities = np.clip(action, a_min=-1, a_max=1)
position = state
new_position = position + velocities
return np.clip(
new_position,
a_min=-Point2DEnv.boundary_dist,
a_max=Point2DEnv.boundary_dist,
)
@staticmethod
def true_states(state, actions):
real_states = [state]
for action in actions:
next_state = Point2DEnv.true_model(state, action)
real_states.append(next_state)
state = next_state
return real_states
@staticmethod
def plot_trajectory(ax, states, actions, goal=None):
assert len(states) == len(actions) + 1
x = states[:, 0]
y = -states[:, 1]
num_states = len(states)
plasma_cm = plt.get_cmap('plasma')
for i, state in enumerate(states):
color = plasma_cm(float(i) / num_states)
ax.plot(
state[0],
-state[1],
marker='o',
color=color,
markersize=10,
)
actions_x = actions[:, 0]
actions_y = -actions[:, 1]
ax.quiver(x[:-1],
y[:-1],
x[1:] - x[:-1],
y[1:] - y[:-1],
scale_units='xy',
angles='xy',
scale=1,
width=0.005)
ax.quiver(
x[:-1],
y[:-1],
actions_x,
actions_y,
scale_units='xy',
angles='xy',
scale=1,
color='r',
width=0.0035,
)
ax.plot(
[
-Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
[
Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
color='k',
linestyle='-',
)
ax.plot(
[
Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
[
Point2DEnv.boundary_dist,
Point2DEnv.boundary_dist,
],
color='k',
linestyle='-',
)
ax.plot(
[
Point2DEnv.boundary_dist,
Point2DEnv.boundary_dist,
],
[
Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
color='k',
linestyle='-',
)
ax.plot(
[
Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
[
-Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
color='k',
linestyle='-',
)
if goal is not None:
ax.plot(goal[0], -goal[1], marker='*', color='g', markersize=15)
ax.set_ylim(
-Point2DEnv.boundary_dist - 1,
Point2DEnv.boundary_dist + 1,
)
ax.set_xlim(
-Point2DEnv.boundary_dist - 1,
Point2DEnv.boundary_dist + 1,
)
def initialize_camera(self, init_fctn):
pass
class Point2DEnv(MultitaskEnv, Serializable):
"""
A little 2D point whose life goal is to reach a target.
"""
def __init__(
self,
render_dt_msec=0,
action_l2norm_penalty=0, # disabled for now
render_onscreen=False,
render_size=256,
reward_type="dense",
action_scale=1.0,
target_radius=0.5,
boundary_dist=4,
ball_radius=0.15,
ball_pixel_radius=0,
walls=None,
init_pos_range=None,
target_pos_range=None,
sparse_goals=None,
shuffle_states=False,
images_are_rgb=False, # else black and white
show_goal=True,
n_bins=10,
use_count_reward=False,
show_discrete_grid=False,
convert_obs_to_image=False,
**kwargs):
if walls is None:
walls = []
if walls is None:
walls = []
# if fixed_goal is not None:
# fixed_goal = np.array(fixed_goal)
self.quick_init(locals())
self.render_dt_msec = render_dt_msec
self.action_l2norm_penalty = action_l2norm_penalty
self.render_onscreen = render_onscreen
self.render_size = render_size
self.reward_type = reward_type
self.action_scale = action_scale
self.target_radius = target_radius
self.boundary_dist = boundary_dist
self.ball_radius = ball_radius
self.ball_pixel_radius = ball_pixel_radius
self.walls = walls
self.n_bins = n_bins
self.use_count_reward = use_count_reward
self.show_discrete_grid = show_discrete_grid
self.images_are_rgb = images_are_rgb
self.show_goal = show_goal
self.sparse_goals = sparse_goals
self.shuffle_states = shuffle_states
self.convert_obs_to_image = convert_obs_to_image
self.x_bins = np.linspace(-self.boundary_dist, self.boundary_dist,
self.n_bins)
self.y_bins = np.linspace(-self.boundary_dist, self.boundary_dist,
self.n_bins)
self.bin_counts = np.ones((self.n_bins + 1, self.n_bins + 1))
self._random_mapping_x = np.random.permutation(range(N_SHUFFLE_BINS))
self._random_mapping_y = np.random.permutation(range(N_SHUFFLE_BINS))
self.max_target_distance = self.boundary_dist - self.target_radius
self._target_position = None
self._position = np.zeros((2))
u = np.ones(2)
self.action_space = spaces.Box(-u, u, dtype=np.float32)
o = self.boundary_dist * np.ones(2)
self.obs_range = spaces.Box(-o, o, dtype='float32')
print(
f"[Point2D] Using boundary dist: {self.boundary_dist}, obs space: ({self.obs_range.low}, {self.obs_range.high})"
)
if not init_pos_range:
self.init_pos_range = self.obs_range
else:
assert np.all(np.abs(init_pos_range) < boundary_dist), (
f"Init position must be"
"within the boundaries of the environment: ({-boundary_dist}, {boundary_dist})"
)
low, high = init_pos_range
self.init_pos_range = spaces.Box(np.array(low),
np.array(high),
dtype='float32')
print(
f"[Point2D] Using initial pos range of low={low}, high={high}")
if not target_pos_range:
self.target_pos_range = self.obs_range
else:
assert np.all(np.abs(target_pos_range) < boundary_dist), (
f"Goal position must be"
"within the boundaries of the environment: ({-boundary_dist}, {boundary_dist})"
)
low, high = target_pos_range
self.target_pos_range = spaces.Box(np.array(low),
np.array(high),
dtype='float32')
self.observation_space = spaces.Dict([
('observation', self.obs_range),
('onehot_observation',
spaces.Box(0,
1,
shape=(2 * (self.n_bins + 1), ),
dtype=np.float32)),
('desired_goal', self.obs_range),
('achieved_goal', self.obs_range),
('state_observation', self.obs_range),
('state_desired_goal', self.obs_range),
('state_achieved_goal', self.obs_range),
])
self.drawer = None
self.render_drawer = None
self.reset()
def step(self, velocities):
assert self.action_scale <= 1.0
velocities = np.clip(velocities, a_min=-1, a_max=1) * self.action_scale
new_position = self._position + velocities
orig_new_pos = new_position.copy()
for wall in self.walls:
new_position = wall.handle_collision(self._position, new_position)
if sum(new_position != orig_new_pos) > 1:
"""
Hack: sometimes you get caught on two walls at a time. If you
process the input in the other direction, you might only get
caught on one wall instead.
"""
new_position = orig_new_pos.copy()
for wall in self.walls[::-1]:
new_position = wall.handle_collision(self._position,
new_position)
self._position = new_position
self._position = np.clip(
self._position,
a_min=-self.boundary_dist,
a_max=self.boundary_dist,
)
distance_to_target = np.linalg.norm(self._position -
self._target_position)
is_success = distance_to_target < self.target_radius
ob = self._get_obs()
x_d, y_d = ob['discrete_observation']
self.bin_counts[x_d, y_d] += 1
reward = self.compute_reward(velocities, ob)
info = {
'radius': self.target_radius,
'target_position': self._target_position,
'distance_to_target': distance_to_target,
'velocity': velocities,
'speed': np.linalg.norm(velocities),
'is_success': is_success,
'ext_reward': self._ext_reward,
}
if self.sparse_goals is not None:
# Set reward for anything within threshold of goal(s) to 1
# (ignore any classifier reward)
goals, threshold = self.sparse_goals
min_dist = self.compute_min_dist(goals)
info['is_sparse_success'] = min_dist < threshold
if hasattr(self, 'wall_shape'):
if self.wall_shape == 'medium-maze':
info[
'manhattan_dist_to_target'] = self._medium_maze_manhattan_distance(
ob['state_achieved_goal'], ob['state_desired_goal'])
elif self.wall_shape == 'hard-maze':
info[
'manhattan_dist_to_target'] = self._hard_maze_goal_distance(
ob['state_achieved_goal'], ob['state_desired_goal'])
elif self.wall_shape == 'double-medium-maze':
goals, threshold = self.sparse_goals
info['manhattan_dist_to_sparse_goal'] = self.compute_min_dist(
goals, self._double_maze_manhattan_distance)
info['manhattan_dist_to_target'] = self.compute_min_dist(
ob['state_desired_goal'],
self._double_maze_manhattan_distance)
if self.use_count_reward:
info['count_bonus'] = self._count_bonus
done = False
return ob, reward, done, info
def compute_min_dist(
self,
goals,
distance_fn=lambda s1, s2: np.linalg.norm(s1 - s2, axis=-1)):
dists = []
if len(goals.shape) == 1:
goals = goals[None]
for goal in goals:
dist = distance_fn(self._position, goal)
dists.append(dist)
min_dist = np.array(dists).min(axis=0)
return min_dist
def reset(self):
# TODO: Make this more general
self._target_position = self.sample_goal()['state_desired_goal']
# if self.randomize_position_on_reset:
self._position = self._sample_position(
# self.obs_range.low,
# self.obs_range.high,
self.init_pos_range.low,
self.init_pos_range.high,
)
return self._get_obs()
def _position_inside_wall(self, pos):
for wall in self.walls:
if wall.contains_point(pos):
return True
return False
def _sample_position(self, low, high):
if np.all(low == high):
return low
pos = np.random.uniform(low, high)
while self._position_inside_wall(pos) is True:
pos = np.random.uniform(low, high)
return pos
def clear_bin_counts(self):
self.bin_counts = np.ones((self.n_bins + 1, self.n_bins + 1))
def _discretize_observation(self, obs):
x, y = obs['state_observation'].copy()
x_d, y_d = np.digitize(x, self.x_bins), np.digitize(y, self.y_bins)
return np.array([x_d, y_d])
def _discretized_states(self, states, bins=16, low=-4, high=4):
"""
Converts continuous to discrete states.
Params
- states: A shape (n, 2) batch of continuous observations
- bins: Number of bins for both x and y coordinates
- low: Lowest value (inclusive) for continuous x and y
- high: Highest value (inclusive) for continuous x and y
"""
bin_size = (high - low) / bins
shifted_states = states - low
return np.clip(shifted_states // bin_size, 0,
bins - 1).astype(np.int32)
def _get_shuffled_states(self, states):
if len(states.shape) == 1:
states = states[None]
states = self._discretized_states(states,
bins=N_SHUFFLE_BINS,
low=MAZE_LOW,
high=MAZE_HIGH)
results = (np.hstack([
self._random_mapping_x[states[:, 0]][:, None],
self._random_mapping_y[states[:, 1]][:, None]
]) - 8) / 2
return results
def _state_to_pixel_coords(self,
states,
state_min=-4,
state_max=4,
img_width=28):
coords = (states - state_min) * (img_width / (state_max - state_min))
# Need to subtract 1 because pygame rendering shifts everything to the left/up by 1 for some reason
# (e.g. [-3, -3] is at 5, 5 instead of 6, 6). However this does not apply to the top/left edges.
return np.clip(coords, 0, None).astype(np.int32)
def _get_obs(self):
state_obs = self._get_shuffled_states(self._position.copy()).squeeze() \
if self.shuffle_states else self._position.copy()
obs = collections.OrderedDict(
observation=self._position.copy(),
desired_goal=self._target_position.copy(),
achieved_goal=self._position.copy(),
state_observation=state_obs,
state_desired_goal=self._target_position.copy(),
state_achieved_goal=self._position.copy(),
)
# Update with discretized state
pos_discrete = self._discretize_observation(obs)
pos_onehot = np.eye(self.n_bins + 1)[pos_discrete]
obs['discrete_observation'] = pos_discrete
obs['onehot_observation'] = pos_onehot
return obs
def _hard_maze_goal_distance(self, s1, goal=None):
s1 = s1.copy()
if len(s1.shape) == 1:
s1 = s1[None]
x1, y1 = s1[:, 0], s1[:, 1]
dist = np.zeros(len(s1))
top_section = y1 <= -2
dist[top_section] += np.abs(
2 - x1[top_section]) # Move horizontally to corner
dist[top_section] += -2 - y1[top_section] # Move vertically to corner
x1[top_section], y1[top_section] = 2, -2
right_section = x1 >= 2
dist[right_section] += x1[right_section] - 2 # Move horizontally
dist[right_section] += np.abs(
2 - y1[right_section]) # Move vertically to corner
x1[right_section], y1[right_section] = 2, 2
bottom_section = y1 >= 2
dist[bottom_section] += np.abs(-2 -
x1[bottom_section]) # Move horizontally
dist[bottom_section] += y1[
bottom_section] - 2 # Move vertically to corner
x1[bottom_section], y1[bottom_section] = -2, 2
left_section = x1 <= -2
dist[left_section] += (-2 - x1[left_section]) # Move horizontally
dist[left_section] += np.abs(
y1[left_section]) # Move vertically to corner
x1[left_section], y1[left_section] = -2, 0
mid_section = np.logical_and(y1 <= 0, y1 >= -2)
dist[mid_section] += np.abs(x1[mid_section]) # Move horizontally
dist[mid_section] += np.abs(
y1[mid_section]) # Move vertically to corner
# Move to goal!!!
dist += np.abs(x1 - (-0.5))
dist += np.abs(y1 - 1.25)
return dist
def _medium_maze_manhattan_distance(self, s1, s2):
# Maze wall positions
left_wall_x = -self.boundary_dist / 3
left_wall_bottom = self.inner_wall_max_dist
right_wall_x = self.boundary_dist / 3
right_wall_top = -self.inner_wall_max_dist
s1 = s1.copy()
s2 = s2.copy()
if len(s1.shape) == 1:
s1, s2 = s1[None], s2[None]
# Since maze distances are symmetric, redefine s1,s2 for convenience
# so that points in s1 are to the left of those in s2
combined = np.hstack((s1[:, None], s2[:, None]))
indices = np.argmin((s1[:, 0], s2[:, 0]), axis=0)
s1 = np.take_along_axis(combined, indices[:, None, None],
axis=1).squeeze(axis=1)
s2 = np.take_along_axis(combined, 1 - indices[:, None, None],
axis=1).squeeze(axis=1)
x1 = s1[:, 0]
x2 = s2[:, 0]
# Horizontal movement
x_dist = np.abs(x2 - x1)
# Vertical movement
boundary_ys = [left_wall_bottom, right_wall_top, 0]
boundary_xs = [left_wall_x, right_wall_x, self.boundary_dist, -4.0001]
y_directions = [
1, -1, 0
] # +1 means need to get to bottom, -1 means need to get to top
curr_y, goal_y = s1[:, 1], s2[:, 1]
y_dist = np.zeros(len(s1))
for i in range(3):
# Get all points where s1 and s2 respectively are in the current vertical section of the maze
curr_in_section = x1 <= boundary_xs[i]
goal_in_section = np.logical_and(boundary_xs[i - 1] < x2,
x2 <= boundary_xs[i])
goal_after_section = x2 > boundary_xs[i]
# Both in current section: move directly to goal
mask = np.logical_and(curr_in_section, goal_in_section)
y_dist += mask * np.abs(curr_y - goal_y)
curr_y[mask] = goal_y[mask]
# s2 is further in maze: move to next corner
mask = np.logical_and(
curr_in_section,
np.logical_and(goal_after_section, y_directions[i] *
(boundary_ys[i] - curr_y) > 0))
y_dist += mask * np.clip(
y_directions[i] * (boundary_ys[i] - curr_y), 0, None)
curr_y[mask] = boundary_ys[i]
return x_dist + y_dist
def _double_maze_manhattan_distance(self, s1, s2):
# Maze wall positions
left_wall_bottom = self.inner_wall_max_dist + 1
right_wall_top = -self.inner_wall_max_dist - 1
s1 = s1.copy()
s2 = s2.copy()
if len(s1.shape) == 1:
s1 = s1[None]
if len(s2.shape) == 1:
s2 = s2[None]
# Since maze distances are symmetric, redefine s1,s2 for convenience
# so that points in s1 are to the left of those in s2
combined = np.hstack((s1[:, None], s2[:, None]))
indices = np.argmin((s1[:, 0], s2[:, 0]), axis=0)
s1 = np.take_along_axis(combined, indices[:, None, None],
axis=1).squeeze(axis=1)
s2 = np.take_along_axis(combined, 1 - indices[:, None, None],
axis=1).squeeze(axis=1)
x1 = s1[:, 0]
x2 = s2[:, 0]
# Horizontal movement
x_dist = np.abs(x2 - x1)
# Vertical movement
boundary_ys = [
right_wall_top, left_wall_bottom, right_wall_top, left_wall_bottom,
0
]
boundary_xs = [
-3 / 5 * self.boundary_dist, -1 / 5 * self.boundary_dist,
1 / 5 * self.boundary_dist, 3 / 5 * self.boundary_dist,
self.boundary_dist, -4.0001
]
y_directions = [
-1, +1, -1, +1, 0
] # +1 means need to get to bottom, -1 means need to get to top
curr_y, goal_y = s1[:, 1], s2[:, 1]
y_dist = np.zeros(len(s1))
for i in range(5):
# Get all points where s1 and s2 respectively are in the current vertical section of the maze
curr_in_section = x1 <= boundary_xs[i]
# print(x1)
# print(boundary_xs[i])
goal_in_section = np.logical_and(boundary_xs[i - 1] < x2,
x2 <= boundary_xs[i])
goal_after_section = x2 > boundary_xs[i]
# print("x1:", x1)
# print("x2:", x2)
# print(boundary_xs[i])
# print(boundary_xs[i-1])
# print(curr_in_section, goal_in_section)
# Both in current section: move directly to goal
mask = np.logical_and(curr_in_section, goal_in_section)
y_dist += mask * np.abs(curr_y - goal_y)
curr_y[mask] = goal_y[mask]
# s2 is further in maze: move to next corner
mask = np.logical_and(
curr_in_section,
np.logical_and(goal_after_section, y_directions[i] *
(boundary_ys[i] - curr_y) > 0))
y_dist += mask * np.clip(
y_directions[i] * (boundary_ys[i] - curr_y), 0, None)
curr_y[mask] = boundary_ys[i]
# print(x_dist, y_dist)
return x_dist + y_dist
def compute_rewards(self, actions, obs, reward_type=None):
reward_type = reward_type or self.reward_type
achieved_goals = obs['state_achieved_goal']
desired_goals = obs['state_desired_goal']
d = np.linalg.norm(achieved_goals - desired_goals, axis=-1)
if reward_type == "sparse":
r = -(d > self.target_radius).astype(np.float32)
elif reward_type == "multi-sparse":
goals, threshold = self.sparse_goals
min_dist = np.array([self.compute_min_dist(goals)])
r = -(min_dist > self.target_radius).astype(np.float32)
elif reward_type == "dense":
r = -d
elif reward_type == "vectorized_dense":
r = -np.abs(achieved_goals - desired_goals)
elif reward_type == "medium_maze_ground_truth_manhattan":
"""
# Use maze distance from current position to goal as the negative reward.
# This is specifically for the medium Maze-v0 env, which has two vertical walls.
"""
r = -self._medium_maze_manhattan_distance(achieved_goals,
desired_goals)
elif reward_type == "laplace_smoothing":
# Laplace smoothing: 1 within the goal region, 1/(n+2) at all other states
# (where n is the number of visitations)
r = np.zeros(d.shape)
pos_d = obs['discrete_observation']
r = 1 / (self.bin_counts[pos_d[:, 0], pos_d[:, 1]] + 2)
r[d <= self.target_radius] = 1
elif reward_type == "none":
r = np.zeros(d.shape)
else:
raise NotImplementedError(
f"Unrecognized reward type: {reward_type}")
if self.use_count_reward:
# TODO: Add different count based strategies
pos_d = obs['discrete_observation']
self._ext_reward = r.copy()
self._count_bonus = 1 / np.sqrt(self.bin_counts[pos_d[:, 0],
pos_d[:, 1]])
r += self._count_bonus
else:
self._ext_reward = r.copy()
return r
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
for stat_name in [
'radius',
'target_position',
'distance_to_target',
'velocity',
'speed',
'is_success',
'ext_reward',
'count_bonus',
] + (['is_sparse_success'] if self.sparse_goals is not None else []):
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def get_goal(self):
return {
'desired_goal': self._target_position.copy(),
'state_desired_goal': self._target_position.copy(),
}
def sample_goals(self, batch_size):
# if self.fixed_goal:
# goals = np.repeat(
# self.fixed_goal.copy()[None],
# batch_size,
# 0)
# else:
goals = np.zeros((batch_size, self.obs_range.low.size))
for b in range(batch_size):
if batch_size > 1:
logging.warning("This is very slow!")
goals[b, :] = self._sample_position(
# self.obs_range.low,
# self.obs_range.high,
self.target_pos_range.low,
self.target_pos_range.high,
)
return {
'desired_goal': goals,
'state_desired_goal': goals,
}
def set_position(self, pos):
self._position[0] = pos[0]
self._position[1] = pos[1]
"""Functions for ImageEnv wrapper"""
def get_image(self, width=None, height=None, invert_colors=False):
"""Returns a black and white image"""
if self.drawer is None:
if width != height:
raise NotImplementedError()
self.drawer = PygameViewer(
screen_width=width,
screen_height=height,
# TODO: Action scale = 1 breaks rendering, why?
# x_bounds=(-self.boundary_dist - self.ball_radius,
# self.boundary_dist + self.ball_radius),
# y_bounds=(-self.boundary_dist - self.ball_radius,
# self.boundary_dist + self.ball_radius),
x_bounds=(-self.boundary_dist, self.boundary_dist),
y_bounds=(-self.boundary_dist, self.boundary_dist),
render_onscreen=self.render_onscreen,
)
old_position = self._position
self._position = np.array([-4, -4])
self.draw(self.drawer)
img = self.drawer.get_image()
# img = skimage.transform.resize(img, (width, height), anti_aliasing=True, preserve_range=True)
self._position = old_position
if self.images_are_rgb:
im = img.transpose((1, 0, 2))
else:
r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
im = (-r + b).transpose().flatten()
im[0:2, 0:2] = np.array([255, 255, 255])
im = 255 - im # Invert image - make background black, walls white
im = im[None]
# Set pixel at ball position to blue for each individual observation
pixel_coords = self._state_to_pixel_coords(self._position,
img_width=width)[None]
for i in range(-self.ball_pixel_radius, self.ball_pixel_radius + 1):
for j in range(-self.ball_pixel_radius,
self.ball_pixel_radius + 1):
im[range(len(pixel_coords)),
np.clip(pixel_coords[:, 1] + i, 0, 27),
np.clip(pixel_coords[:, 0] + j, 0, 27)] = np.array(
[0, 0, 255])
self._position = old_position
return im[0] / 255
def set_to_goal(self, goal_dict):
goal = goal_dict["state_desired_goal"]
self._position = goal
self._target_position = goal
def get_env_state(self):
return self._get_obs()
def set_env_state(self, state):
position = state["state_observation"]
goal = state["state_desired_goal"]
self._position = position
self._target_position = goal
def draw(self, drawer):
drawer.fill(Color('white'))
if self.show_discrete_grid:
for x in self.x_bins:
drawer.draw_segment((x, -self.boundary_dist),
(x, self.boundary_dist),
Color(220, 220, 220, 25),
aa=False)
for y in self.y_bins:
drawer.draw_segment((-self.boundary_dist, y),
(self.boundary_dist, y),
Color(220, 220, 220, 25),
aa=False)
if self.show_goal:
drawer.draw_solid_circle(
self._target_position,
self.target_radius,
Color('green'),
)
try:
drawer.draw_solid_circle(
self._position,
self.ball_radius,
Color('blue'),
)
except ValueError as e:
print('\n\n RENDER ERROR \n\n')
for wall in self.walls:
drawer.draw_segment(
wall.endpoint1,
wall.endpoint2,
Color('black'),
)
drawer.draw_segment(
wall.endpoint2,
wall.endpoint3,
Color('black'),
)
drawer.draw_segment(
wall.endpoint3,
wall.endpoint4,
Color('black'),
)
drawer.draw_segment(
wall.endpoint4,
wall.endpoint1,
Color('black'),
)
drawer.render()
def render(self,
mode='human',
width=None,
height=None,
invert_colors=False,
close=False):
if close:
self.render_drawer = None
return
if mode == 'rgb_array':
return self.get_image(width=width,
height=height,
invert_colors=invert_colors)
if self.render_drawer is None or self.render_drawer.terminated:
self.render_drawer = PygameViewer(
self.render_size,
self.render_size,
# x_bounds=(-self.boundary_dist-self.ball_radius,
# self.boundary_dist+self.ball_radius),
# y_bounds=(-self.boundary_dist-self.ball_radius,
# self.boundary_dist+self.ball_radius),
x_bounds=(-self.boundary_dist, self.boundary_dist),
y_bounds=(-self.boundary_dist, self.boundary_dist),
render_onscreen=True,
)
self.draw(self.render_drawer)
self.render_drawer.tick(self.render_dt_msec)
if mode != 'interactive':
self.render_drawer.check_for_exit()
# def get_diagnostics(self, paths, prefix=''):
# statistics = OrderedDict()
# for stat_name in ('distance_to_target', ):
# stat_name = stat_name
# stat = get_stat_in_paths(paths, 'env_infos', stat_name)
# statistics.update(create_stats_ordered_dict(
# '%s%s' % (prefix, stat_name),
# stat,
# always_show_all_stats=True,
# ))
# statistics.update(create_stats_ordered_dict(
# 'Final %s%s' % (prefix, stat_name),
# [s[-1] for s in stat],
# always_show_all_stats=True,
# ))
# return statistics
"""Static visualization/utility methods"""
@staticmethod
def true_model(state, action):
velocities = np.clip(action, a_min=-1, a_max=1)
position = state
new_position = position + velocities
return np.clip(
new_position,
a_min=-Point2DEnv.boundary_dist,
a_max=Point2DEnv.boundary_dist,
)
@staticmethod
def true_states(state, actions):
real_states = [state]
for action in actions:
next_state = Point2DEnv.true_model(state, action)
real_states.append(next_state)
state = next_state
return real_states
@staticmethod
def plot_trajectory(ax, states, actions, goal=None):
assert len(states) == len(actions) + 1
x = states[:, 0]
y = -states[:, 1]
num_states = len(states)
plasma_cm = plt.get_cmap('plasma')
for i, state in enumerate(states):
color = plasma_cm(float(i) / num_states)
ax.plot(
state[0],
-state[1],
marker='o',
color=color,
markersize=10,
)
actions_x = actions[:, 0]
actions_y = -actions[:, 1]
ax.quiver(x[:-1],
y[:-1],
x[1:] - x[:-1],
y[1:] - y[:-1],
scale_units='xy',
angles='xy',
scale=1,
width=0.005)
ax.quiver(
x[:-1],
y[:-1],
actions_x,
actions_y,
scale_units='xy',
angles='xy',
scale=1,
color='r',
width=0.0035,
)
ax.plot(
[
-Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
[
Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
color='k',
linestyle='-',
)
ax.plot(
[
Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
[
Point2DEnv.boundary_dist,
Point2DEnv.boundary_dist,
],
color='k',
linestyle='-',
)
ax.plot(
[
Point2DEnv.boundary_dist,
Point2DEnv.boundary_dist,
],
[
Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
color='k',
linestyle='-',
)
ax.plot(
[
Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
[
-Point2DEnv.boundary_dist,
-Point2DEnv.boundary_dist,
],
color='k',
linestyle='-',
)
if goal is not None:
ax.plot(goal[0], -goal[1], marker='*', color='g', markersize=15)
ax.set_ylim(
-Point2DEnv.boundary_dist - 1,
Point2DEnv.boundary_dist + 1,
)
ax.set_xlim(
-Point2DEnv.boundary_dist - 1,
Point2DEnv.boundary_dist + 1,
)
def initialize_camera(self, init_fctn):
pass