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*.pyc
**/__pycache__/
**/.pytest_cache/
**/dist/
**/build/
**/*.egg-info/
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# mujoco-maze
Some maze environments for reinforcement learning(RL) using [mujoco-py] and
[openai gym][gym].
Thankfully, this project is based on the code from [tensorflow/models][models], [rllab]
and [deep-skill-chaining][dsc].
## License
This project is licensed under Apache License, Version 2.0
([LICENSE-APACHE](LICENSE) or http://www.apache.org/licenses/LICENSE-2.0).
[dsc]: https://github.com/deep-skill-chaining/deep-skill-chaining
[gym]: https://github.com/openai/gym
[models]: https://github.com/tensorflow/models/tree/master/research/efficient-hrl
[mujoco-py]: https://github.com/openai/mujoco-py
[rllab]: https://github.com/rll/rllab

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# Copyright 2018 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Wrapper for creating the ant environment in gym_mujoco."""
import math
import numpy as np
import mujoco_py
from gym import utils
from gym.envs.mujoco import mujoco_env
def q_inv(a):
return [a[0], -a[1], -a[2], -a[3]]
def q_mult(a, b): # multiply two quaternion
w = a[0] * b[0] - a[1] * b[1] - a[2] * b[2] - a[3] * b[3]
i = a[0] * b[1] + a[1] * b[0] + a[2] * b[3] - a[3] * b[2]
j = a[0] * b[2] - a[1] * b[3] + a[2] * b[0] + a[3] * b[1]
k = a[0] * b[3] + a[1] * b[2] - a[2] * b[1] + a[3] * b[0]
return [w, i, j, k]
class AntEnv(mujoco_env.MujocoEnv, utils.EzPickle):
FILE = "ant.xml"
ORI_IND = 3
def __init__(
self,
file_path=None,
expose_all_qpos=True,
expose_body_coms=None,
expose_body_comvels=None,
):
self._expose_all_qpos = expose_all_qpos
self._expose_body_coms = expose_body_coms
self._expose_body_comvels = expose_body_comvels
self._body_com_indices = {}
self._body_comvel_indices = {}
mujoco_env.MujocoEnv.__init__(self, file_path, 5)
utils.EzPickle.__init__(self)
@property
def physics(self):
# check mujoco version is greater than version 1.50 to call correct physics
# model containing PyMjData object for getting and setting position/velocity
# check https://github.com/openai/mujoco-py/issues/80 for updates to api
if mujoco_py.get_version() >= "1.50":
return self.sim
else:
return self.model
def _step(self, a):
return self.step(a)
def step(self, a):
xposbefore = self.get_body_com("torso")[0]
self.do_simulation(a, self.frame_skip)
xposafter = self.get_body_com("torso")[0]
forward_reward = (xposafter - xposbefore) / self.dt
ctrl_cost = 0.5 * np.square(a).sum()
survive_reward = 1.0
reward = forward_reward - ctrl_cost + survive_reward
_ = self.state_vector()
done = False
ob = self._get_obs()
return (
ob,
reward,
done,
dict(
reward_forward=forward_reward,
reward_ctrl=-ctrl_cost,
reward_survive=survive_reward,
),
)
def _get_obs(self):
# No cfrc observation
if self._expose_all_qpos:
obs = np.concatenate(
[
self.physics.data.qpos.flat[:15], # Ensures only ant obs.
self.physics.data.qvel.flat[:14],
]
)
else:
obs = np.concatenate(
[self.physics.data.qpos.flat[2:15], self.physics.data.qvel.flat[:14],]
)
if self._expose_body_coms is not None:
for name in self._expose_body_coms:
com = self.get_body_com(name)
if name not in self._body_com_indices:
indices = range(len(obs), len(obs) + len(com))
self._body_com_indices[name] = indices
obs = np.concatenate([obs, com])
if self._expose_body_comvels is not None:
for name in self._expose_body_comvels:
comvel = self.get_body_comvel(name)
if name not in self._body_comvel_indices:
indices = range(len(obs), len(obs) + len(comvel))
self._body_comvel_indices[name] = indices
obs = np.concatenate([obs, comvel])
return obs
def reset_model(self):
qpos = self.init_qpos + self.np_random.uniform(
size=self.model.nq, low=-0.1, high=0.1
)
qvel = self.init_qvel + self.np_random.randn(self.model.nv) * 0.1
# Set everything other than ant to original position and 0 velocity.
qpos[15:] = self.init_qpos[15:]
qvel[14:] = 0.0
self.set_state(qpos, qvel)
return self._get_obs()
def viewer_setup(self):
self.viewer.cam.distance = self.model.stat.extent * 0.5
def get_ori(self):
ori = [0, 1, 0, 0]
rot = self.physics.data.qpos[
self.__class__.ORI_IND : self.__class__.ORI_IND + 4
] # take the quaternion
ori = q_mult(q_mult(rot, ori), q_inv(rot))[1:3] # project onto x-y plane
ori = math.atan2(ori[1], ori[0])
return ori
def set_xy(self, xy):
qpos = np.copy(self.physics.data.qpos)
qpos[0] = xy[0]
qpos[1] = xy[1]
qvel = self.physics.data.qvel
self.set_state(qpos, qvel)
def get_xy(self):
return self.physics.data.qpos[:2]

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# Copyright 2018 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from environments.maze_env import MazeEnv
from environments.ant import AntEnv
class AntMazeEnv(MazeEnv):
MODEL_CLASS = AntEnv

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<mujoco model="ant">
<compiler inertiafromgeom="true" angle="degree" coordinate="local" />
<option timestep="0.02" integrator="RK4" />
<custom>
<numeric name="init_qpos" data="0.0 0.0 0.55 1.0 0.0 0.0 0.0 0.0 1.0 0.0 -1.0 0.0 -1.0 0.0 1.0" />
</custom>
<default>
<joint limited="true" armature="1" damping="1" />
<geom condim="3" conaffinity="0" margin="0.01" friction="1 0.5 0.5" solref=".02 1" solimp=".8 .8 .01" rgba="0.8 0.6 0.4 1" density="5.0" />
</default>
<asset>
<texture type="skybox" builtin="gradient" width="100" height="100" rgb1="1 1 1" rgb2="0 0 0" />
<texture name="texgeom" type="cube" builtin="flat" mark="cross" width="127" height="1278" rgb1="0.8 0.6 0.4" rgb2="0.8 0.6 0.4" markrgb="1 1 1" random="0.01" />
<texture name="texplane" type="2d" builtin="checker" rgb1="0 0 0" rgb2="0.8 0.8 0.8" width="100" height="100" />
<material name='MatPlane' texture="texplane" shininess="1" texrepeat="60 60" specular="1" reflectance="0.5" />
<material name='geom' texture="texgeom" texuniform="true" />
</asset>
<worldbody>
<light directional="true" cutoff="100" exponent="1" diffuse="1 1 1" specular=".1 .1 .1" pos="0 0 1.3" dir="-0 0 -1.3" />
<geom name='floor' pos='0 0 0' size='40 40 40' type='plane' conaffinity='1' rgba='0.8 0.9 0.8 1' condim='3' />
<body name="torso" pos="0 0 0.75">
<geom name="torso_geom" type="sphere" size="0.25" pos="0 0 0" />
<joint name="root" type="free" limited="false" pos="0 0 0" axis="0 0 1" margin="0.01" armature="0" damping="0" />
<body name="front_left_leg" pos="0 0 0">
<geom name="aux_1_geom" type="capsule" size="0.08" fromto="0.0 0.0 0.0 0.2 0.2 0.0" />
<body name="aux_1" pos="0.2 0.2 0">
<joint name="hip_1" type="hinge" pos="0.0 0.0 0.0" axis="0 0 1" range="-30 30" />
<geom name="left_leg_geom" type="capsule" size="0.08" fromto="0.0 0.0 0.0 0.2 0.2 0.0" />
<body pos="0.2 0.2 0">
<joint name="ankle_1" type="hinge" pos="0.0 0.0 0.0" axis="-1 1 0" range="30 70" />
<geom name="left_ankle_geom" type="capsule" size="0.08" fromto="0.0 0.0 0.0 0.4 0.4 0.0" />
</body>
</body>
</body>
<body name="front_right_leg" pos="0 0 0">
<geom name="aux_2_geom" type="capsule" size="0.08" fromto="0.0 0.0 0.0 -0.2 0.2 0.0" />
<body name="aux_2" pos="-0.2 0.2 0">
<joint name="hip_2" type="hinge" pos="0.0 0.0 0.0" axis="0 0 1" range="-30 30" />
<geom name="right_leg_geom" type="capsule" size="0.08" fromto="0.0 0.0 0.0 -0.2 0.2 0.0" />
<body pos="-0.2 0.2 0">
<joint name="ankle_2" type="hinge" pos="0.0 0.0 0.0" axis="1 1 0" range="-70 -30" />
<geom name="right_ankle_geom" type="capsule" size="0.08" fromto="0.0 0.0 0.0 -0.4 0.4 0.0" />
</body>
</body>
</body>
<body name="back_leg" pos="0 0 0">
<geom name="aux_3_geom" type="capsule" size="0.08" fromto="0.0 0.0 0.0 -0.2 -0.2 0.0" />
<body name="aux_3" pos="-0.2 -0.2 0">
<joint name="hip_3" type="hinge" pos="0.0 0.0 0.0" axis="0 0 1" range="-30 30" />
<geom name="back_leg_geom" type="capsule" size="0.08" fromto="0.0 0.0 0.0 -0.2 -0.2 0.0" />
<body pos="-0.2 -0.2 0">
<joint name="ankle_3" type="hinge" pos="0.0 0.0 0.0" axis="-1 1 0" range="-70 -30" />
<geom name="third_ankle_geom" type="capsule" size="0.08" fromto="0.0 0.0 0.0 -0.4 -0.4 0.0" />
</body>
</body>
</body>
<body name="right_back_leg" pos="0 0 0">
<geom name="aux_4_geom" type="capsule" size="0.08" fromto="0.0 0.0 0.0 0.2 -0.2 0.0" />
<body name="aux_4" pos="0.2 -0.2 0">
<joint name="hip_4" type="hinge" pos="0.0 0.0 0.0" axis="0 0 1" range="-30 30" />
<geom name="rightback_leg_geom" type="capsule" size="0.08" fromto="0.0 0.0 0.0 0.2 -0.2 0.0" />
<body pos="0.2 -0.2 0">
<joint name="ankle_4" type="hinge" pos="0.0 0.0 0.0" axis="1 1 0" range="30 70" />
<geom name="fourth_ankle_geom" type="capsule" size="0.08" fromto="0.0 0.0 0.0 0.4 -0.4 0.0" />
</body>
</body>
</body>
</body>
</worldbody>
<actuator>
<motor joint="hip_4" ctrlrange="-30.0 30.0" ctrllimited="true" />
<motor joint="ankle_4" ctrlrange="-30.0 30.0" ctrllimited="true" />
<motor joint="hip_1" ctrlrange="-30.0 30.0" ctrllimited="true" />
<motor joint="ankle_1" ctrlrange="-30.0 30.0" ctrllimited="true" />
<motor joint="hip_2" ctrlrange="-30.0 30.0" ctrllimited="true" />
<motor joint="ankle_2" ctrlrange="-30.0 30.0" ctrllimited="true" />
<motor joint="hip_3" ctrlrange="-30.0 30.0" ctrllimited="true" />
<motor joint="ankle_3" ctrlrange="-30.0 30.0" ctrllimited="true" />
</actuator>
</mujoco>

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# Copyright 2018 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Adapted from rllab maze_env.py."""
import os
import tempfile
import xml.etree.ElementTree as ET
import math
import numpy as np
import gym
from environments import maze_env_utils
# Directory that contains mujoco xml files.
MODEL_DIR = "environments/assets"
class MazeEnv(gym.Env):
MODEL_CLASS = None
MAZE_HEIGHT = None
MAZE_SIZE_SCALING = None
def __init__(
self,
maze_id=None,
maze_height=0.5,
maze_size_scaling=8,
n_bins=0,
sensor_range=3.0,
sensor_span=2 * math.pi,
observe_blocks=False,
put_spin_near_agent=False,
top_down_view=False,
manual_collision=False,
*args,
**kwargs,
):
self._maze_id = maze_id
model_cls = self.__class__.MODEL_CLASS
if model_cls is None:
raise "MODEL_CLASS unspecified!"
xml_path = os.path.join(MODEL_DIR, model_cls.FILE)
tree = ET.parse(xml_path)
worldbody = tree.find(".//worldbody")
self.MAZE_HEIGHT = height = maze_height
self.MAZE_SIZE_SCALING = size_scaling = maze_size_scaling
self._n_bins = n_bins
self._sensor_range = sensor_range * size_scaling
self._sensor_span = sensor_span
self._observe_blocks = observe_blocks
self._put_spin_near_agent = put_spin_near_agent
self._top_down_view = top_down_view
self._manual_collision = manual_collision
self.MAZE_STRUCTURE = structure = maze_env_utils.construct_maze(
maze_id=self._maze_id
)
self.elevated = any(
-1 in row for row in structure
) # Elevate the maze to allow for falling.
self.blocks = any(
any(maze_env_utils.can_move(r) for r in row) for row in structure
) # Are there any movable blocks?
torso_x, torso_y = self._find_robot()
self._init_torso_x = torso_x
self._init_torso_y = torso_y
self._init_positions = [
(x - torso_x, y - torso_y) for x, y in self._find_all_robots()
]
self._xy_to_rowcol = lambda x, y: (
2 + (y + size_scaling / 2) / size_scaling,
2 + (x + size_scaling / 2) / size_scaling,
)
self._view = np.zeros(
[5, 5, 3]
) # walls (immovable), chasms (fall), movable blocks
height_offset = 0.0
if self.elevated:
# Increase initial z-pos of ant.
height_offset = height * size_scaling
torso = tree.find(".//body[@name='torso']")
torso.set("pos", "0 0 %.2f" % (0.75 + height_offset))
if self.blocks:
# If there are movable blocks, change simulation settings to perform
# better contact detection.
default = tree.find(".//default")
default.find(".//geom").set("solimp", ".995 .995 .01")
self.movable_blocks = []
for i in range(len(structure)):
for j in range(len(structure[0])):
struct = structure[i][j]
if struct == "r" and self._put_spin_near_agent:
struct = maze_env_utils.Move.SpinXY
if self.elevated and struct not in [-1]:
# Create elevated platform.
ET.SubElement(
worldbody,
"geom",
name="elevated_%d_%d" % (i, j),
pos="%f %f %f"
% (
j * size_scaling - torso_x,
i * size_scaling - torso_y,
height / 2 * size_scaling,
),
size="%f %f %f"
% (
0.5 * size_scaling,
0.5 * size_scaling,
height / 2 * size_scaling,
),
type="box",
material="",
contype="1",
conaffinity="1",
rgba="0.9 0.9 0.9 1",
)
if struct == 1: # Unmovable block.
# Offset all coordinates so that robot starts at the origin.
ET.SubElement(
worldbody,
"geom",
name="block_%d_%d" % (i, j),
pos="%f %f %f"
% (
j * size_scaling - torso_x,
i * size_scaling - torso_y,
height_offset + height / 2 * size_scaling,
),
size="%f %f %f"
% (
0.5 * size_scaling,
0.5 * size_scaling,
height / 2 * size_scaling,
),
type="box",
material="",
contype="1",
conaffinity="1",
rgba="0.4 0.4 0.4 1",
)
elif maze_env_utils.can_move(struct): # Movable block.
# The "falling" blocks are shrunk slightly and increased in mass to
# ensure that it can fall easily through a gap in the platform blocks.
name = "movable_%d_%d" % (i, j)
self.movable_blocks.append((name, struct))
falling = maze_env_utils.can_move_z(struct)
spinning = maze_env_utils.can_spin(struct)
x_offset = 0.25 * size_scaling if spinning else 0.0
y_offset = 0.0
shrink = 0.1 if spinning else 0.99 if falling else 1.0
height_shrink = 0.1 if spinning else 1.0
movable_body = ET.SubElement(
worldbody,
"body",
name=name,
pos="%f %f %f"
% (
j * size_scaling - torso_x + x_offset,
i * size_scaling - torso_y + y_offset,
height_offset + height / 2 * size_scaling * height_shrink,
),
)
ET.SubElement(
movable_body,
"geom",
name="block_%d_%d" % (i, j),
pos="0 0 0",
size="%f %f %f"
% (
0.5 * size_scaling * shrink,
0.5 * size_scaling * shrink,
height / 2 * size_scaling * height_shrink,
),
type="box",
material="",
mass="0.001" if falling else "0.0002",
contype="1",
conaffinity="1",
rgba="0.9 0.1 0.1 1",
)
if maze_env_utils.can_move_x(struct):
ET.SubElement(
movable_body,
"joint",
armature="0",
axis="1 0 0",
damping="0.0",
limited="true" if falling else "false",
range="%f %f" % (-size_scaling, size_scaling),
margin="0.01",
name="movable_x_%d_%d" % (i, j),
pos="0 0 0",
type="slide",
)
if maze_env_utils.can_move_y(struct):
ET.SubElement(
movable_body,
"joint",
armature="0",
axis="0 1 0",
damping="0.0",
limited="true" if falling else "false",
range="%f %f" % (-size_scaling, size_scaling),
margin="0.01",
name="movable_y_%d_%d" % (i, j),
pos="0 0 0",
type="slide",
)
if maze_env_utils.can_move_z(struct):
ET.SubElement(
movable_body,
"joint",
armature="0",
axis="0 0 1",
damping="0.0",
limited="true",
range="%f 0" % (-height_offset),
margin="0.01",
name="movable_z_%d_%d" % (i, j),
pos="0 0 0",
type="slide",
)
if maze_env_utils.can_spin(struct):
ET.SubElement(
movable_body,
"joint",
armature="0",
axis="0 0 1",
damping="0.0",
limited="false",
name="spinable_%d_%d" % (i, j),
pos="0 0 0",
type="ball",
)
torso = tree.find(".//body[@name='torso']")
geoms = torso.findall(".//geom")
for geom in geoms:
if "name" not in geom.attrib:
raise Exception("Every geom of the torso must have a name " "defined")
_, file_path = tempfile.mkstemp(text=True, suffix=".xml")
tree.write(file_path)
self.wrapped_env = model_cls(*args, file_path=file_path, **kwargs)
def get_ori(self):
return self.wrapped_env.get_ori()
def get_top_down_view(self):
self._view = np.zeros_like(self._view)
def valid(row, col):
return self._view.shape[0] > row >= 0 and self._view.shape[1] > col >= 0
def update_view(x, y, d, row=None, col=None):
if row is None or col is None:
x = x - self._robot_x
y = y - self._robot_y
th = self._robot_ori
row, col = self._xy_to_rowcol(x, y)
update_view(x, y, d, row=row, col=col)
return
row, row_frac, col, col_frac = int(row), row % 1, int(col), col % 1
if row_frac < 0:
row_frac += 1
if col_frac < 0:
col_frac += 1
if valid(row, col):
self._view[row, col, d] += (
min(1.0, row_frac + 0.5) - max(0.0, row_frac - 0.5)
) * (min(1.0, col_frac + 0.5) - max(0.0, col_frac - 0.5))
if valid(row - 1, col):
self._view[row - 1, col, d] += (max(0.0, 0.5 - row_frac)) * (
min(1.0, col_frac + 0.5) - max(0.0, col_frac - 0.5)
)
if valid(row + 1, col):
self._view[row + 1, col, d] += (max(0.0, row_frac - 0.5)) * (
min(1.0, col_frac + 0.5) - max(0.0, col_frac - 0.5)
)
if valid(row, col - 1):
self._view[row, col - 1, d] += (
min(1.0, row_frac + 0.5) - max(0.0, row_frac - 0.5)
) * (max(0.0, 0.5 - col_frac))
if valid(row, col + 1):
self._view[row, col + 1, d] += (
min(1.0, row_frac + 0.5) - max(0.0, row_frac - 0.5)
) * (max(0.0, col_frac - 0.5))
if valid(row - 1, col - 1):
self._view[row - 1, col - 1, d] += (max(0.0, 0.5 - row_frac)) * max(
0.0, 0.5 - col_frac
)
if valid(row - 1, col + 1):
self._view[row - 1, col + 1, d] += (max(0.0, 0.5 - row_frac)) * max(
0.0, col_frac - 0.5
)
if valid(row + 1, col + 1):
self._view[row + 1, col + 1, d] += (max(0.0, row_frac - 0.5)) * max(
0.0, col_frac - 0.5
)
if valid(row + 1, col - 1):
self._view[row + 1, col - 1, d] += (max(0.0, row_frac - 0.5)) * max(
0.0, 0.5 - col_frac
)
# Draw ant.
robot_x, robot_y = self.wrapped_env.get_body_com("torso")[:2]
self._robot_x = robot_x
self._robot_y = robot_y
self._robot_ori = self.get_ori()
structure = self.MAZE_STRUCTURE
size_scaling = self.MAZE_SIZE_SCALING
height = self.MAZE_HEIGHT
# Draw immovable blocks and chasms.
for i in range(len(structure)):
for j in range(len(structure[0])):
if structure[i][j] == 1: # Wall.
update_view(
j * size_scaling - self._init_torso_x,
i * size_scaling - self._init_torso_y,
0,
)
if structure[i][j] == -1: # Chasm.
update_view(
j * size_scaling - self._init_torso_x,
i * size_scaling - self._init_torso_y,
1,
)
# Draw movable blocks.
for block_name, block_type in self.movable_blocks:
block_x, block_y = self.wrapped_env.get_body_com(block_name)[:2]
update_view(block_x, block_y, 2)
return self._view
def get_range_sensor_obs(self):
"""Returns egocentric range sensor observations of maze."""
robot_x, robot_y, robot_z = self.wrapped_env.get_body_com("torso")[:3]
ori = self.get_ori()
structure = self.MAZE_STRUCTURE
size_scaling = self.MAZE_SIZE_SCALING
height = self.MAZE_HEIGHT
segments = []
# Get line segments (corresponding to outer boundary) of each immovable
# block or drop-off.
for i in range(len(structure)):
for j in range(len(structure[0])):
if structure[i][j] in [1, -1]: # There's a wall or drop-off.
cx = j * size_scaling - self._init_torso_x
cy = i * size_scaling - self._init_torso_y
x1 = cx - 0.5 * size_scaling
x2 = cx + 0.5 * size_scaling
y1 = cy - 0.5 * size_scaling
y2 = cy + 0.5 * size_scaling
struct_segments = [
((x1, y1), (x2, y1)),
((x2, y1), (x2, y2)),
((x2, y2), (x1, y2)),
((x1, y2), (x1, y1)),
]
for seg in struct_segments:
segments.append(dict(segment=seg, type=structure[i][j],))
# Get line segments (corresponding to outer boundary) of each movable
# block within the agent's z-view.
for block_name, block_type in self.movable_blocks:
block_x, block_y, block_z = self.wrapped_env.get_body_com(block_name)[:3]
if (
block_z + height * size_scaling / 2 >= robot_z
and robot_z >= block_z - height * size_scaling / 2
): # Block in view.
x1 = block_x - 0.5 * size_scaling
x2 = block_x + 0.5 * size_scaling
y1 = block_y - 0.5 * size_scaling
y2 = block_y + 0.5 * size_scaling
struct_segments = [
((x1, y1), (x2, y1)),
((x2, y1), (x2, y2)),
((x2, y2), (x1, y2)),
((x1, y2), (x1, y1)),
]
for seg in struct_segments:
segments.append(dict(segment=seg, type=block_type,))
sensor_readings = np.zeros((self._n_bins, 3)) # 3 for wall, drop-off, block
for ray_idx in range(self._n_bins):
ray_ori = (
ori
- self._sensor_span * 0.5
+ (2 * ray_idx + 1.0) / (2 * self._n_bins) * self._sensor_span
)
ray_segments = []
# Get all segments that intersect with ray.
for seg in segments:
p = maze_env_utils.ray_segment_intersect(
ray=((robot_x, robot_y), ray_ori), segment=seg["segment"]
)
if p is not None:
ray_segments.append(
dict(
segment=seg["segment"],
type=seg["type"],
ray_ori=ray_ori,
distance=maze_env_utils.point_distance(
p, (robot_x, robot_y)
),
)
)
if len(ray_segments) > 0:
# Find out which segment is intersected first.
first_seg = sorted(ray_segments, key=lambda x: x["distance"])[0]
seg_type = first_seg["type"]
idx = (
0
if seg_type == 1
else 1 # Wall.
if seg_type == -1
else 2 # Drop-off.
if maze_env_utils.can_move(seg_type)
else None # Block.
)
if first_seg["distance"] <= self._sensor_range:
sensor_readings[ray_idx][idx] = (
self._sensor_range - first_seg["distance"]
) / self._sensor_range
return sensor_readings
def _get_obs(self):
wrapped_obs = self.wrapped_env._get_obs()
if self._top_down_view:
view = [self.get_top_down_view().flat]
else:
view = []
if self._observe_blocks:
additional_obs = []
for block_name, block_type in self.movable_blocks:
additional_obs.append(self.wrapped_env.get_body_com(block_name))
wrapped_obs = np.concatenate(
[wrapped_obs[:3]] + additional_obs + [wrapped_obs[3:]]
)
range_sensor_obs = self.get_range_sensor_obs()
return np.concatenate(
[wrapped_obs, range_sensor_obs.flat] + view + [[self.t * 0.001]]
)
def reset(self):
self.t = 0
self.trajectory = []
self.wrapped_env.reset()
if len(self._init_positions) > 1:
xy = random.choice(self._init_positions)
self.wrapped_env.set_xy(xy)
return self._get_obs()
@property
def viewer(self):
return self.wrapped_env.viewer
def render(self, *args, **kwargs):
return self.wrapped_env.render(*args, **kwargs)
@property
def observation_space(self):
shape = self._get_obs().shape
high = np.inf * np.ones(shape)
low = -high
return gym.spaces.Box(low, high)
@property
def action_space(self):
return self.wrapped_env.action_space
def _find_robot(self):
structure = self.MAZE_STRUCTURE
size_scaling = self.MAZE_SIZE_SCALING
for i in range(len(structure)):
for j in range(len(structure[0])):
if structure[i][j] == "r":
return j * size_scaling, i * size_scaling
assert False, "No robot in maze specification."
def _find_all_robots(self):
structure = self.MAZE_STRUCTURE
size_scaling = self.MAZE_SIZE_SCALING
coords = []
for i in range(len(structure)):
for j in range(len(structure[0])):
if structure[i][j] == "r":
coords.append((j * size_scaling, i * size_scaling))
return coords
def _is_in_collision(self, pos):
x, y = pos
structure = self.MAZE_STRUCTURE
size_scaling = self.MAZE_SIZE_SCALING
for i in range(len(structure)):
for j in range(len(structure[0])):
if structure[i][j] == 1:
minx = j * size_scaling - size_scaling * 0.5 - self._init_torso_x
maxx = j * size_scaling + size_scaling * 0.5 - self._init_torso_x
miny = i * size_scaling - size_scaling * 0.5 - self._init_torso_y
maxy = i * size_scaling + size_scaling * 0.5 - self._init_torso_y
if minx <= x <= maxx and miny <= y <= maxy:
return True
return False
def step(self, action):
self.t += 1
if self._manual_collision:
old_pos = self.wrapped_env.get_xy()
inner_next_obs, inner_reward, done, info = self.wrapped_env.step(action)
new_pos = self.wrapped_env.get_xy()
if self._is_in_collision(new_pos):
self.wrapped_env.set_xy(old_pos)
else:
inner_next_obs, inner_reward, done, info = self.wrapped_env.step(action)
next_obs = self._get_obs()
done = False
return next_obs, inner_reward, done, info

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# Copyright 2018 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Adapted from rllab maze_env_utils.py."""
import math
class Move(object):
X = 11
Y = 12
Z = 13
XY = 14
XZ = 15
YZ = 16
XYZ = 17
SpinXY = 18
def can_move_x(movable):
return movable in [Move.X, Move.XY, Move.XZ, Move.XYZ, Move.SpinXY]
def can_move_y(movable):
return movable in [Move.Y, Move.XY, Move.YZ, Move.XYZ, Move.SpinXY]
def can_move_z(movable):
return movable in [Move.Z, Move.XZ, Move.YZ, Move.XYZ]
def can_spin(movable):
return movable in [Move.SpinXY]
def can_move(movable):
return can_move_x(movable) or can_move_y(movable) or can_move_z(movable)
def construct_maze(maze_id="Maze"):
if maze_id == "Maze":
structure = [
[1, 1, 1, 1, 1],
[1, "r", 0, 0, 1],
[1, 1, 1, 0, 1],
[1, 0, 0, 0, 1],
[1, 1, 1, 1, 1],
]
elif maze_id == "Push":
structure = [
[1, 1, 1, 1, 1],
[1, 0, "r", 1, 1],
[1, 0, Move.XY, 0, 1],
[1, 1, 0, 1, 1],
[1, 1, 1, 1, 1],
]
elif maze_id == "Fall":
structure = [
[1, 1, 1, 1],
[1, "r", 0, 1],
[1, 0, Move.YZ, 1],
[1, -1, -1, 1],
[1, 0, 0, 1],
[1, 1, 1, 1],
]
elif maze_id == "Block":
O = "r"
structure = [
[1, 1, 1, 1, 1],
[1, O, 0, 0, 1],
[1, 0, 0, 0, 1],
[1, 0, 0, 0, 1],
[1, 1, 1, 1, 1],
]
elif maze_id == "BlockMaze":
O = "r"
structure = [
[1, 1, 1, 1],
[1, O, 0, 1],
[1, 1, 0, 1],
[1, 0, 0, 1],
[1, 1, 1, 1],
]
else:
raise NotImplementedError("The provided MazeId %s is not recognized" % maze_id)
return structure
def line_intersect(pt1, pt2, ptA, ptB):
"""
Taken from https://www.cs.hmc.edu/ACM/lectures/intersections.html
this returns the intersection of Line(pt1,pt2) and Line(ptA,ptB)
"""
DET_TOLERANCE = 0.00000001
# the first line is pt1 + r*(pt2-pt1)
# in component form:
x1, y1 = pt1
x2, y2 = pt2
dx1 = x2 - x1
dy1 = y2 - y1
# the second line is ptA + s*(ptB-ptA)
x, y = ptA
xB, yB = ptB
dx = xB - x
dy = yB - y
DET = -dx1 * dy + dy1 * dx
if math.fabs(DET) < DET_TOLERANCE:
return (0, 0, 0, 0, 0)
# now, the determinant should be OK
DETinv = 1.0 / DET
# find the scalar amount along the "self" segment
r = DETinv * (-dy * (x - x1) + dx * (y - y1))
# find the scalar amount along the input line
s = DETinv * (-dy1 * (x - x1) + dx1 * (y - y1))
# return the average of the two descriptions
xi = (x1 + r * dx1 + x + s * dx) / 2.0
yi = (y1 + r * dy1 + y + s * dy) / 2.0
return (xi, yi, 1, r, s)
def ray_segment_intersect(ray, segment):
"""
Check if the ray originated from (x, y) with direction theta intersects the line segment (x1, y1) -- (x2, y2),
and return the intersection point if there is one
"""
(x, y), theta = ray
# (x1, y1), (x2, y2) = segment
pt1 = (x, y)
len = 1
pt2 = (x + len * math.cos(theta), y + len * math.sin(theta))
xo, yo, valid, r, s = line_intersect(pt1, pt2, *segment)
if valid and r >= 0 and 0 <= s <= 1:
return (xo, yo)
return None
def point_distance(p1, p2):
x1, y1 = p1
x2, y2 = p2
return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5

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# Copyright 2018 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Wrapper for creating the ant environment in gym_mujoco."""
import math
import numpy as np
import mujoco_py
from gym import utils
from gym.envs.mujoco import mujoco_env
class PointEnv(mujoco_env.MujocoEnv, utils.EzPickle):
FILE = "point.xml"
ORI_IND = 2
def __init__(self, file_path=None, expose_all_qpos=True):
self._expose_all_qpos = expose_all_qpos
mujoco_env.MujocoEnv.__init__(self, file_path, 1)
utils.EzPickle.__init__(self)
@property
def physics(self):
# check mujoco version is greater than version 1.50 to call correct physics
# model containing PyMjData object for getting and setting position/velocity
# check https://github.com/openai/mujoco-py/issues/80 for updates to api
if mujoco_py.get_version() >= "1.50":
return self.sim
else:
return self.model
def _step(self, a):
return self.step(a)
def step(self, action):
action[0] = 0.2 * action[0]
qpos = np.copy(self.physics.data.qpos)
qpos[2] += action[1]
ori = qpos[2]
# compute increment in each direction
dx = math.cos(ori) * action[0]
dy = math.sin(ori) * action[0]
# ensure that the robot is within reasonable range
qpos[0] = np.clip(qpos[0] + dx, -100, 100)
qpos[1] = np.clip(qpos[1] + dy, -100, 100)
qvel = self.physics.data.qvel
self.set_state(qpos, qvel)
for _ in range(0, self.frame_skip):
self.physics.step()
next_obs = self._get_obs()
reward = 0
done = False
info = {}
return next_obs, reward, done, info
def _get_obs(self):
if self._expose_all_qpos:
return np.concatenate(
[
self.physics.data.qpos.flat[:3], # Only point-relevant coords.
self.physics.data.qvel.flat[:3],
]
)
return np.concatenate(
[self.physics.data.qpos.flat[2:3], self.physics.data.qvel.flat[:3]]
)
def reset_model(self):
qpos = self.init_qpos + self.np_random.uniform(
size=self.physics.model.nq, low=-0.1, high=0.1
)
qvel = self.init_qvel + self.np_random.randn(self.physics.model.nv) * 0.1
# Set everything other than point to original position and 0 velocity.
qpos[3:] = self.init_qpos[3:]
qvel[3:] = 0.0
self.set_state(qpos, qvel)
return self._get_obs()
def get_ori(self):
return self.physics.data.qpos[self.__class__.ORI_IND]
def set_xy(self, xy):
qpos = np.copy(self.physics.data.qpos)
qpos[0] = xy[0]
qpos[1] = xy[1]
qvel = self.physics.data.qvel

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mujoco_maze/point_maze_env.py Normal file
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# Copyright 2018 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from environments.maze_env import MazeEnv
from environments.point import PointEnv
class PointMazeEnv(MazeEnv):
MODEL_CLASS = PointEnv

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pyproject.toml Normal file
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[tool.black]
line-length = 88
target-version = ['py36']
include = '\.pyi?$'
exclude = '''
(
/(
\.eggs
| \.git
| \.mypy_cache
| _build
| buck-out
| build
| dist
)/
)
'''

18
setup.cfg Normal file
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[flake8]
max-line-length = 88
ignore = W391, W503, F821, E203, E231
[mypy]
python_version = 3.6
ignore_missing_imports = True
warn_unused_configs = True
[isort]
multi_line_output=3
include_trailing_comma=True
force_grid_wrap=0
use_parentheses=True
line_length=88
lines_between_types=0
combine_as_imports=True
known_first_party=rainy, tests