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Remove tensorflow headers and Add doc comments

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This commit is contained in:
kngwyu 2020-07-01 14:12:06 +09:00
parent 91249105b8
commit 14351830bb
9 changed files with 50 additions and 112 deletions
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16
mujoco_maze/__init__.py
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@ -1,5 +1,15 @@
"""
Mujoco Maze
----------
A maze environment using mujoco that supports custom tasks and robots.
"""
import gym
from mujoco_maze.ant import AntEnv
from mujoco_maze.point import PointEnv
from mujoco_maze.maze_task import TaskRegistry
@ -7,8 +17,9 @@ for maze_id in TaskRegistry.keys():
for i, task_cls in enumerate(TaskRegistry.tasks(maze_id)):
gym.envs.register(
id=f"Ant{maze_id}-v{i}",
entry_point="mujoco_maze.ant_maze_env:AntMazeEnv",
entry_point="mujoco_maze.maze_env:MazeEnv",
kwargs=dict(
model_cls=AntEnv,
maze_task=task_cls,
maze_size_scaling=task_cls.MAZE_SIZE_SCALING.ant,
inner_reward_scaling=task_cls.INNER_REWARD_SCALING,
@ -21,8 +32,9 @@ for maze_id in TaskRegistry.keys():
for i, task_cls in enumerate(TaskRegistry.tasks(maze_id)):
gym.envs.register(
id=f"Point{maze_id}-v{i}",
entry_point="mujoco_maze.point_maze_env:PointMazeEnv",
entry_point="mujoco_maze.maze_env:MazeEnv",
kwargs=dict(
model_cls=PointEnv,
maze_task=task_cls,
maze_size_scaling=task_cls.MAZE_SIZE_SCALING.point,
inner_reward_scaling=task_cls.INNER_REWARD_SCALING,

3
mujoco_maze/agent_model.py
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@ -1,4 +1,4 @@
"""Common API definition for Ant and Point.
"""Common APIs for defining mujoco robot.
"""
from abc import ABC, abstractmethod
@ -10,6 +10,7 @@ from gym.utils import EzPickle
class AgentModel(ABC, MujocoEnv, EzPickle):
FILE: str
ORI_IND: int
MANUAL_COLLISION: bool
def __init__(self, file_path: str, frame_skip: int) -> None:
MujocoEnv.__init__(self, file_path, frame_skip)

22
mujoco_maze/ant.py
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@ -1,19 +1,10 @@
# 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.
# ==============================================================================
"""
A four-legged robot as an explorer in the maze.
Based on `models`_ and `gym`_ (both ant and ant-v3).
"""Wrapper for creating the ant environment in gym_mujoco."""
.. _models: https://github.com/tensorflow/models/tree/master/research/efficient-hrl
.. _gym: https://github.com/openai/gym
"""
import math
from typing import Callable, Optional, Tuple
@ -49,6 +40,7 @@ def q_mult(a, b): # multiply two quaternion
class AntEnv(AgentModel):
FILE: str = "ant.xml"
ORI_IND: int = 3
MANUAL_COLLISION: bool = False
def __init__(
self,

21
mujoco_maze/ant_maze_env.py
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@ -1,21 +0,0 @@
# 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 mujoco_maze.ant import AntEnv
from mujoco_maze.maze_env import MazeEnv
class AntMazeEnv(MazeEnv):
MODEL_CLASS = AntEnv

31
mujoco_maze/maze_env.py
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@ -1,19 +1,10 @@
# 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.
# ==============================================================================
"""
Mujoco Maze environment.
Based on `models`_ and `rllab`_.
"""Adapted from rllab maze_env.py."""
.. _models: https://github.com/tensorflow/models/tree/master/research/efficient-hrl
.. _rllab: https://github.com/rll/rllab
"""
import itertools as it
import os
@ -32,11 +23,9 @@ MODEL_DIR = os.path.dirname(os.path.abspath(__file__)) + "/assets"
class MazeEnv(gym.Env):
MODEL_CLASS: Type[AgentModel] = AgentModel
MANUAL_COLLISION: bool = False
def __init__(
self,
model_cls: Type[AgentModel],
maze_task: Type[maze_task.MazeTask] = maze_task.SingleGoalSparseUMaze,
n_bins: int = 0,
sensor_range: float = 3.0,
@ -50,7 +39,7 @@ class MazeEnv(gym.Env):
) -> None:
self._task = maze_task(maze_size_scaling)
xml_path = os.path.join(MODEL_DIR, self.MODEL_CLASS.FILE)
xml_path = os.path.join(MODEL_DIR, model_cls.FILE)
tree = ET.parse(xml_path)
worldbody = tree.find(".//worldbody")
@ -260,7 +249,7 @@ class MazeEnv(gym.Env):
_, file_path = tempfile.mkstemp(text=True, suffix=".xml")
tree.write(file_path)
self.world_tree = tree
self.wrapped_env = self.MODEL_CLASS(*args, file_path=file_path, **kwargs)
self.wrapped_env = model_cls(*args, file_path=file_path, **kwargs)
self.observation_space = self._get_obs_space()
def get_ori(self) -> float:
@ -541,7 +530,7 @@ class MazeEnv(gym.Env):
def step(self, action):
self.t += 1
if self.MANUAL_COLLISION:
if self.wrapped_env.MANUAL_COLLISION:
old_pos = self.wrapped_env.get_xy()
inner_next_obs, inner_reward, _, info = self.wrapped_env.step(action)
new_pos = self.wrapped_env.get_xy()

22
mujoco_maze/maze_env_utils.py
View File

@ -1,19 +1,11 @@
# 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.
# ==============================================================================
"""
Utilities for creating maze.
Based on `models`_ and `rllab`_.
.. _models: https://github.com/tensorflow/models/tree/master/research/efficient-hrl
.. _rllab: https://github.com/rll/rllab
"""
"""Adapted from rllab maze_env_utils.py."""
import itertools as it
import math
from enum import Enum

3
mujoco_maze/maze_task.py
View File

@ -1,3 +1,6 @@
"""Maze tasks that are defined by their map, termination condition, and goals.
"""
from abc import ABC, abstractmethod
from typing import Dict, List, NamedTuple, Type

22
mujoco_maze/point.py
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@ -1,19 +1,10 @@
# 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.
# ==============================================================================
"""
A ball-like robot as an explorer in the maze.
Based on `models`_ and `rllab`_.
"""Wrapper for creating the ant environment in gym_mujoco."""
.. _models: https://github.com/tensorflow/models/tree/master/research/efficient-hrl
.. _rllab: https://github.com/rll/rllab
"""
import math
from typing import Optional, Tuple
@ -27,6 +18,7 @@ from mujoco_maze.agent_model import AgentModel
class PointEnv(AgentModel):
FILE: str = "point.xml"
ORI_IND: int = 2
MANUAL_COLLISION: bool = True
VELOCITY_LIMITS: float = 10.0

22
mujoco_maze/point_maze_env.py
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@ -1,22 +0,0 @@
# 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 mujoco_maze.maze_env import MazeEnv
from mujoco_maze.point import PointEnv
class PointMazeEnv(MazeEnv):
MODEL_CLASS = PointEnv
MANUAL_COLLISION = True