Refactor using AgentModel

mujoco_maze/agent_model.py

@@ -0,0 +1,39 @@
+"""Common API definition for Ant and Point.
+"""
+from abc import ABC, abstractmethod
+from gym.envs.mujoco.mujoco_env import MujocoEnv
+from gym.utils import EzPickle
+import numpy as np
+from typing import Tuple
+
+
+class AgentModel(ABC, MujocoEnv, EzPickle):
+    FILE: str
+    ORI_IND: int
+
+    def __init__(self, file_path: str, frame_skip: int) -> None:
+        MujocoEnv.__init__(self, file_path, frame_skip)
+        EzPickle.__init__(self)
+
+    @abstractmethod
+    def _get_obs(self) -> np.ndarray:
+        """Returns the observation from the model.
+        """
+        pass
+
+    @abstractmethod
+    def get_xy(self) -> Tuple[float, float]:
+        """Returns the coordinate of the agent.
+        """
+        pass
+
+    @abstractmethod
+    def set_xy(self, xy: Tuple[float, float]) -> None:
+        """Set the coordinate of the agent.
+        """
+        pass
+
+    @abstractmethod
+    def get_ori(self) -> float:
+        pass
+

mujoco_maze/ant.py

@@ -17,8 +17,8 @@
 
 import math
 import numpy as np
-from gym import utils
-from gym.envs.mujoco import mujoco_env
+
+from mujoco_maze.agent_model import AgentModel
 
 
 def q_inv(a):
@@ -33,7 +33,7 @@ def q_mult(a, b):  # multiply two quaternion
     return [w, i, j, k]
 
 
-class AntEnv(mujoco_env.MujocoEnv, utils.EzPickle):
+class AntEnv(AgentModel):
     FILE = "ant.xml"
     ORI_IND = 3
 
@@ -50,8 +50,7 @@ class AntEnv(mujoco_env.MujocoEnv, utils.EzPickle):
         self._body_com_indices = {}
         self._body_comvel_indices = {}
 
-        mujoco_env.MujocoEnv.__init__(self, file_path, 5)
-        utils.EzPickle.__init__(self)
+        super().__init__(file_path, 5)
 
     def _step(self, a):
         return self.step(a)
@@ -126,9 +125,8 @@ class AntEnv(mujoco_env.MujocoEnv, utils.EzPickle):
 
     def get_ori(self):
         ori = [0, 1, 0, 0]
-        rot = self.sim.data.qpos[
-            self.__class__.ORI_IND : self.__class__.ORI_IND + 4
-        ]  # take the quaternion
+        ori_ind = self.ORI_IND
+        rot = self.sim.data.qpos[ori_ind: 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

mujoco_maze/maze_env.py

@@ -22,6 +22,9 @@ import math
 import numpy as np
 import gym
 
+from typing import Type
+
+from mujoco_maze.agent_model import AgentModel
 from mujoco_maze import maze_env_utils
 
 # Directory that contains mujoco xml files.
@@ -29,7 +32,7 @@ MODEL_DIR = os.path.dirname(os.path.abspath(__file__)) + "/assets"
 
 
 class MazeEnv(gym.Env):
-    MODEL_CLASS = None
+    MODEL_CLASS: Type[AgentModel] = AgentModel
 
     MAZE_HEIGHT = None
     MAZE_SIZE_SCALING = None
@@ -51,10 +54,7 @@ class MazeEnv(gym.Env):
     ):
         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)
+        xml_path = os.path.join(MODEL_DIR, self.MODEL_CLASS.FILE)
         tree = ET.parse(xml_path)
         worldbody = tree.find(".//worldbody")
 
@@ -264,7 +264,7 @@ class MazeEnv(gym.Env):
         _, file_path = tempfile.mkstemp(text=True, suffix=".xml")
         tree.write(file_path)
 
-        self.wrapped_env = model_cls(*args, file_path=file_path, **kwargs)
+        self.wrapped_env = self.MODEL_CLASS(*args, file_path=file_path, **kwargs)
 
     def get_ori(self):
         return self.wrapped_env.get_ori()
@@ -477,7 +477,7 @@ class MazeEnv(gym.Env):
         self.t = 0
         self.wrapped_env.reset()
         if len(self._init_positions) > 1:
-            xy = random.choice(self._init_positions)
+            xy = np.random.choice(self._init_positions)
             self.wrapped_env.set_xy(xy)
         return self._get_obs()
 

mujoco_maze/point.py

@@ -17,19 +17,18 @@
 
 import math
 import numpy as np
-from gym import utils
-from gym.envs.mujoco import mujoco_env
+
+from mujoco_maze.agent_model import AgentModel
 
 
-class PointEnv(mujoco_env.MujocoEnv, utils.EzPickle):
+class PointEnv(AgentModel):
     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)
+        super().__init__(file_path, 1)
 
     def _step(self, a):
         return self.step(a)

pyproject.toml

@@ -10,9 +10,9 @@ repository = "https://github.com/kngwyu/mujoco-maze"
 homepage = "https://github.com/kngwyu/mujoco-maze"
 
 [tool.poetry.dependencies]
-python = "^3.5"  # Compatible python versions must be declared here
-gym = ">=0.14"
-mujoco-py = ">=2.0"
+python = "^3.6"  # Compatible python versions must be declared here
+gym = ">=0.16"
+mujoco-py = ">=1.5"
 
 [tool.poetry.dev-dependencies]
 pytest = "^3.0"