adjust for gym0.26.0

mujoco_maze/agent_model.py

@@ -6,6 +6,7 @@ from typing import Optional
 import numpy as np
 from gym.envs.mujoco.mujoco_env import MujocoEnv
 from gym.utils import EzPickle
+from gym.spaces import Space
 
 
 class AgentModel(ABC, MujocoEnv, EzPickle):
@@ -15,8 +16,8 @@ class AgentModel(ABC, MujocoEnv, EzPickle):
     RADIUS: Optional[float] = None
     OBJBALL_TYPE: Optional[str] = None
 
-    def __init__(self, file_path: str, frame_skip: int) -> None:
-        MujocoEnv.__init__(self, file_path, frame_skip)
+    def __init__(self, file_path: str, frame_skip: int, observation_space: Space) -> None:
+        MujocoEnv.__init__(self, file_path, frame_skip, observation_space)
         EzPickle.__init__(self)
 
     def close(self):

mujoco_maze/maze_env.py

@@ -18,6 +18,8 @@ import numpy as np
 from mujoco_maze import maze_env_utils, maze_task
 from mujoco_maze.agent_model import AgentModel
 
+from gym.core import ObsType
+
 # Directory that contains mujoco xml files.
 MODEL_DIR = os.path.dirname(os.path.abspath(__file__)) + "/assets"
 
@@ -366,7 +368,7 @@ class MazeEnv(gym.Env):
         obs = np.concatenate([wrapped_obs[:3]] + additional_obs + [wrapped_obs[3:]])
         return np.concatenate([obs, *view, np.array([self.t * 0.001])])
 
-    def reset(self) -> np.ndarray:
+    def reset(self, **kwargs) -> Tuple[ObsType, dict]:
         self.t = 0
         self.wrapped_env.reset()
         # Samples a new goal
@@ -376,7 +378,8 @@ class MazeEnv(gym.Env):
         if len(self._init_positions) > 1:
             xy = np.random.choice(self._init_positions)
             self.wrapped_env.set_xy(xy)
-        return self._get_obs()
+        info = {}
+        return self._get_obs(), info
 
     def set_marker(self) -> None:
         for i, goal in enumerate(self._task.goals):
@@ -410,10 +413,11 @@ class MazeEnv(gym.Env):
                 self._websock_server_pipe = start_server(self._websock_port)
             return self._websock_server_pipe.send(self._render_image())
         else:
+            self.wrapped_env.render_mode = mode
             if self.wrapped_env.viewer is None:
-                self.wrapped_env.render(mode, **kwargs)
+                self.wrapped_env.render()
                 self._maybe_move_camera(self.wrapped_env.viewer)
-            return self.wrapped_env.render(mode, **kwargs)
+            return self.wrapped_env.render()
 
     @property
     def action_space(self):

mujoco_maze/point.py

@@ -9,12 +9,22 @@ Based on `models`_ and `rllab`_.
 from typing import Optional, Tuple
 
 import gym
+import mujoco
 import numpy as np
 
 from mujoco_maze.agent_model import AgentModel
 
 
 class PointEnv(AgentModel):
+    metadata = {
+        "render_modes": [
+            "human",
+            "rgb_array",
+            "depth_array",
+        ],
+        "render_fps": 50,
+    }
+
     FILE: str = "point.xml"
     ORI_IND: int = 2
     MANUAL_COLLISION: bool = True
@@ -24,15 +34,15 @@ class PointEnv(AgentModel):
     VELOCITY_LIMITS: float = 10.0
 
     def __init__(self, file_path: Optional[str] = None) -> None:
-        super().__init__(file_path, 1)
         high = np.inf * np.ones(6, dtype=np.float32)
         high[3:] = self.VELOCITY_LIMITS * 1.2
         high[self.ORI_IND] = np.pi
         low = -high
-        self.observation_space = gym.spaces.Box(low, high)
+        observation_space = gym.spaces.Box(low, high)
+        super().__init__(file_path, 1, observation_space)
 
     def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, dict]:
-        qpos = self.sim.data.qpos.copy()
+        qpos = self.data.qpos.copy()
         qpos[2] += action[1]
         # Clip orientation
         if qpos[2] < -np.pi:
@@ -43,26 +53,26 @@ class PointEnv(AgentModel):
         # Compute increment in each direction
         qpos[0] += np.cos(ori) * action[0]
         qpos[1] += np.sin(ori) * action[0]
-        qvel = np.clip(self.sim.data.qvel, -self.VELOCITY_LIMITS, self.VELOCITY_LIMITS)
+        qvel = np.clip(self.data.qvel, -self.VELOCITY_LIMITS, self.VELOCITY_LIMITS)
         self.set_state(qpos, qvel)
         for _ in range(0, self.frame_skip):
-            self.sim.step()
+            mujoco.mj_step(self.model, self.data)
         next_obs = self._get_obs()
         return next_obs, 0.0, False, {}
 
     def _get_obs(self):
         return np.concatenate(
             [
-                self.sim.data.qpos.flat[:3],  # Only point-relevant coords.
-                self.sim.data.qvel.flat[:3],
+                self.data.qpos.flat[:3],  # Only point-relevant coords.
+                self.data.qvel.flat[:3],
             ]
         )
 
     def reset_model(self):
         qpos = self.init_qpos + self.np_random.uniform(
-            size=self.sim.model.nq, low=-0.1, high=0.1
+            size=self.model.nq, low=-0.1, high=0.1
         )
-        qvel = self.init_qvel + self.np_random.randn(self.sim.model.nv) * 0.1
+        qvel = self.init_qvel + self.np_random.random(self.model.nv) * 0.1
 
         # Set everything other than point to original position and 0 velocity.
         qpos[3:] = self.init_qpos[3:]
@@ -71,12 +81,12 @@ class PointEnv(AgentModel):
         return self._get_obs()
 
     def get_xy(self):
-        return self.sim.data.qpos[:2].copy()
+        return self.data.qpos[:2].copy()
 
     def set_xy(self, xy: np.ndarray) -> None:
-        qpos = self.sim.data.qpos.copy()
+        qpos = self.data.qpos.copy()
         qpos[:2] = xy
-        self.set_state(qpos, self.sim.data.qvel)
+        self.set_state(qpos, self.data.qvel)
 
     def get_ori(self):
-        return self.sim.data.qpos[self.ORI_IND]
+        return self.data.qpos[self.ORI_IND]