Update pre commit

.github/workflows/build-docs-version.yml

@@ -55,4 +55,4 @@ jobs:
           folder: _build
           clean-exclude: |
             v*.*/
-            main
+            main

.github/workflows/manual-build-docs-version.yml

@@ -67,4 +67,4 @@ jobs:
           folder: _build
           clean-exclude: |
             v*.*/
-            main
+            main

.gitignore

@@ -136,4 +136,3 @@ dmypy.json
 
 # Cython debug symbols
 cython_debug/
-

.pre-commit-config.yaml

@@ -1,23 +1,49 @@
 # See https://pre-commit.com for more information
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: check-symlinks
+      - id: destroyed-symlinks
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-toml
+      - id: check-ast
+      - id: check-added-large-files
+      - id: check-merge-conflict
+      - id: check-executables-have-shebangs
+      - id: check-shebang-scripts-are-executable
+      - id: detect-private-key
+      - id: debug-statements
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.3.0
+    hooks:
+      - id: codespell
+        args:
+          - --ignore-words-list=ser,theses,bu,ois
   - repo: https://github.com/PyCQA/flake8
-    rev: 6.0.0
+    rev: 7.0.0
     hooks:
       - id: flake8
         args:
-          - '--per-file-ignores=**/__init__.py:F401,F403,E402'
+#          - '--per-file-ignores=*/__init__.py:F401'
           - --ignore=E203,W503,E741,E731
           - --max-complexity=30
           - --max-line-length=456
           - --show-source
           - --statistics
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v3.16.0
+    hooks:
+      - id: pyupgrade
+        args: ["--py38-plus"]
   - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
+    rev: 5.13.2
     hooks:
       - id: isort
-        args: ["--profile", "black"]
   - repo: https://github.com/python/black
-    rev: 23.3.0
+    rev: 24.4.2
     hooks:
       - id: black

README.md

@@ -235,5 +235,3 @@ Master theses
 *   [Multi-Agent Reinforcement Learning with Application on Traffic Flow Control](https://www.diva-portal.org/smash/get/diva2:1573441/FULLTEXT01.pdf) (Jun 2021)
 *   [Deep Reinforcement Learning for Automated Parking](https://repositorio-aberto.up.pt/bitstream/10216/136074/2/494682.pdf) (Aug 2021)
 *   [Deep Reinforcement Learning and Imitation Learning for Autonomous Driving in a Minimalist Environment](https://www.academia.edu/107587654/Deep_Reinforcement_Learning_and_Imitation_Learning_for_Autonomous_Driving_in_a_Minimalist_Environment) (Jun 2021)
-
-

docs/404.md

@@ -5,4 +5,4 @@ hide-toc: true
 
 # 404
 
-## Page Not Found
+## Page Not Found

docs/bibliography/biblio.bib

@@ -77,4 +77,4 @@ @misc{Qi2017pointnet
       eprint={1612.00593},
       archivePrefix={arXiv},
       primaryClass={cs.CV}
-}
+}

docs/conf.py

@@ -20,6 +20,7 @@
 
 import highway_env  # noqa: F401
 
+
 project = "highway-env"
 copyright = "2023 Farama Foundation"
 author = "Farama Foundation"

docs/index.md

@@ -42,4 +42,4 @@ bibliography/index
 
 Github <https://github.com/Farama-Foundation/HighwayEnv>
 Contribute to the Docs <https://github.com/Farama-Foundation/HighwayEnv/blob/main/docs/README.md>
-```
+```

docs/observations/index.md

@@ -14,7 +14,7 @@ import gymnasium as gym
 import highway_env
 
 env = gym.make(
-    'highway-v0', 
+    'highway-v0',
     config={
         "observation": {
             "type": "OccupancyGrid",
@@ -246,10 +246,10 @@ For instance, consider a vehicle at 25m on the right-lane of the ego-vehicle and
 ```{eval-rst}
 .. table:: $15$ m/s
 
-==  ==  ==  ==  ==  ==  ==  ==  ==  ==  
-0   0   0   0   0   0   0   0   0   0  
-0   0   0   0   0   0   0   0   0   0  
-0   0   0   0   0   0   0   0   0   0  
+==  ==  ==  ==  ==  ==  ==  ==  ==  ==
+0   0   0   0   0   0   0   0   0   0
+0   0   0   0   0   0   0   0   0   0
+0   0   0   0   0   0   0   0   0   0
 ==  ==  ==  ==  ==  ==  ==  ==  ==  ==
 ```
 
@@ -288,18 +288,18 @@ The top row corresponds to the left-lane, the middle row corresponds to the lane
 
 The {py:class}`~highway_env.envs.common.observation.LidarObservation` divides the space around the vehicle into angular sectors, and returns an array with one row per angular sector and two columns:
   - distance to the nearest collidable object (vehicles or obstacles)
-  - component of the objects's relative velocity along that direction 
+  - component of the objects's relative velocity along that direction
 
-The angular sector of index 0 corresponds to an angle 0 (east), and then each index/sector increases the angle (south, west, north). 
+The angular sector of index 0 corresponds to an angle 0 (east), and then each index/sector increases the angle (south, west, north).
 
 For example, for a grid of 8 cells, an obstacle 10 meters away in the south and moving towards the north at 1m/s would lead to the following observation:
-    
+
 ```{eval-rst}
-.. table:: the Lidar observation 
+.. table:: the Lidar observation
 
     ===   ===
-    0     0 
-    0     0 
+    0     0
+    0     0
     10    -1
     0     0
     0     0
@@ -325,7 +325,7 @@ Here is an example of what the distance grid may look like in the parking env:
             "vehicles_count": 3,
         })
     env.reset()
-    
+
     plt.imshow(env.render())
     plt.show()
 ```

docs/scripts/move_404.py

@@ -2,6 +2,7 @@
 
 import sys
 
+
 if __name__ == "__main__":
     if len(sys.argv) < 2:
         print("Provide a path")

docs/user_guide.md

@@ -11,4 +11,4 @@ rewards/index
 graphics/index
 multi_agent
 make_your_own
-```
+```

highway_env/__init__.py

@@ -3,7 +3,8 @@
 
 from gymnasium.envs.registration import register
 
-__version__ = "1.10.0"
+
+__version__ = "2.0.0"
 
 try:
     from farama_notifications import notifications

highway_env/envs/__init__.py

@@ -17,6 +17,7 @@
 from highway_env.envs.two_way_env import TwoWayEnv
 from highway_env.envs.u_turn_env import UTurnEnv
 
+
 __all__ = [
     "ExitEnv",
     "HighwayEnv",
@@ -28,6 +29,7 @@
     "MergeEnv",
     "ParkingEnv",
     "ParkingEnvActionRepeat",
+    "ParkingEnvParkedVehicles",
     "RacetrackEnv",
     "RoundaboutEnv",
     "TwoWayEnv",

highway_env/envs/common/abstract.py

@@ -1,6 +1,8 @@
+from __future__ import annotations
+
 import copy
 import os
-from typing import Dict, List, Optional, Text, Tuple, TypeVar
+from typing import TypeVar
 
 import gymnasium as gym
 import numpy as np
@@ -16,11 +18,11 @@
 from highway_env.vehicle.behavior import IDMVehicle
 from highway_env.vehicle.kinematics import Vehicle
 
+
 Observation = TypeVar("Observation")
 
 
 class AbstractEnv(gym.Env):
-
     """
     A generic environment for various tasks involving a vehicle driving on a road.
 
@@ -31,15 +33,15 @@ class AbstractEnv(gym.Env):
 
     observation_type: ObservationType
     action_type: ActionType
-    _record_video_wrapper: Optional[RecordVideo]
+    _record_video_wrapper: RecordVideo | None
     metadata = {
         "render_modes": ["human", "rgb_array"],
     }
 
     PERCEPTION_DISTANCE = 5.0 * Vehicle.MAX_SPEED
     """The maximum distance of any vehicle present in the observation [m]"""
 
-    def __init__(self, config: dict = None, render_mode: Optional[str] = None) -> None:
+    def __init__(self, config: dict = None, render_mode: str | None = None) -> None:
         super().__init__()
 
         # Configuration
@@ -136,7 +138,7 @@ def _reward(self, action: Action) -> float:
         """
         raise NotImplementedError
 
-    def _rewards(self, action: Action) -> Dict[Text, float]:
+    def _rewards(self, action: Action) -> dict[str, float]:
         """
         Returns a multi-objective vector of rewards.
 
@@ -164,7 +166,7 @@ def _is_truncated(self) -> bool:
         """
         raise NotImplementedError
 
-    def _info(self, obs: Observation, action: Optional[Action] = None) -> dict:
+    def _info(self, obs: Observation, action: Action | None = None) -> dict:
         """
         Return a dictionary of additional information
 
@@ -186,9 +188,9 @@ def _info(self, obs: Observation, action: Optional[Action] = None) -> dict:
     def reset(
         self,
         *,
-        seed: Optional[int] = None,
-        options: Optional[dict] = None,
-    ) -> Tuple[Observation, dict]:
+        seed: int | None = None,
+        options: dict | None = None,
+    ) -> tuple[Observation, dict]:
         """
         Reset the environment to it's initial configuration
 
@@ -219,7 +221,7 @@ def _reset(self) -> None:
         """
         raise NotImplementedError()
 
-    def step(self, action: Action) -> Tuple[Observation, float, bool, bool, dict]:
+    def step(self, action: Action) -> tuple[Observation, float, bool, bool, dict]:
         """
         Perform an action and step the environment dynamics.
 
@@ -247,7 +249,7 @@ def step(self, action: Action) -> Tuple[Observation, float, bool, bool, dict]:
 
         return obs, reward, terminated, truncated, info
 
-    def _simulate(self, action: Optional[Action] = None) -> None:
+    def _simulate(self, action: Action | None = None) -> None:
         """Perform several steps of simulation with constant action."""
         frames = int(
             self.config["simulation_frequency"] // self.config["policy_frequency"]
@@ -279,7 +281,7 @@ def _simulate(self, action: Optional[Action] = None) -> None:
 
         self.enable_auto_render = False
 
-    def render(self) -> Optional[np.ndarray]:
+    def render(self) -> np.ndarray | None:
         """
         Render the environment.
 
@@ -317,7 +319,7 @@ def close(self) -> None:
             self.viewer.close()
         self.viewer = None
 
-    def get_available_actions(self) -> List[int]:
+    def get_available_actions(self) -> list[int]:
         return self.action_type.get_available_actions()
 
     def set_record_video_wrapper(self, wrapper: RecordVideo):
@@ -337,7 +339,7 @@ def _automatic_rendering(self) -> None:
             else:
                 self.render()
 
-    def simplify(self) -> "AbstractEnv":
+    def simplify(self) -> AbstractEnv:
         """
         Return a simplified copy of the environment where distant vehicles have been removed from the road.
 
@@ -354,7 +356,7 @@ def simplify(self) -> "AbstractEnv":
 
         return state_copy
 
-    def change_vehicles(self, vehicle_class_path: str) -> "AbstractEnv":
+    def change_vehicles(self, vehicle_class_path: str) -> AbstractEnv:
         """
         Change the type of all vehicles on the road
 
@@ -371,7 +373,7 @@ def change_vehicles(self, vehicle_class_path: str) -> "AbstractEnv":
                 vehicles[i] = vehicle_class.create_from(v)
         return env_copy
 
-    def set_preferred_lane(self, preferred_lane: int = None) -> "AbstractEnv":
+    def set_preferred_lane(self, preferred_lane: int = None) -> AbstractEnv:
         env_copy = copy.deepcopy(self)
         if preferred_lane:
             for v in env_copy.road.vehicles:
@@ -381,30 +383,30 @@ def set_preferred_lane(self, preferred_lane: int = None) -> "AbstractEnv":
                     v.LANE_CHANGE_MAX_BRAKING_IMPOSED = 1000
         return env_copy
 
-    def set_route_at_intersection(self, _to: str) -> "AbstractEnv":
+    def set_route_at_intersection(self, _to: str) -> AbstractEnv:
         env_copy = copy.deepcopy(self)
         for v in env_copy.road.vehicles:
             if isinstance(v, IDMVehicle):
                 v.set_route_at_intersection(_to)
         return env_copy
 
-    def set_vehicle_field(self, args: Tuple[str, object]) -> "AbstractEnv":
+    def set_vehicle_field(self, args: tuple[str, object]) -> AbstractEnv:
         field, value = args
         env_copy = copy.deepcopy(self)
         for v in env_copy.road.vehicles:
             if v is not self.vehicle:
                 setattr(v, field, value)
         return env_copy
 
-    def call_vehicle_method(self, args: Tuple[str, Tuple[object]]) -> "AbstractEnv":
+    def call_vehicle_method(self, args: tuple[str, tuple[object]]) -> AbstractEnv:
         method, method_args = args
         env_copy = copy.deepcopy(self)
         for i, v in enumerate(env_copy.road.vehicles):
             if hasattr(v, method):
                 env_copy.road.vehicles[i] = getattr(v, method)(*method_args)
         return env_copy
 
-    def randomize_behavior(self) -> "AbstractEnv":
+    def randomize_behavior(self) -> AbstractEnv:
         env_copy = copy.deepcopy(self)
         for v in env_copy.road.vehicles:
             if isinstance(v, IDMVehicle):