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Add python 3.13 and update pre-commit (#1390)

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This commit is contained in:
Mark Towers
2025-06-07 17:57:58 +01:00
committed by GitHub
parent 428493e584
commit 433d7af1f9
85 changed files with 289 additions and 288 deletions
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2
.github/workflows/docs-build-dev.yml vendored
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@@ -19,7 +19,7 @@ jobs:
- uses: actions/setup-python@v5
with:
python-version: '3.10'
python-version: '3.12'
- name: Install dependencies
run: pip install -r docs/requirements.txt

2
.github/workflows/docs-build-release.yml vendored
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@@ -19,7 +19,7 @@ jobs:
- uses: actions/setup-python@v5
with:
python-version: '3.10'
python-version: '3.12'
- name: Install dependencies
run: pip install -r docs/requirements.txt

2
.github/workflows/docs-manual-build.yml vendored
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@@ -33,7 +33,7 @@ jobs:
- uses: actions/setup-python@v5
with:
python-version: '3.10'
python-version: '3.12'
- name: Install dependencies
run: pip install -r docs/requirements.txt

2
.github/workflows/run-pre-commit.yml vendored
View File

@@ -15,6 +15,8 @@ jobs:
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python_version: '3.12'
- run: pipx install pre-commit
- run: pre-commit --version
- run: pre-commit run --all-files

2
.github/workflows/run-pytest.yml vendored
View File

@@ -10,7 +10,7 @@ jobs:
strategy:
fail-fast: true
matrix:
python-version: ['3.10', '3.11', '3.12']
python-version: ['3.10', '3.11', '3.12', '3.13']
numpy-version: ['>=1.21,<2.0', '>=2.1']
steps:
- uses: actions/checkout@v4

2
.github/workflows/run-tutorial.yml vendored
View File

@@ -20,7 +20,7 @@ jobs:
strategy:
fail-fast: false # This ensures all matrix combinations run even if one fails
matrix:
python-version: ["3.10"]
python-version: ["3.12"]
tutorial-group:
- gymnasium_basics
- training_agents

12
.pre-commit-config.yaml
View File

@@ -18,13 +18,13 @@ repos:
- id: detect-private-key
- id: debug-statements
- repo: https://github.com/codespell-project/codespell
rev: v2.3.0
rev: v2.4.1
hooks:
- id: codespell
args:
- --ignore-words-list=reacher,ure,referenc,wile,ontact,manuel
- repo: https://github.com/PyCQA/flake8
rev: 7.1.1
rev: 7.2.0
hooks:
- id: flake8
args:
@@ -35,16 +35,16 @@ repos:
- --show-source
- --statistics
- repo: https://github.com/asottile/pyupgrade
rev: v3.19.1
rev: v3.20.0
hooks:
- id: pyupgrade
args: ["--py38-plus"]
args: ["--py310-plus"]
- repo: https://github.com/PyCQA/isort
rev: 5.13.2
rev: 6.0.1
hooks:
- id: isort
- repo: https://github.com/python/black
rev: 24.10.0
rev: 25.1.0
hooks:
- id: black
- repo: https://github.com/pycqa/pydocstyle

2
README.md
View File

@@ -32,7 +32,7 @@ To install the base Gymnasium library, use `pip install gymnasium`
This does not include dependencies for all families of environments (there's a massive number, and some can be problematic to install on certain systems). You can install these dependencies for one family like `pip install "gymnasium[atari]"` or use `pip install "gymnasium[all]"` to install all dependencies.
We support and test for Python 3.10, 3.11 and 3.12 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.
We support and test for Python 3.10, 3.11, 3.12 and 3.13 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.
## API

46
gymnasium/envs/__init__.py
View File

@@ -184,8 +184,18 @@ register(
# Mujoco
# ----------------------------------------
def _raise_mujoco_py_error(*args: Any, **kwargs: Any):
raise ImportError(
"The mujoco v2 and v3 based environments have been moved to the gymnasium-robotics project (https://github.com/Farama-Foundation/gymnasium-robotics)."
)
# manipulation
register(id="Reacher-v2", entry_point=_raise_mujoco_py_error)
register(
id="Reacher-v4",
entry_point="gymnasium.envs.mujoco.reacher_v4:ReacherEnv",
@@ -200,6 +210,8 @@ register(
reward_threshold=-3.75,
)
register(id="Pusher-v2", entry_point=_raise_mujoco_py_error)
register(
id="Pusher-v4",
entry_point="gymnasium.envs.mujoco.pusher_v4:PusherEnv",
@@ -216,6 +228,8 @@ register(
# balance
register(id="InvertedPendulum-v2", entry_point=_raise_mujoco_py_error)
register(
id="InvertedPendulum-v4",
entry_point="gymnasium.envs.mujoco.inverted_pendulum_v4:InvertedPendulumEnv",
@@ -230,6 +244,8 @@ register(
reward_threshold=950.0,
)
register(id="InvertedDoublePendulum-v2", entry_point=_raise_mujoco_py_error)
register(
id="InvertedDoublePendulum-v4",
entry_point="gymnasium.envs.mujoco.inverted_double_pendulum_v4:InvertedDoublePendulumEnv",
@@ -246,6 +262,10 @@ register(
# runners
register(id="HalfCheetah-v2", entry_point=_raise_mujoco_py_error)
register(id="HalfCheetah-v3", entry_point=_raise_mujoco_py_error)
register(
id="HalfCheetah-v4",
entry_point="gymnasium.envs.mujoco.half_cheetah_v4:HalfCheetahEnv",
@@ -260,6 +280,10 @@ register(
reward_threshold=4800.0,
)
register(id="Hopper-v2", entry_point=_raise_mujoco_py_error)
register(id="Hopper-v3", entry_point=_raise_mujoco_py_error)
register(
id="Hopper-v4",
entry_point="gymnasium.envs.mujoco.hopper_v4:HopperEnv",
@@ -274,6 +298,10 @@ register(
reward_threshold=3800.0,
)
register(id="Swimmer-v2", entry_point=_raise_mujoco_py_error)
register(id="Swimmer-v3", entry_point=_raise_mujoco_py_error)
register(
id="Swimmer-v4",
entry_point="gymnasium.envs.mujoco.swimmer_v4:SwimmerEnv",
@@ -288,18 +316,26 @@ register(
reward_threshold=360.0,
)
register(id="Walker2d-v2", entry_point=_raise_mujoco_py_error)
register(id="Walker2d-v3", entry_point=_raise_mujoco_py_error)
register(
id="Walker2d-v4",
max_episode_steps=1000,
entry_point="gymnasium.envs.mujoco.walker2d_v4:Walker2dEnv",
max_episode_steps=1000,
)
register(
id="Walker2d-v5",
max_episode_steps=1000,
entry_point="gymnasium.envs.mujoco.walker2d_v5:Walker2dEnv",
max_episode_steps=1000,
)
register(id="Ant-v2", entry_point=_raise_mujoco_py_error)
register(id="Ant-v3", entry_point=_raise_mujoco_py_error)
register(
id="Ant-v4",
entry_point="gymnasium.envs.mujoco.ant_v4:AntEnv",
@@ -314,6 +350,10 @@ register(
reward_threshold=6000.0,
)
register(id="Humanoid-v2", entry_point=_raise_mujoco_py_error)
register(id="Humanoid-v3", entry_point=_raise_mujoco_py_error)
register(
id="Humanoid-v4",
entry_point="gymnasium.envs.mujoco.humanoid_v4:HumanoidEnv",
@@ -326,6 +366,8 @@ register(
max_episode_steps=1000,
)
register(id="HumanoidStandup-v2", entry_point=_raise_mujoco_py_error)
register(
id="HumanoidStandup-v4",
entry_point="gymnasium.envs.mujoco.humanoidstandup_v4:HumanoidStandupEnv",

18
gymnasium/envs/box2d/bipedal_walker.py
View File

@@ -1,7 +1,7 @@
__credits__ = ["Andrea PIERRÉ"]
import math
from typing import TYPE_CHECKING, List, Optional
from typing import TYPE_CHECKING
import numpy as np
@@ -174,13 +174,13 @@ class BipedalWalker(gym.Env, EzPickle):
"render_fps": FPS,
}
def __init__(self, render_mode: Optional[str] = None, hardcore: bool = False):
def __init__(self, render_mode: str | None = None, hardcore: bool = False):
EzPickle.__init__(self, render_mode, hardcore)
self.isopen = True
self.world = Box2D.b2World()
self.terrain: List[Box2D.b2Body] = []
self.hull: Optional[Box2D.b2Body] = None
self.terrain: list[Box2D.b2Body] = []
self.hull: Box2D.b2Body | None = None
self.prev_shaping = None
@@ -264,7 +264,7 @@ class BipedalWalker(gym.Env, EzPickle):
# state += [l.fraction for l in self.lidar]
self.render_mode = render_mode
self.screen: Optional[pygame.Surface] = None
self.screen: pygame.Surface | None = None
self.clock = None
def _destroy(self):
@@ -432,8 +432,8 @@ class BipedalWalker(gym.Env, EzPickle):
def reset(
self,
*,
seed: Optional[int] = None,
options: Optional[dict] = None,
seed: int | None = None,
options: dict | None = None,
):
super().reset(seed=seed)
self._destroy()
@@ -458,8 +458,8 @@ class BipedalWalker(gym.Env, EzPickle):
(self.np_random.uniform(-INITIAL_RANDOM, INITIAL_RANDOM), 0), True
)
self.legs: List[Box2D.b2Body] = []
self.joints: List[Box2D.b2RevoluteJoint] = []
self.legs: list[Box2D.b2Body] = []
self.joints: list[Box2D.b2RevoluteJoint] = []
for i in [-1, +1]:
leg = self.world.CreateDynamicBody(
position=(init_x, init_y - LEG_H / 2 - LEG_DOWN),

13
gymnasium/envs/box2d/car_racing.py
View File

@@ -1,7 +1,6 @@
__credits__ = ["Andrea PIERRÉ"]
import math
from typing import Optional, Union
import numpy as np
@@ -212,7 +211,7 @@ class CarRacing(gym.Env, EzPickle):
def __init__(
self,
render_mode: Optional[str] = None,
render_mode: str | None = None,
verbose: bool = False,
lap_complete_percent: float = 0.95,
domain_randomize: bool = False,
@@ -233,14 +232,14 @@ class CarRacing(gym.Env, EzPickle):
self.contactListener_keepref = FrictionDetector(self, self.lap_complete_percent)
self.world = Box2D.b2World((0, 0), contactListener=self.contactListener_keepref)
self.screen: Optional[pygame.Surface] = None
self.screen: pygame.Surface | None = None
self.surf = None
self.clock = None
self.isopen = True
self.invisible_state_window = None
self.invisible_video_window = None
self.road = None
self.car: Optional[Car] = None
self.car: Car | None = None
self.reward = 0.0
self.prev_reward = 0.0
self.verbose = verbose
@@ -499,8 +498,8 @@ class CarRacing(gym.Env, EzPickle):
def reset(
self,
*,
seed: Optional[int] = None,
options: Optional[dict] = None,
seed: int | None = None,
options: dict | None = None,
):
super().reset(seed=seed)
self._destroy()
@@ -538,7 +537,7 @@ class CarRacing(gym.Env, EzPickle):
self.render()
return self.step(None)[0], {}
def step(self, action: Union[np.ndarray, int]):
def step(self, action: np.ndarray | int):
assert self.car is not None
if action is not None:
if self.continuous:

10
gymnasium/envs/box2d/lunar_lander.py
View File

@@ -1,7 +1,7 @@
__credits__ = ["Andrea PIERRÉ"]
import math
from typing import TYPE_CHECKING, Optional
from typing import TYPE_CHECKING
import numpy as np
@@ -213,7 +213,7 @@ class LunarLander(gym.Env, EzPickle):
def __init__(
self,
render_mode: Optional[str] = None,
render_mode: str | None = None,
continuous: bool = False,
gravity: float = -10.0,
enable_wind: bool = False,
@@ -254,7 +254,7 @@ class LunarLander(gym.Env, EzPickle):
self.isopen = True
self.world = Box2D.b2World(gravity=(0, gravity))
self.moon = None
self.lander: Optional[Box2D.b2Body] = None
self.lander: Box2D.b2Body | None = None
self.particles = []
self.prev_reward = None
@@ -323,8 +323,8 @@ class LunarLander(gym.Env, EzPickle):
def reset(
self,
*,
seed: Optional[int] = None,
options: Optional[dict] = None,
seed: int | None = None,
options: dict | None = None,
):
super().reset(seed=seed)
self._destroy()

6
gymnasium/envs/classic_control/acrobot.py
View File

@@ -1,7 +1,5 @@
"""classic Acrobot task"""
from typing import Optional
import numpy as np
from numpy import cos, pi, sin
@@ -172,7 +170,7 @@ class AcrobotEnv(Env):
domain_fig = None
actions_num = 3
def __init__(self, render_mode: Optional[str] = None):
def __init__(self, render_mode: str | None = None):
self.render_mode = render_mode
self.screen = None
self.clock = None
@@ -185,7 +183,7 @@ class AcrobotEnv(Env):
self.action_space = spaces.Discrete(3)
self.state = None
def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
def reset(self, *, seed: int | None = None, options: dict | None = None):
super().reset(seed=seed)
# Note that if you use custom reset bounds, it may lead to out-of-bound
# state/observations.

16
gymnasium/envs/classic_control/cartpole.py
View File

@@ -5,7 +5,7 @@ permalink: https://perma.cc/C9ZM-652R
"""
import math
from typing import Optional, Tuple, Union
from typing import Union
import numpy as np
@@ -117,7 +117,7 @@ class CartPoleEnv(gym.Env[np.ndarray, Union[int, np.ndarray]]):
}
def __init__(
self, sutton_barto_reward: bool = False, render_mode: Optional[str] = None
self, sutton_barto_reward: bool = False, render_mode: str | None = None
):
self._sutton_barto_reward = sutton_barto_reward
@@ -228,8 +228,8 @@ class CartPoleEnv(gym.Env[np.ndarray, Union[int, np.ndarray]]):
def reset(
self,
*,
seed: Optional[int] = None,
options: Optional[dict] = None,
seed: int | None = None,
options: dict | None = None,
):
super().reset(seed=seed)
# Note that if you use custom reset bounds, it may lead to out-of-bound
@@ -362,7 +362,7 @@ class CartPoleVectorEnv(VectorEnv):
self,
num_envs: int = 1,
max_episode_steps: int = 500,
render_mode: Optional[str] = None,
render_mode: str | None = None,
sutton_barto_reward: bool = False,
):
self._sutton_barto_reward = sutton_barto_reward
@@ -419,7 +419,7 @@ class CartPoleVectorEnv(VectorEnv):
def step(
self, action: np.ndarray
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, dict]:
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, dict]:
assert self.action_space.contains(
action
), f"{action!r} ({type(action)}) invalid"
@@ -486,8 +486,8 @@ class CartPoleVectorEnv(VectorEnv):
def reset(
self,
*,
seed: Optional[int] = None,
options: Optional[dict] = None,
seed: int | None = None,
options: dict | None = None,
):
super().reset(seed=seed)
# Note that if you use custom reset bounds, it may lead to out-of-bound

5
gymnasium/envs/classic_control/continuous_mountain_car.py
View File

@@ -14,7 +14,6 @@ permalink: https://perma.cc/6Z2N-PFWC
"""
import math
from typing import Optional
import numpy as np
@@ -114,7 +113,7 @@ class Continuous_MountainCarEnv(gym.Env):
"render_fps": 30,
}
def __init__(self, render_mode: Optional[str] = None, goal_velocity=0):
def __init__(self, render_mode: str | None = None, goal_velocity=0):
self.min_action = -1.0
self.max_action = 1.0
self.min_position = -1.2
@@ -183,7 +182,7 @@ class Continuous_MountainCarEnv(gym.Env):
# truncation=False as the time limit is handled by the `TimeLimit` wrapper added during `make`
return self.state, reward, terminated, False, {}
def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
def reset(self, *, seed: int | None = None, options: dict | None = None):
super().reset(seed=seed)
# Note that if you use custom reset bounds, it may lead to out-of-bound
# state/observations.

7
gymnasium/envs/classic_control/mountain_car.py
View File

@@ -4,7 +4,6 @@ permalink: https://perma.cc/6Z2N-PFWC
"""
import math
from typing import Optional
import numpy as np
@@ -106,7 +105,7 @@ class MountainCarEnv(gym.Env):
"render_fps": 30,
}
def __init__(self, render_mode: Optional[str] = None, goal_velocity=0):
def __init__(self, render_mode: str | None = None, goal_velocity=0):
self.min_position = -1.2
self.max_position = 0.6
self.max_speed = 0.07
@@ -157,8 +156,8 @@ class MountainCarEnv(gym.Env):
def reset(
self,
*,
seed: Optional[int] = None,
options: Optional[dict] = None,
seed: int | None = None,
options: dict | None = None,
):
super().reset(seed=seed)
# Note that if you use custom reset bounds, it may lead to out-of-bound

5
gymnasium/envs/classic_control/pendulum.py
View File

@@ -1,7 +1,6 @@
__credits__ = ["Carlos Luis"]
from os import path
from typing import Optional
import numpy as np
@@ -101,7 +100,7 @@ class PendulumEnv(gym.Env):
"render_fps": 30,
}
def __init__(self, render_mode: Optional[str] = None, g=10.0):
def __init__(self, render_mode: str | None = None, g=10.0):
self.max_speed = 8
self.max_torque = 2.0
self.dt = 0.05
@@ -148,7 +147,7 @@ class PendulumEnv(gym.Env):
# truncation=False as the time limit is handled by the `TimeLimit` wrapper added during `make`
return self._get_obs(), -costs, False, False, {}
def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
def reset(self, *, seed: int | None = None, options: dict | None = None):
super().reset(seed=seed)
if options is None:
high = np.array([DEFAULT_X, DEFAULT_Y])

6
gymnasium/envs/classic_control/utils.py
View File

@@ -2,7 +2,7 @@
Utility functions used for classic control environments.
"""
from typing import Optional, SupportsFloat, Tuple
from typing import SupportsFloat
def verify_number_and_cast(x: SupportsFloat) -> float:
@@ -15,8 +15,8 @@ def verify_number_and_cast(x: SupportsFloat) -> float:
def maybe_parse_reset_bounds(
options: Optional[dict], default_low: float, default_high: float
) -> Tuple[float, float]:
options: dict | None, default_low: float, default_high: float
) -> tuple[float, float]:
"""
This function can be called during a reset() to customize the sampling
ranges for setting the initial state distributions.

14
gymnasium/envs/mujoco/ant_v5.py
View File

@@ -1,7 +1,5 @@
__credits__ = ["Kallinteris-Andreas"]
from typing import Dict, Tuple, Union
import numpy as np
from gymnasium import utils
@@ -213,8 +211,8 @@ class AntEnv(MujocoEnv, utils.EzPickle):
- Fixed `info["x_position"]` & `info["y_position"]` & `info["distance_from_origin"]` giving `xpos` instead of `qpos` observations (`xpos` observations are behind 1 `mj_step()` more [here](https://github.com/deepmind/mujoco/issues/889#issuecomment-1568896388)) (related [GitHub issue #1](https://github.com/Farama-Foundation/Gymnasium/issues/521) & [GitHub issue #2](https://github.com/Farama-Foundation/Gymnasium/issues/539)).
- Removed `info["forward_reward"]` as it is equivalent to `info["reward_forward"]`.
* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3, also removed contact forces from the default observation space (new variable `use_contact_forces=True` can restore them).
* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen).
* v2: All continuous control environments now use mujoco-py >= 1.50.
* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen). Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release
"""
@@ -232,15 +230,15 @@ class AntEnv(MujocoEnv, utils.EzPickle):
self,
xml_file: str = "ant.xml",
frame_skip: int = 5,
default_camera_config: Dict[str, Union[float, int]] = DEFAULT_CAMERA_CONFIG,
default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG,
forward_reward_weight: float = 1,
ctrl_cost_weight: float = 0.5,
contact_cost_weight: float = 5e-4,
healthy_reward: float = 1.0,
main_body: Union[int, str] = 1,
main_body: int | str = 1,
terminate_when_unhealthy: bool = True,
healthy_z_range: Tuple[float, float] = (0.2, 1.0),
contact_force_range: Tuple[float, float] = (-1.0, 1.0),
healthy_z_range: tuple[float, float] = (0.2, 1.0),
contact_force_range: tuple[float, float] = (-1.0, 1.0),
reset_noise_scale: float = 0.1,
exclude_current_positions_from_observation: bool = True,
include_cfrc_ext_in_observation: bool = True,

8
gymnasium/envs/mujoco/half_cheetah_v5.py
View File

@@ -1,7 +1,5 @@
__credits__ = ["Kallinteris-Andreas", "Rushiv Arora"]
from typing import Dict, Union
import numpy as np
from gymnasium import utils
@@ -138,8 +136,8 @@ class HalfCheetahEnv(MujocoEnv, utils.EzPickle):
- Restored the `xml_file` argument (was removed in `v4`).
- Renamed `info["reward_run"]` to `info["reward_forward"]` to be consistent with the other environments.
* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3.
* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen).
* v2: All continuous control environments now use mujoco-py >= 1.50.
* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen). Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release.
"""
@@ -157,7 +155,7 @@ class HalfCheetahEnv(MujocoEnv, utils.EzPickle):
self,
xml_file: str = "half_cheetah.xml",
frame_skip: int = 5,
default_camera_config: Dict[str, Union[float, int]] = DEFAULT_CAMERA_CONFIG,
default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG,
forward_reward_weight: float = 1.0,
ctrl_cost_weight: float = 0.1,
reset_noise_scale: float = 0.1,

14
gymnasium/envs/mujoco/hopper_v5.py
View File

@@ -1,7 +1,5 @@
__credits__ = ["Kallinteris-Andreas"]
from typing import Dict, Tuple, Union
import numpy as np
from gymnasium import utils
@@ -148,8 +146,8 @@ class HopperEnv(MujocoEnv, utils.EzPickle):
- Added individual reward terms in `info` (`info["reward_forward"]`, `info["reward_ctrl"]`, `info["reward_survive"]`).
- Added `info["z_distance_from_origin"]` which is equal to the vertical distance of the "torso" body from its initial position.
* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3.
* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco-py >= 1.50.
* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen). Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release.
"""
@@ -167,14 +165,14 @@ class HopperEnv(MujocoEnv, utils.EzPickle):
self,
xml_file: str = "hopper.xml",
frame_skip: int = 4,
default_camera_config: Dict[str, Union[float, int]] = DEFAULT_CAMERA_CONFIG,
default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG,
forward_reward_weight: float = 1.0,
ctrl_cost_weight: float = 1e-3,
healthy_reward: float = 1.0,
terminate_when_unhealthy: bool = True,
healthy_state_range: Tuple[float, float] = (-100.0, 100.0),
healthy_z_range: Tuple[float, float] = (0.7, float("inf")),
healthy_angle_range: Tuple[float, float] = (-0.2, 0.2),
healthy_state_range: tuple[float, float] = (-100.0, 100.0),
healthy_z_range: tuple[float, float] = (0.7, float("inf")),
healthy_angle_range: tuple[float, float] = (-0.2, 0.2),
reset_noise_scale: float = 5e-3,
exclude_current_positions_from_observation: bool = True,
**kwargs,

12
gymnasium/envs/mujoco/humanoid_v5.py
View File

@@ -1,7 +1,5 @@
__credits__ = ["Kallinteris-Andreas"]
from typing import Dict, Tuple, Union
import numpy as np
from gymnasium import utils
@@ -289,9 +287,9 @@ class HumanoidEnv(MujocoEnv, utils.EzPickle):
- Renamed `info["reward_quadctrl"]` to `info["reward_ctrl"]` to be consistent with the other environments.
- Removed `info["forward_reward"]` as it is equivalent to `info["reward_forward"]`.
* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3
* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen). Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
- Note: the environment robot model was slightly changed at `gym==0.21.0` and training results are not comparable with `gym<0.21` and `gym>=0.21` (related [GitHub PR](https://github.com/openai/gym/pull/932/files))
* v2: All continuous control environments now use mujoco-py >= 1.50
* v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
- Note: the environment robot model was slightly changed at `gym==0.21.0` and training results are not comparable with `gym<0.21` and `gym>=0.21` (related [GitHub PR](https://github.com/openai/gym/pull/932/files))
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release
@@ -310,14 +308,14 @@ class HumanoidEnv(MujocoEnv, utils.EzPickle):
self,
xml_file: str = "humanoid.xml",
frame_skip: int = 5,
default_camera_config: Dict[str, Union[float, int]] = DEFAULT_CAMERA_CONFIG,
default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG,
forward_reward_weight: float = 1.25,
ctrl_cost_weight: float = 0.1,
contact_cost_weight: float = 5e-7,
contact_cost_range: Tuple[float, float] = (-np.inf, 10.0),
contact_cost_range: tuple[float, float] = (-np.inf, 10.0),
healthy_reward: float = 5.0,
terminate_when_unhealthy: bool = True,
healthy_z_range: Tuple[float, float] = (1.0, 2.0),
healthy_z_range: tuple[float, float] = (1.0, 2.0),
reset_noise_scale: float = 1e-2,
exclude_current_positions_from_observation: bool = True,
include_cinert_in_observation: bool = True,

10
gymnasium/envs/mujoco/humanoidstandup_v5.py
View File

@@ -1,7 +1,5 @@
__credits__ = ["Kallinteris-Andreas"]
from typing import Dict, Tuple, Union
import numpy as np
from gymnasium import utils
@@ -271,8 +269,8 @@ class HumanoidStandupEnv(MujocoEnv, utils.EzPickle):
- Added `info["x_position"]` & `info["y_position"]` which contain the observations excluded when `exclude_current_positions_from_observation == True`.
- Added `info["z_distance_from_origin"]` which is the vertical distance of the "torso" body from its initial position.
* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3.
* v3: This environment does not have a v3 release.
* v2: All continuous control environments now use mujoco-py >= 1.50.
* v3: This environment does not have a v3 release. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release.
"""
@@ -290,11 +288,11 @@ class HumanoidStandupEnv(MujocoEnv, utils.EzPickle):
self,
xml_file: str = "humanoidstandup.xml",
frame_skip: int = 5,
default_camera_config: Dict[str, Union[float, int]] = DEFAULT_CAMERA_CONFIG,
default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG,
uph_cost_weight: float = 1,
ctrl_cost_weight: float = 0.1,
impact_cost_weight: float = 0.5e-6,
impact_cost_range: Tuple[float, float] = (-np.inf, 10.0),
impact_cost_range: tuple[float, float] = (-np.inf, 10.0),
reset_noise_scale: float = 1e-2,
exclude_current_positions_from_observation: bool = True,
include_cinert_in_observation: bool = True,

8
gymnasium/envs/mujoco/inverted_double_pendulum_v5.py
View File

@@ -1,7 +1,5 @@
__credits__ = ["Kallinteris-Andreas"]
from typing import Dict, Union
import numpy as np
from gymnasium import utils
@@ -130,8 +128,8 @@ class InvertedDoublePendulumEnv(MujocoEnv, utils.EzPickle):
- Added `healthy_reward` argument to configure the reward function (defaults are effectively the same as in `v4`).
- Added individual reward terms in `info` (`info["reward_survive"]`, `info["distance_penalty"]`, `info["velocity_penalty"]`).
* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3.
* v3: This environment does not have a v3 release.
* v2: All continuous control environments now use mujoco-py >= 1.50.
* v3: This environment does not have a v3 release. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v1: max_time_steps raised to 1000 for robot based tasks (including inverted pendulum).
* v0: Initial versions release.
"""
@@ -149,7 +147,7 @@ class InvertedDoublePendulumEnv(MujocoEnv, utils.EzPickle):
self,
xml_file: str = "inverted_double_pendulum.xml",
frame_skip: int = 5,
default_camera_config: Dict[str, Union[float, int]] = {},
default_camera_config: dict[str, float | int] = {},
healthy_reward: float = 10.0,
reset_noise_scale: float = 0.1,
**kwargs,

8
gymnasium/envs/mujoco/inverted_pendulum_v5.py
View File

@@ -1,7 +1,5 @@
__credits__ = ["Kallinteris-Andreas"]
from typing import Dict, Union
import numpy as np
from gymnasium import utils
@@ -105,8 +103,8 @@ class InvertedPendulumEnv(MujocoEnv, utils.EzPickle):
- Added `reset_noise_scale` argument to set the range of initial states.
- Added `info["reward_survive"]` which contains the reward.
* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3.
* v3: This environment does not have a v3 release.
* v2: All continuous control environments now use mujoco-py >= 1.5.
* v3: This environment does not have a v3 release. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v2: All continuous control environments now use mujoco-py >= 1.5. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v1: max_time_steps raised to 1000 for robot based tasks (including inverted pendulum).
* v0: Initial versions release.
"""
@@ -124,7 +122,7 @@ class InvertedPendulumEnv(MujocoEnv, utils.EzPickle):
self,
xml_file: str = "inverted_pendulum.xml",
frame_skip: int = 2,
default_camera_config: Dict[str, Union[float, int]] = DEFAULT_CAMERA_CONFIG,
default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG,
reset_noise_scale: float = 0.01,
**kwargs,
):

23
gymnasium/envs/mujoco/mujoco_env.py
View File

@@ -1,5 +1,4 @@
from os import path
from typing import Dict, Optional, Tuple, Union
import numpy as np
from numpy.typing import NDArray
@@ -41,15 +40,15 @@ class MujocoEnv(gym.Env):
self,
model_path: str,
frame_skip: int,
observation_space: Optional[Space],
render_mode: Optional[str] = None,
observation_space: Space | None,
render_mode: str | None = None,
width: int = DEFAULT_SIZE,
height: int = DEFAULT_SIZE,
camera_id: Optional[int] = None,
camera_name: Optional[str] = None,
default_camera_config: Optional[Dict[str, Union[float, int]]] = None,
camera_id: int | None = None,
camera_name: str | None = None,
default_camera_config: dict[str, float | int] | None = None,
max_geom: int = 1000,
visual_options: Dict[int, bool] = {},
visual_options: dict[int, bool] = {},
):
"""Base abstract class for mujoco based environments.
@@ -116,7 +115,7 @@ class MujocoEnv(gym.Env):
def _initialize_simulation(
self,
) -> Tuple["mujoco.MjModel", "mujoco.MjData"]:
) -> tuple["mujoco.MjModel", "mujoco.MjData"]:
"""
Initialize MuJoCo simulation data structures `mjModel` and `mjData`.
"""
@@ -170,8 +169,8 @@ class MujocoEnv(gym.Env):
def reset(
self,
*,
seed: Optional[int] = None,
options: Optional[dict] = None,
seed: int | None = None,
options: dict | None = None,
):
super().reset(seed=seed)
@@ -210,7 +209,7 @@ class MujocoEnv(gym.Env):
# ----------------------------
def step(
self, action: NDArray[np.float32]
) -> Tuple[NDArray[np.float64], np.float64, bool, bool, Dict[str, np.float64]]:
) -> tuple[NDArray[np.float64], np.float64, bool, bool, dict[str, np.float64]]:
raise NotImplementedError
def reset_model(self) -> NDArray[np.float64]:
@@ -220,7 +219,7 @@ class MujocoEnv(gym.Env):
"""
raise NotImplementedError
def _get_reset_info(self) -> Dict[str, float]:
def _get_reset_info(self) -> dict[str, float]:
"""Function that generates the `info` that is returned during a `reset()`."""
return {}

0
gymnasium/envs/mujoco/mujoco_py_env.py Normal file
View File

35
gymnasium/envs/mujoco/mujoco_rendering.py
View File

@@ -1,6 +1,5 @@
import os
import time
from typing import Dict, Optional
import glfw
import imageio
@@ -49,7 +48,7 @@ class BaseRender:
width: int,
height: int,
max_geom: int = 1000,
visual_options: Dict[int, bool] = {},
visual_options: dict[int, bool] = {},
):
"""Render context superclass for offscreen and window rendering."""
self.model = model
@@ -182,7 +181,7 @@ class OffScreenViewer(BaseRender):
width: int,
height: int,
max_geom: int = 1000,
visual_options: Dict[int, bool] = {},
visual_options: dict[int, bool] = {},
):
# We must make GLContext before MjrContext
self._get_opengl_backend(width, height)
@@ -205,9 +204,7 @@ class OffScreenViewer(BaseRender):
self.opengl_context = _ALL_RENDERERS[self.backend](width, height)
except KeyError as e:
raise RuntimeError(
"Environment variable {} must be one of {!r}: got {!r}.".format(
"MUJOCO_GL", _ALL_RENDERERS.keys(), self.backend
)
f"Environment variable {'MUJOCO_GL'} must be one of {_ALL_RENDERERS.keys()!r}: got {self.backend!r}."
) from e
else:
@@ -238,8 +235,8 @@ class OffScreenViewer(BaseRender):
def render(
self,
render_mode: Optional[str],
camera_id: Optional[int] = None,
render_mode: str | None,
camera_id: int | None = None,
segmentation: bool = False,
):
if camera_id is not None:
@@ -339,10 +336,10 @@ class WindowViewer(BaseRender):
self,
model: "mujoco.MjModel",
data: "mujoco.MjData",
width: Optional[int] = None,
height: Optional[int] = None,
width: int | None = None,
height: int | None = None,
max_geom: int = 1000,
visual_options: Dict[int, bool] = {},
visual_options: dict[int, bool] = {},
):
glfw.init()
@@ -694,13 +691,13 @@ class MujocoRenderer:
self,
model: "mujoco.MjModel",
data: "mujoco.MjData",
default_cam_config: Optional[dict] = None,
width: Optional[int] = None,
height: Optional[int] = None,
default_cam_config: dict | None = None,
width: int | None = None,
height: int | None = None,
max_geom: int = 1000,
camera_id: Optional[int] = None,
camera_name: Optional[str] = None,
visual_options: Dict[int, bool] = {},
camera_id: int | None = None,
camera_name: str | None = None,
visual_options: dict[int, bool] = {},
):
"""A wrapper for clipping continuous actions within the valid bound.
@@ -746,7 +743,7 @@ class MujocoRenderer:
def render(
self,
render_mode: Optional[str],
render_mode: str | None,
):
"""Renders a frame of the simulation in a specific format and camera view.
@@ -768,7 +765,7 @@ class MujocoRenderer:
elif render_mode == "human":
return viewer.render()
def _get_viewer(self, render_mode: Optional[str]):
def _get_viewer(self, render_mode: str | None):
"""Initializes and returns a viewer class depending on the render_mode
- `WindowViewer` class for "human" render mode
- `OffScreenViewer` class for "rgb_array", "depth_array", or "rgbd_tuple" render mode

8
gymnasium/envs/mujoco/pusher_v5.py
View File

@@ -1,7 +1,5 @@
__credits__ = ["Kallinteris-Andreas"]
from typing import Dict, Union
import numpy as np
from gymnasium import utils
@@ -153,8 +151,8 @@ class PusherEnv(MujocoEnv, utils.EzPickle):
- Added `info["reward_near"]` which is equal to the reward term `reward_near`.
* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3.
- Warning: This version of the environment is not compatible with `mujoco>=3.0.0` (related [GitHub issue](https://github.com/Farama-Foundation/Gymnasium/issues/950)).
* v3: This environment does not have a v3 release.
* v2: All continuous control environments now use mujoco-py >= 1.50.
* v3: This environment does not have a v3 release. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v1: max_time_steps raised to 1000 for robot based tasks (not including pusher, which has a max_time_steps of 100). Added reward_threshold to environments.
* v0: Initial versions release.
"""
@@ -172,7 +170,7 @@ class PusherEnv(MujocoEnv, utils.EzPickle):
self,
xml_file: str = "pusher_v5.xml",
frame_skip: int = 5,
default_camera_config: Dict[str, Union[float, int]] = DEFAULT_CAMERA_CONFIG,
default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG,
reward_near_weight: float = 0.5,
reward_dist_weight: float = 1,
reward_control_weight: float = 0.1,

8
gymnasium/envs/mujoco/reacher_v5.py
View File

@@ -1,7 +1,5 @@
__credits__ = ["Kallinteris-Andreas"]
from typing import Dict, Union
import numpy as np
from gymnasium import utils
@@ -130,8 +128,8 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
- Added `reward_dist_weight`, `reward_control_weight` arguments to configure the reward function (defaults are effectively the same as in `v4`).
- Fixed `info["reward_ctrl"]` not being multiplied by the reward weight.
* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3
* v3: This environment does not have a v3 release.
* v2: All continuous control environments now use mujoco-py >= 1.50
* v3: This environment does not have a v3 release. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v1: max_time_steps raised to 1000 for robot based tasks (not including reacher, which has a max_time_steps of 50). Added reward_threshold to environments.
* v0: Initial versions release
"""
@@ -149,7 +147,7 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
self,
xml_file: str = "reacher.xml",
frame_skip: int = 2,
default_camera_config: Dict[str, Union[float, int]] = DEFAULT_CAMERA_CONFIG,
default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG,
reward_dist_weight: float = 1,
reward_control_weight: float = 1,
**kwargs,

8
gymnasium/envs/mujoco/swimmer_v5.py
View File

@@ -1,7 +1,5 @@
__credits__ = ["Kallinteris-Andreas", "Rushiv Arora"]
from typing import Dict, Union
import numpy as np
from gymnasium import utils
@@ -137,8 +135,8 @@ class SwimmerEnv(MujocoEnv, utils.EzPickle):
- Added `exclude_current_positions_from_observation` argument.
- Replaced `info["reward_fwd"]` and `info["forward_reward"]` with `info["reward_forward"]` to be consistent with the other environments.
* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3.
* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen).
* v2: All continuous control environments now use mujoco-py >= 1.50.
* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen). Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release.
"""
@@ -156,7 +154,7 @@ class SwimmerEnv(MujocoEnv, utils.EzPickle):
self,
xml_file: str = "swimmer.xml",
frame_skip: int = 4,
default_camera_config: Dict[str, Union[float, int]] = {},
default_camera_config: dict[str, float | int] = {},
forward_reward_weight: float = 1.0,
ctrl_cost_weight: float = 1e-4,
reset_noise_scale: float = 0.1,

12
gymnasium/envs/mujoco/walker2d_v5.py
View File

@@ -1,7 +1,5 @@
__credits__ = ["Kallinteris-Andreas"]
from typing import Dict, Tuple, Union
import numpy as np
from gymnasium import utils
@@ -157,8 +155,8 @@ class Walker2dEnv(MujocoEnv, utils.EzPickle):
- Added individual reward terms in `info` (`info["reward_forward"]`, `info["reward_ctrl"]`, `info["reward_survive"]`).
- Added `info["z_distance_from_origin"]` which is equal to the vertical distance of the "torso" body from its initial position.
* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3
* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco-py >= 1.50
* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen). Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release
"""
@@ -176,13 +174,13 @@ class Walker2dEnv(MujocoEnv, utils.EzPickle):
self,
xml_file: str = "walker2d_v5.xml",
frame_skip: int = 4,
default_camera_config: Dict[str, Union[float, int]] = DEFAULT_CAMERA_CONFIG,
default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG,
forward_reward_weight: float = 1.0,
ctrl_cost_weight: float = 1e-3,
healthy_reward: float = 1.0,
terminate_when_unhealthy: bool = True,
healthy_z_range: Tuple[float, float] = (0.8, 2.0),
healthy_angle_range: Tuple[float, float] = (-1.0, 1.0),
healthy_z_range: tuple[float, float] = (0.8, 2.0),
healthy_angle_range: tuple[float, float] = (-1.0, 1.0),
reset_noise_scale: float = 5e-3,
exclude_current_positions_from_observation: bool = True,
**kwargs,

4
gymnasium/envs/phys2d/cartpole.py
View File

@@ -2,7 +2,7 @@
from __future__ import annotations
from typing import Any, Tuple, TypeAlias
from typing import Any, TypeAlias
import jax
import jax.numpy as jnp
@@ -19,7 +19,7 @@ from gymnasium.vector import AutoresetMode
PRNGKeyType: TypeAlias = jax.Array
StateType: TypeAlias = jax.Array
RenderStateType = Tuple["pygame.Surface", "pygame.time.Clock"] # type: ignore # noqa: F821
RenderStateType = tuple["pygame.Surface", "pygame.time.Clock"] # type: ignore # noqa: F821
@struct.dataclass

4
gymnasium/envs/phys2d/pendulum.py
View File

@@ -3,7 +3,7 @@
from __future__ import annotations
from os import path
from typing import Any, Optional, Tuple, TypeAlias
from typing import Any, Optional, TypeAlias
import jax
import jax.numpy as jnp
@@ -20,7 +20,7 @@ from gymnasium.vector import AutoresetMode
PRNGKeyType: TypeAlias = jax.Array
StateType: TypeAlias = jax.Array
RenderStateType = Tuple["pygame.Surface", "pygame.time.Clock", Optional[float]] # type: ignore # noqa: F821
RenderStateType = tuple["pygame.Surface", "pygame.time.Clock", Optional[float]] # type: ignore # noqa: F821
@struct.dataclass

14
gymnasium/envs/registration.py
View File

@@ -7,15 +7,16 @@ import copy
import dataclasses
import difflib
import importlib
import importlib.metadata as metadata
import importlib.util
import json
import re
import sys
from collections import defaultdict
from collections.abc import Callable, Iterable, Sequence
from dataclasses import dataclass, field
from enum import Enum
from types import ModuleType
from typing import Any, Callable, Iterable, Sequence
from typing import Any, Protocol
import gymnasium as gym
from gymnasium import Env, Wrapper, error, logger
@@ -23,14 +24,6 @@ from gymnasium.logger import warn
from gymnasium.vector import AutoresetMode
if sys.version_info < (3, 10):
import importlib_metadata as metadata # type: ignore
else:
import importlib.metadata as metadata
from typing import Protocol
ENV_ID_RE = re.compile(
r"^(?:(?P<namespace>[\w:-]+)\/)?(?:(?P<name>[\w:.-]+?))(?:-v(?P<version>\d+))?$"
)
@@ -606,7 +599,6 @@ def register(
assert (
entry_point is not None or vector_entry_point is not None
), "Either `entry_point` or `vector_entry_point` (or both) must be provided"
global registry, current_namespace
ns, name, version = parse_env_id(id)
if kwargs is None:

10
gymnasium/envs/tabular/blackjack.py
View File

@@ -2,7 +2,7 @@
import math
import os
from typing import NamedTuple, Optional, Tuple, TypeAlias, Union
from typing import NamedTuple, TypeAlias
import jax
import jax.numpy as jnp
@@ -20,7 +20,7 @@ from gymnasium.wrappers import HumanRendering
PRNGKeyType: TypeAlias = jax.Array
RenderStateType = Tuple["pygame.Surface", str, int] # type: ignore # noqa: F821
RenderStateType = tuple["pygame.Surface", str, int] # type: ignore # noqa: F821
deck = jnp.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10])
@@ -246,7 +246,7 @@ class BlackjackFunctional(
def transition(
self,
state: EnvState,
action: Union[int, jax.Array],
action: int | jax.Array,
key: PRNGKeyType,
params: BlackJackParams = BlackJackParams,
) -> EnvState:
@@ -381,7 +381,7 @@ class BlackjackFunctional(
state: StateType,
render_state: RenderStateType,
params: BlackJackParams = BlackJackParams,
) -> Tuple[RenderStateType, np.ndarray]:
) -> tuple[RenderStateType, np.ndarray]:
"""Renders an image from a state."""
try:
import pygame
@@ -510,7 +510,7 @@ class BlackJackJaxEnv(FunctionalJaxEnv, EzPickle):
metadata = {"render_modes": ["rgb_array"], "render_fps": 50, "jax": True}
def __init__(self, render_mode: Optional[str] = None, **kwargs):
def __init__(self, render_mode: str | None = None, **kwargs):
"""Initializes Gym wrapper for blackjack functional env."""
EzPickle.__init__(self, render_mode=render_mode, **kwargs)
env = BlackjackFunctional(**kwargs)

7
gymnasium/envs/toy_text/blackjack.py
View File

@@ -1,5 +1,4 @@
import os
from typing import Optional
import numpy as np
@@ -149,7 +148,7 @@ class BlackjackEnv(gym.Env):
"render_fps": 4,
}
def __init__(self, render_mode: Optional[str] = None, natural=False, sab=False):
def __init__(self, render_mode: str | None = None, natural=False, sab=False):
self.action_space = spaces.Discrete(2)
self.observation_space = spaces.Tuple(
(spaces.Discrete(32), spaces.Discrete(11), spaces.Discrete(2))
@@ -201,8 +200,8 @@ class BlackjackEnv(gym.Env):
def reset(
self,
seed: Optional[int] = None,
options: Optional[dict] = None,
seed: int | None = None,
options: dict | None = None,
):
super().reset(seed=seed)
self.dealer = draw_hand(self.np_random)

10
gymnasium/envs/toy_text/cliffwalking.py
View File

@@ -1,7 +1,7 @@
from contextlib import closing
from io import StringIO
from os import path
from typing import Any, List, Optional, Tuple, Union
from typing import Any
import numpy as np
@@ -101,7 +101,7 @@ class CliffWalkingEnv(Env):
"render_fps": 4,
}
def __init__(self, render_mode: Optional[str] = None, is_slippery: bool = False):
def __init__(self, render_mode: str | None = None, is_slippery: bool = False):
self.shape = (4, 12)
self.start_state_index = np.ravel_multi_index((3, 0), self.shape)
@@ -159,8 +159,8 @@ class CliffWalkingEnv(Env):
return coord
def _calculate_transition_prob(
self, current: Union[List[int], np.ndarray], move: int
) -> List[Tuple[float, Any, int, bool]]:
self, current: list[int] | np.ndarray, move: int
) -> list[tuple[float, Any, int, bool]]:
"""Determine the outcome for an action. Transition Prob is always 1.0.
Args:
@@ -203,7 +203,7 @@ class CliffWalkingEnv(Env):
# truncation=False as the time limit is handled by the `TimeLimit` wrapper added during `make`
return int(s), r, t, False, {"prob": p}
def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
def reset(self, *, seed: int | None = None, options: dict | None = None):
super().reset(seed=seed)
self.s = categorical_sample(self.initial_state_distrib, self.np_random)
self.lastaction = None

13
gymnasium/envs/toy_text/frozen_lake.py
View File

@@ -1,7 +1,6 @@
from contextlib import closing
from io import StringIO
from os import path
from typing import List, Optional
import numpy as np
@@ -33,7 +32,7 @@ MAPS = {
# DFS to check that it's a valid path.
def is_valid(board: List[List[str]], max_size: int) -> bool:
def is_valid(board: list[list[str]], max_size: int) -> bool:
frontier, discovered = [], set()
frontier.append((0, 0))
while frontier:
@@ -54,8 +53,8 @@ def is_valid(board: List[List[str]], max_size: int) -> bool:
def generate_random_map(
size: int = 8, p: float = 0.8, seed: Optional[int] = None
) -> List[str]:
size: int = 8, p: float = 0.8, seed: int | None = None
) -> list[str]:
"""Generates a random valid map (one that has a path from start to goal)
Args:
@@ -222,7 +221,7 @@ class FrozenLakeEnv(Env):
def __init__(
self,
render_mode: Optional[str] = None,
render_mode: str | None = None,
desc=None,
map_name="4x4",
is_slippery=True,
@@ -317,8 +316,8 @@ class FrozenLakeEnv(Env):
def reset(
self,
*,
seed: Optional[int] = None,
options: Optional[dict] = None,
seed: int | None = None,
options: dict | None = None,
):
super().reset(seed=seed)
self.s = categorical_sample(self.initial_state_distrib, self.np_random)

7
gymnasium/envs/toy_text/taxi.py
View File

@@ -1,7 +1,6 @@
from contextlib import closing
from io import StringIO
from os import path
from typing import Optional
import numpy as np
@@ -279,7 +278,7 @@ class TaxiEnv(Env):
def __init__(
self,
render_mode: Optional[str] = None,
render_mode: str | None = None,
is_rainy: bool = False,
fickle_passenger: bool = False,
):
@@ -425,8 +424,8 @@ class TaxiEnv(Env):
def reset(
self,
*,
seed: Optional[int] = None,
options: Optional[dict] = None,
seed: int | None = None,
options: dict | None = None,
):
super().reset(seed=seed)
self.s = categorical_sample(self.initial_state_distrib, self.np_random)

3
gymnasium/experimental/functional.py
View File

@@ -2,7 +2,8 @@
from __future__ import annotations
from typing import Any, Callable, Generic, TypeVar
from collections.abc import Callable
from typing import Any, Generic, TypeVar
import numpy as np

3
gymnasium/logger.py
View File

@@ -1,7 +1,6 @@
"""Set of functions for logging messages."""
import warnings
from typing import Optional, Type
from gymnasium.utils import colorize
@@ -19,7 +18,7 @@ warnings.filterwarnings("once", "", DeprecationWarning, module=r"^gymnasium\.")
def warn(
msg: str,
*args: object,
category: Optional[Type[Warning]] = None,
category: type[Warning] | None = None,
stacklevel: int = 1,
):
"""Raises a warning to the user if the min_level <= WARN.

3
gymnasium/spaces/box.py
View File

@@ -2,7 +2,8 @@
from __future__ import annotations
from typing import Any, Iterable, Mapping, Sequence, SupportsFloat
from collections.abc import Iterable, Mapping, Sequence
from typing import Any, SupportsFloat
import numpy as np
from numpy.typing import NDArray

5
gymnasium/spaces/dict.py
View File

@@ -5,14 +5,15 @@ from __future__ import annotations
import collections.abc
import typing
from collections import OrderedDict
from typing import Any, KeysView, Sequence
from collections.abc import KeysView, Sequence
from typing import Any
import numpy as np
from gymnasium.spaces.space import Space
class Dict(Space[typing.Dict[str, Any]], typing.Mapping[str, Space[Any]]):
class Dict(Space[dict[str, Any]], typing.Mapping[str, Space[Any]]):
"""A dictionary of :class:`Space` instances.
Elements of this space are (ordered) dictionaries of elements from the constituent spaces.

3
gymnasium/spaces/discrete.py
View File

@@ -2,7 +2,8 @@
from __future__ import annotations
from typing import Any, Iterable, Mapping, Sequence
from collections.abc import Iterable, Mapping, Sequence
from typing import Any
import numpy as np

3
gymnasium/spaces/graph.py
View File

@@ -2,7 +2,8 @@
from __future__ import annotations
from typing import Any, NamedTuple, Sequence
from collections.abc import Sequence
from typing import Any, NamedTuple
import numpy as np
from numpy.typing import NDArray

3
gymnasium/spaces/multi_binary.py
View File

@@ -2,7 +2,8 @@
from __future__ import annotations
from typing import Any, Sequence
from collections.abc import Sequence
from typing import Any
import numpy as np
from numpy.typing import NDArray

3
gymnasium/spaces/multi_discrete.py
View File

@@ -2,7 +2,8 @@
from __future__ import annotations
from typing import Any, Iterable, Mapping, Sequence
from collections.abc import Iterable, Mapping, Sequence
from typing import Any
import numpy as np
from numpy.typing import NDArray

3
gymnasium/spaces/oneof.py
View File

@@ -3,7 +3,8 @@
from __future__ import annotations
import typing
from typing import Any, Iterable
from collections.abc import Iterable
from typing import Any
import numpy as np

2
gymnasium/spaces/sequence.py
View File

@@ -12,7 +12,7 @@ import gymnasium as gym
from gymnasium.spaces.space import Space
class Sequence(Space[Union[typing.Tuple[Any, ...], Any]]):
class Sequence(Space[Union[tuple[Any, ...], Any]]):
r"""This space represent sets of finite-length sequences.
This space represents the set of tuples of the form :math:`(a_0, \dots, a_n)` where the :math:`a_i` belong

3
gymnasium/spaces/space.py
View File

@@ -2,7 +2,8 @@
from __future__ import annotations
from typing import Any, Generic, Iterable, Mapping, Sequence, TypeAlias, TypeVar
from collections.abc import Iterable, Mapping, Sequence
from typing import Any, Generic, TypeAlias, TypeVar
import numpy as np
import numpy.typing as npt

5
gymnasium/spaces/tuple.py
View File

@@ -3,14 +3,15 @@
from __future__ import annotations
import typing
from typing import Any, Iterable
from collections.abc import Iterable
from typing import Any
import numpy as np
from gymnasium.spaces.space import Space
class Tuple(Space[typing.Tuple[Any, ...]], typing.Sequence[Any]):
class Tuple(Space[tuple[Any, ...]], typing.Sequence[Any]):
"""A tuple (more precisely: the cartesian product) of :class:`Space` instances.
Elements of this space are tuples of elements of the constituent spaces.

5
gymnasium/spaces/utils.py
View File

@@ -7,7 +7,6 @@ These functions mostly take care of flattening and unflattening elements of spac
from __future__ import annotations
import operator as op
import typing
from functools import reduce, singledispatch
from typing import Any, TypeVar, Union
@@ -111,9 +110,7 @@ def _flatdim_oneof(space: OneOf) -> int:
T = TypeVar("T")
FlatType = Union[
NDArray[Any], typing.Dict[str, Any], typing.Tuple[Any, ...], GraphInstance
]
FlatType = Union[NDArray[Any], dict[str, Any], tuple[Any, ...], GraphInstance]
@singledispatch

2
gymnasium/utils/passive_env_checker.py
View File

@@ -1,8 +1,8 @@
"""A set of functions for passively checking environment implementations."""
import inspect
from collections.abc import Callable
from functools import partial
from typing import Callable
import numpy as np

2
gymnasium/utils/performance.py
View File

@@ -1,7 +1,7 @@
"""A collection of runtime performance bencharks, useful for debugging performance related issues."""
import time
from typing import Callable
from collections.abc import Callable
import gymnasium

7
gymnasium/utils/play.py
View File

@@ -3,7 +3,8 @@
from __future__ import annotations
from collections import deque
from typing import TYPE_CHECKING, Callable, List
from collections.abc import Callable
from typing import TYPE_CHECKING
import numpy as np
@@ -89,7 +90,7 @@ class PlayableGame:
def _get_video_size(self, zoom: float | None = None) -> tuple[int, int]:
rendered = self.env.render()
if isinstance(rendered, List):
if isinstance(rendered, list):
rendered = rendered[-1]
assert rendered is not None and isinstance(rendered, np.ndarray)
video_size = (rendered.shape[1], rendered.shape[0])
@@ -299,7 +300,7 @@ def play(
callback(prev_obs, obs, action, rew, terminated, truncated, info)
if obs is not None:
rendered = env.render()
if isinstance(rendered, List):
if isinstance(rendered, list):
rendered = rendered[-1]
assert rendered is not None and isinstance(rendered, np.ndarray)
display_arr(

2
gymnasium/utils/save_video.py
View File

@@ -3,7 +3,7 @@
from __future__ import annotations
import os
from typing import Callable
from collections.abc import Callable
import gymnasium as gym
from gymnasium import logger

6
gymnasium/utils/step_api_compatibility.py
View File

@@ -2,21 +2,21 @@
from __future__ import annotations
from typing import SupportsFloat, Tuple, Union
from typing import SupportsFloat, Union
import numpy as np
from gymnasium.core import ObsType
DoneStepType = Tuple[
DoneStepType = tuple[
Union[ObsType, np.ndarray],
Union[SupportsFloat, np.ndarray],
Union[bool, np.ndarray],
Union[dict, list],
]
TerminatedTruncatedStepType = Tuple[
TerminatedTruncatedStepType = tuple[
Union[ObsType, np.ndarray],
Union[SupportsFloat, np.ndarray],
Union[bool, np.ndarray],

3
gymnasium/vector/async_vector_env.py
View File

@@ -6,12 +6,13 @@ import multiprocessing
import sys
import time
import traceback
from collections.abc import Callable, Sequence
from copy import deepcopy
from enum import Enum
from multiprocessing import Queue
from multiprocessing.connection import Connection
from multiprocessing.sharedctypes import SynchronizedArray
from typing import Any, Callable, Sequence
from typing import Any
import numpy as np

3
gymnasium/vector/sync_vector_env.py
View File

@@ -2,8 +2,9 @@
from __future__ import annotations
from collections.abc import Callable, Iterator, Sequence
from copy import deepcopy
from typing import Any, Callable, Iterator, Sequence
from typing import Any
import numpy as np

4
gymnasium/vector/utils/space_utils.py
View File

@@ -10,10 +10,10 @@
from __future__ import annotations
import typing
from collections.abc import Callable
from collections.abc import Callable, Iterable, Iterator
from copy import deepcopy
from functools import singledispatch
from typing import Any, Iterable, Iterator
from typing import Any
import numpy as np

3
gymnasium/wrappers/array_conversion.py
View File

@@ -20,8 +20,9 @@ import functools
import importlib
import numbers
from collections import abc
from collections.abc import Iterable, Mapping
from types import ModuleType, NoneType
from typing import Any, Iterable, Mapping, SupportsFloat
from typing import Any, SupportsFloat
import numpy as np
from packaging.version import Version

7
gymnasium/wrappers/rendering.py
View File

@@ -11,8 +11,9 @@ from __future__ import annotations
import gc
import os
from collections.abc import Callable
from copy import deepcopy
from typing import Any, Callable, Generic, List, SupportsFloat
from typing import Any, Generic, SupportsFloat
import numpy as np
@@ -322,7 +323,7 @@ class RecordVideo(
assert self.recording, "Cannot capture a frame, recording wasn't started."
frame = self.env.render()
if isinstance(frame, List):
if isinstance(frame, list):
if len(frame) == 0: # render was called
return
self.render_history += frame
@@ -375,7 +376,7 @@ class RecordVideo(
def render(self) -> RenderFrame | list[RenderFrame]:
"""Compute the render frames as specified by render_mode attribute during initialization of the environment."""
render_out = super().render()
if self.recording and isinstance(render_out, List):
if self.recording and isinstance(render_out, list):
self.recorded_frames += render_out
if len(self.render_history) > 0:

2
gymnasium/wrappers/transform_action.py
View File

@@ -7,7 +7,7 @@
from __future__ import annotations
from typing import Callable
from collections.abc import Callable
import numpy as np

3
gymnasium/wrappers/transform_observation.py
View File

@@ -13,7 +13,8 @@
from __future__ import annotations
from typing import Any, Callable, Final, Sequence
from collections.abc import Callable, Sequence
from typing import Any, Final
import numpy as np

3
gymnasium/wrappers/transform_reward.py
View File

@@ -6,7 +6,8 @@
from __future__ import annotations
from typing import Callable, SupportsFloat
from collections.abc import Callable
from typing import SupportsFloat
import numpy as np

2
gymnasium/wrappers/utils.py
View File

@@ -2,8 +2,8 @@
from __future__ import annotations
from collections.abc import Callable
from functools import singledispatch
from typing import Callable
import numpy as np

3
gymnasium/wrappers/vector/vectorize_action.py
View File

@@ -2,8 +2,9 @@
from __future__ import annotations
from collections.abc import Callable
from copy import deepcopy
from typing import Any, Callable
from typing import Any
import numpy as np

3
gymnasium/wrappers/vector/vectorize_observation.py
View File

@@ -2,8 +2,9 @@
from __future__ import annotations
from collections.abc import Callable, Sequence
from copy import deepcopy
from typing import Any, Callable, Sequence
from typing import Any
import numpy as np

3
gymnasium/wrappers/vector/vectorize_reward.py
View File

@@ -2,7 +2,8 @@
from __future__ import annotations
from typing import Any, Callable
from collections.abc import Callable
from typing import Any
import numpy as np

4
pyproject.toml
View File

@@ -19,6 +19,7 @@ classifiers = [
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
'Intended Audience :: Science/Research',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
]
@@ -35,7 +36,7 @@ dynamic = ["version"]
atari = ["ale_py >=0.9"]
box2d = ["box2d-py ==2.3.5", "pygame >=2.1.3", "swig ==4.*"]
classic-control = ["pygame >=2.1.3"]
classic_control = ["pygame >=2.1.3"] # kept for backward compatibility
classic_control = ["pygame >=2.1.3"] # kept for backward compatibility
mujoco = ["mujoco >=2.1.5", "imageio >=2.14.1", "packaging >=23.0"]
toy-text = ["pygame >=2.1.3"]
toy_text = ["pygame >=2.1.3"] # kept for backward compatibility
@@ -163,6 +164,5 @@ reportOperatorIssue = "none" # TODO fix one by one
reportOptionalMemberAccess = "none" # TODO fix one by one
reportAssignmentType = "none" # TODO fix one by one
[tool.pytest.ini_options]
filterwarnings = ["ignore::DeprecationWarning:gymnasium.*:"]

4
tests/envs/functional/test_core.py
View File

@@ -1,4 +1,4 @@
from typing import Any, Dict, Optional
from typing import Any
import numpy as np
@@ -6,7 +6,7 @@ from gymnasium.experimental.functional import FuncEnv
class BasicTestEnv(FuncEnv):
def __init__(self, options: Optional[Dict[str, Any]] = None):
def __init__(self, options: dict[str, Any] | None = None):
super().__init__(options)
def initial(self, rng: Any) -> np.ndarray:

5
tests/envs/registration/test_register.py
View File

@@ -1,7 +1,6 @@
"""Tests that `gymnasium.register` works as expected."""
import re
from typing import Optional
import pytest
@@ -61,9 +60,7 @@ def register_registration_testing_envs():
("MyAwesomeEnv-v", None, "MyAwesomeEnv-v", None),
],
)
def test_register(
env_id: str, namespace: Optional[str], name: str, version: Optional[int]
):
def test_register(env_id: str, namespace: str | None, name: str, version: int | None):
gym.register(env_id, "no-entry-point")
assert gym.spec(env_id).id == env_id

6
tests/envs/test_env_implementation.py
View File

@@ -1,5 +1,3 @@
from typing import Optional
import numpy as np
import pytest
@@ -293,7 +291,7 @@ def test_taxi_fickle_passenger():
@pytest.mark.parametrize(
"low_high", [None, (-0.4, 0.4), (np.array(-0.4), np.array(0.4))]
)
def test_customizable_resets(env_name: str, low_high: Optional[list]):
def test_customizable_resets(env_name: str, low_high: list | None):
env = gym.make(env_name)
env.action_space.seed(0)
# First ensure we can do a reset.
@@ -316,7 +314,7 @@ def test_customizable_resets(env_name: str, low_high: Optional[list]):
(np.array(1.2), np.array(1.0)),
],
)
def test_customizable_pendulum_resets(low_high: Optional[list]):
def test_customizable_pendulum_resets(low_high: list | None):
env = gym.make("Pendulum-v1")
env.action_space.seed(0)
# First ensure we can do a reset and the values are within expected ranges.

14
tests/envs/utils.py
View File

@@ -1,13 +1,11 @@
"""Finds all the specs that we can test with"""
from typing import List, Optional
import gymnasium as gym
from gymnasium import logger
from gymnasium.envs.registration import EnvSpec
def try_make_env(env_spec: EnvSpec) -> Optional[gym.Env]:
def try_make_env(env_spec: EnvSpec) -> gym.Env | None:
"""Tries to make the environment showing if it is possible.
Warning the environments have no wrappers, including time limit and order enforcing.
@@ -30,23 +28,23 @@ def try_make_env(env_spec: EnvSpec) -> Optional[gym.Env]:
# Tries to make all environment to test with
all_testing_initialised_envs: List[Optional[gym.Env]] = [
all_testing_initialised_envs: list[gym.Env | None] = [
try_make_env(env_spec) for env_spec in gym.envs.registry.values()
]
all_testing_initialised_envs: List[gym.Env] = [
all_testing_initialised_envs: list[gym.Env] = [
env for env in all_testing_initialised_envs if env is not None
]
# All testing, mujoco and gymnasium environment specs
all_testing_env_specs: List[EnvSpec] = [
all_testing_env_specs: list[EnvSpec] = [
env.spec for env in all_testing_initialised_envs
]
mujoco_testing_env_specs: List[EnvSpec] = [
mujoco_testing_env_specs: list[EnvSpec] = [
env_spec
for env_spec in all_testing_env_specs
if "gymnasium.envs.mujoco" in env_spec.entry_point
]
gym_testing_env_specs: List[EnvSpec] = [
gym_testing_env_specs: list[EnvSpec] = [
env_spec
for env_spec in all_testing_env_specs
if any(

6
tests/spaces/test_spaces.py
View File

@@ -3,7 +3,7 @@ import itertools
import json # note: ujson fails this test due to float equality
import pickle
import tempfile
from typing import Callable, List, Union
from collections.abc import Callable
import numpy as np
import pytest
@@ -400,9 +400,7 @@ def test_space_sample_mask(space: Space, mask, n_trials: int = 100):
assert np.all(variance < scipy.stats.chi2.isf(ALPHA, df=1))
elif isinstance(space, MultiDiscrete):
# Due to the multi-axis capability of MultiDiscrete, these functions need to be recursive and that the expected / observed numpy are of non-regular shapes
def _generate_frequency(
_dim: Union[np.ndarray, int], _mask, func: Callable
) -> List:
def _generate_frequency(_dim: np.ndarray | int, _mask, func: Callable) -> list:
if isinstance(_dim, np.ndarray):
return [
_generate_frequency(sub_dim, sub_mask, func)

3
tests/spaces/test_utils.py
View File

@@ -1,5 +1,4 @@
from itertools import zip_longest
from typing import Optional
import numpy as np
import pytest
@@ -77,7 +76,7 @@ assert len(TESTING_SPACES) == len(TESTING_SPACES_EXPECTED_FLATDIMS)
zip_longest(TESTING_SPACES, TESTING_SPACES_EXPECTED_FLATDIMS),
ids=TESTING_SPACES_IDS,
)
def test_flatdim(space: gym.spaces.Space, flatdim: Optional[int]):
def test_flatdim(space: gym.spaces.Space, flatdim: int | None):
"""Checks that the flattened dims of the space is equal to an expected value."""
if space.is_np_flattenable:
dim = utils.flatdim(space)

4
tests/spaces/utils.py
View File

@@ -1,5 +1,3 @@
from typing import List
import numpy as np
from gymnasium.spaces import (
@@ -116,7 +114,7 @@ TESTING_COMPOSITE_SPACES = [
]
TESTING_COMPOSITE_SPACES_IDS = [f"{space}" for space in TESTING_COMPOSITE_SPACES]
TESTING_SPACES: List[Space] = TESTING_FUNDAMENTAL_SPACES + TESTING_COMPOSITE_SPACES
TESTING_SPACES: list[Space] = TESTING_FUNDAMENTAL_SPACES + TESTING_COMPOSITE_SPACES
TESTING_SPACES_IDS = TESTING_FUNDAMENTAL_SPACES_IDS + TESTING_COMPOSITE_SPACES_IDS

4
tests/utils/test_env_checker.py
View File

@@ -2,7 +2,7 @@
import re
import warnings
from typing import Callable, Tuple, Union
from collections.abc import Callable
import numpy as np
import pytest
@@ -137,7 +137,7 @@ def test_check_reset_seed_determinism(test, func: Callable, message: str):
def _deprecated_return_info(
self, return_info: bool = False
) -> Union[Tuple[ObsType, dict], ObsType]:
) -> tuple[ObsType, dict] | ObsType:
"""function to simulate the signature and behavior of a `reset` function with the deprecated `return_info` optional argument"""
if return_info:
return self.observation_space.sample(), {}

8
tests/utils/test_passive_env_checker.py
View File

@@ -1,6 +1,6 @@
import re
import warnings
from typing import Callable, Dict, Union
from collections.abc import Callable
import numpy as np
import pytest
@@ -143,7 +143,7 @@ def test_check_observation_space(test, space, message: str):
],
)
def test_check_action_space(
test: Union[UserWarning, type], space: spaces.Space, message: str
test: UserWarning | type, space: spaces.Space, message: str
):
"""Tests the check action space function."""
if test is UserWarning:
@@ -267,7 +267,7 @@ def _make_reset_results(results):
],
],
)
def test_passive_env_reset_checker(test, func: Callable, message: str, kwargs: Dict):
def test_passive_env_reset_checker(test, func: Callable, message: str, kwargs: dict):
"""Tests the passive env reset check"""
if test is UserWarning:
with pytest.warns(
@@ -352,7 +352,7 @@ def _modified_step(
],
)
def test_passive_env_step_checker(
test: Union[UserWarning, type], func: Callable, message: str
test: UserWarning | type, func: Callable, message: str
):
"""Tests the passive env step checker."""
if test is UserWarning:

2
tests/utils/test_play.py
View File

@@ -1,6 +1,6 @@
from collections.abc import Callable
from functools import partial
from itertools import product
from typing import Callable
import numpy as np
import pygame

5
tests/vector/testing_utils.py
View File

@@ -1,7 +1,6 @@
"""Testing utilitys for `gymnasium.vector`."""
import time
from typing import Optional
import numpy as np
@@ -67,7 +66,7 @@ class SlowEnv(gym.Env):
)
self.action_space = Box(low=0.0, high=1.0, shape=(), dtype=np.float32)
def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
def reset(self, *, seed: int | None = None, options: dict | None = None):
"""Resets the environment with a time sleep."""
super().reset(seed=seed)
if self.slow_reset > 0:
@@ -113,7 +112,7 @@ class CustomSpaceEnv(gym.Env):
self.observation_space = CustomSpace()
self.action_space = CustomSpace()
def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
def reset(self, *, seed: int | None = None, options: dict | None = None):
"""Resets the environment."""
super().reset(seed=seed)
return "reset", {}

2
tests/vector/utils/test_space_utils.py
View File

@@ -2,7 +2,7 @@
import copy
import re
from typing import Iterable
from collections.abc import Iterable
import numpy as np
import pytest

9
tests/wrappers/test_record_video.py
View File

@@ -2,7 +2,6 @@
import os
import shutil
from typing import List
import numpy as np
import pytest
@@ -151,13 +150,13 @@ def test_with_rgb_array_list(n_steps: int = 10):
env.step(env.action_space.sample())
render_out = env.render()
assert isinstance(render_out, List)
assert isinstance(render_out, list)
assert len(render_out) == n_steps + 1
assert all(isinstance(render, np.ndarray) for render in render_out)
assert all(render.ndim == 3 for render in render_out)
render_out = env.render()
assert isinstance(render_out, List)
assert isinstance(render_out, list)
assert len(render_out) == 0
env.close()
@@ -173,13 +172,13 @@ def test_with_rgb_array_list(n_steps: int = 10):
env.step(env.action_space.sample())
render_out = env.render()
assert isinstance(render_out, List)
assert isinstance(render_out, list)
assert len(render_out) == n_steps + 1
assert all(isinstance(render, np.ndarray) for render in render_out)
assert all(render.ndim == 3 for render in render_out)
render_out = env.render()
assert isinstance(render_out, List)
assert isinstance(render_out, list)
assert len(render_out) == 0
env.close()

4
tests/wrappers/vector/test_normalize_reward.py
View File

@@ -1,7 +1,5 @@
"""Test suite for vector NormalizeReward wrapper."""
from typing import Optional
import numpy as np
from gymnasium import wrappers
@@ -10,7 +8,7 @@ from gymnasium.vector import SyncVectorEnv
from tests.testing_env import GenericTestEnv
def reset_func(self, seed: Optional[int] = None, options: Optional[dict] = None):
def reset_func(self, seed: int | None = None, options: dict | None = None):
self.step_id = 0
return self.observation_space.sample(), {}