"""A collections of rendering-based wrappers.
* ``RenderCollection`` - Collects rendered frames into a list
* ``RecordVideo`` - Records a video of the environments
* ``HumanRendering`` - Provides human rendering of environments with ``"rgb_array"``
"""
from __future__ import annotations
import os
from copy import deepcopy
from typing import Any, Callable, List, SupportsFloat
import numpy as np
import gymnasium as gym
from gymnasium import error, logger
from gymnasium.core import ActType, ObsType, RenderFrame
from gymnasium.error import DependencyNotInstalled
__all__ = [
"RenderCollection",
"RecordVideo",
"HumanRendering",
]
[docs]
class RenderCollection(
gym.Wrapper[ObsType, ActType, ObsType, ActType], gym.utils.RecordConstructorArgs
):
"""Collect rendered frames of an environment such ``render`` returns a ``list[RenderedFrame]``.
No vector version of the wrapper exists.
Example:
Return the list of frames for the number of steps ``render`` wasn't called.
>>> import gymnasium as gym
>>> env = gym.make("LunarLander-v3", render_mode="rgb_array")
>>> env = RenderCollection(env)
>>> _ = env.reset(seed=123)
>>> for _ in range(5):
... _ = env.step(env.action_space.sample())
...
>>> frames = env.render()
>>> len(frames)
6
>>> frames = env.render()
>>> len(frames)
0
Return the list of frames for the number of steps the episode was running.
>>> import gymnasium as gym
>>> env = gym.make("LunarLander-v3", render_mode="rgb_array")
>>> env = RenderCollection(env, pop_frames=False)
>>> _ = env.reset(seed=123)
>>> for _ in range(5):
... _ = env.step(env.action_space.sample())
...
>>> frames = env.render()
>>> len(frames)
6
>>> frames = env.render()
>>> len(frames)
6
Collect all frames for all episodes, without clearing them when render is called
>>> import gymnasium as gym
>>> env = gym.make("LunarLander-v3", render_mode="rgb_array")
>>> env = RenderCollection(env, pop_frames=False, reset_clean=False)
>>> _ = env.reset(seed=123)
>>> for _ in range(5):
... _ = env.step(env.action_space.sample())
...
>>> _ = env.reset(seed=123)
>>> for _ in range(5):
... _ = env.step(env.action_space.sample())
...
>>> frames = env.render()
>>> len(frames)
12
>>> frames = env.render()
>>> len(frames)
12
Change logs:
* v0.26.2 - Initially added
"""
def __init__(
self,
env: gym.Env[ObsType, ActType],
pop_frames: bool = True,
reset_clean: bool = True,
):
"""Initialize a :class:`RenderCollection` instance.
Args:
env: The environment that is being wrapped
pop_frames (bool): If true, clear the collection frames after ``meth:render`` is called. Default value is ``True``.
reset_clean (bool): If true, clear the collection frames when ``meth:reset`` is called. Default value is ``True``.
"""
gym.utils.RecordConstructorArgs.__init__(
self, pop_frames=pop_frames, reset_clean=reset_clean
)
gym.Wrapper.__init__(self, env)
assert env.render_mode is not None
assert not env.render_mode.endswith("_list")
self.frame_list: list[RenderFrame] = []
self.pop_frames = pop_frames
self.reset_clean = reset_clean
self.metadata = deepcopy(self.env.metadata)
if f"{self.env.render_mode}_list" not in self.metadata["render_modes"]:
self.metadata["render_modes"].append(f"{self.env.render_mode}_list")
@property
def render_mode(self):
"""Returns the collection render_mode name."""
return f"{self.env.render_mode}_list"
def step(
self, action: ActType
) -> tuple[ObsType, SupportsFloat, bool, bool, dict[str, Any]]:
"""Perform a step in the base environment and collect a frame."""
output = super().step(action)
self.frame_list.append(super().render())
return output
def reset(
self, *, seed: int | None = None, options: dict[str, Any] | None = None
) -> tuple[ObsType, dict[str, Any]]:
"""Reset the base environment, eventually clear the frame_list, and collect a frame."""
output = super().reset(seed=seed, options=options)
if self.reset_clean:
self.frame_list = []
self.frame_list.append(super().render())
return output
def render(self) -> list[RenderFrame]:
"""Returns the collection of frames and, if pop_frames = True, clears it."""
frames = self.frame_list
if self.pop_frames:
self.frame_list = []
return frames
[docs]
class RecordVideo(
gym.Wrapper[ObsType, ActType, ObsType, ActType], gym.utils.RecordConstructorArgs
):
"""Records videos of environment episodes using the environment's render function.
.. py:currentmodule:: gymnasium.utils.save_video
Usually, you only want to record episodes intermittently, say every hundredth episode or at every thousandth environment step.
To do this, you can specify ``episode_trigger`` or ``step_trigger``.
They should be functions returning a boolean that indicates whether a recording should be started at the
current episode or step, respectively.
The ``episode_trigger`` should return ``True`` on the episode when recording should start.
The ``step_trigger`` should return ``True`` on the n-th environment step that the recording should be started, where n sums over all previous episodes.
If neither :attr:`episode_trigger` nor ``step_trigger`` is passed, a default ``episode_trigger`` will be employed, i.e. :func:`capped_cubic_video_schedule`.
This function starts a video at every episode that is a power of 3 until 1000 and then every 1000 episodes.
By default, the recording will be stopped once reset is called.
However, you can also create recordings of fixed length (possibly spanning several episodes)
by passing a strictly positive value for ``video_length``.
No vector version of the wrapper exists.
Examples - Run the environment for 50 episodes, and save the video every 10 episodes starting from the 0th:
>>> import os
>>> import gymnasium as gym
>>> env = gym.make("LunarLander-v3", render_mode="rgb_array")
>>> trigger = lambda t: t % 10 == 0
>>> env = RecordVideo(env, video_folder="./save_videos1", episode_trigger=trigger, disable_logger=True)
>>> for i in range(50):
... termination, truncation = False, False
... _ = env.reset(seed=123)
... while not (termination or truncation):
... obs, rew, termination, truncation, info = env.step(env.action_space.sample())
...
>>> env.close()
>>> len(os.listdir("./save_videos1"))
5
Examples - Run the environment for 5 episodes, start a recording every 200th step, making sure each video is 100 frames long:
>>> import os
>>> import gymnasium as gym
>>> env = gym.make("LunarLander-v3", render_mode="rgb_array")
>>> trigger = lambda t: t % 200 == 0
>>> env = RecordVideo(env, video_folder="./save_videos2", step_trigger=trigger, video_length=100, disable_logger=True)
>>> for i in range(5):
... termination, truncation = False, False
... _ = env.reset(seed=123)
... _ = env.action_space.seed(123)
... while not (termination or truncation):
... obs, rew, termination, truncation, info = env.step(env.action_space.sample())
...
>>> env.close()
>>> len(os.listdir("./save_videos2"))
2
Examples - Run 3 episodes, record everything, but in chunks of 1000 frames:
>>> import os
>>> import gymnasium as gym
>>> env = gym.make("LunarLander-v3", render_mode="rgb_array")
>>> env = RecordVideo(env, video_folder="./save_videos3", video_length=1000, disable_logger=True)
>>> for i in range(3):
... termination, truncation = False, False
... _ = env.reset(seed=123)
... while not (termination or truncation):
... obs, rew, termination, truncation, info = env.step(env.action_space.sample())
...
>>> env.close()
>>> len(os.listdir("./save_videos3"))
2
Change logs:
* v0.25.0 - Initially added to replace ``wrappers.monitoring.VideoRecorder``
"""
def __init__(
self,
env: gym.Env[ObsType, ActType],
video_folder: str,
episode_trigger: Callable[[int], bool] | None = None,
step_trigger: Callable[[int], bool] | None = None,
video_length: int = 0,
name_prefix: str = "rl-video",
fps: int | None = None,
disable_logger: bool = True,
):
"""Wrapper records videos of rollouts.
Args:
env: The environment that will be wrapped
video_folder (str): The folder where the recordings will be stored
episode_trigger: Function that accepts an integer and returns ``True`` iff a recording should be started at this episode
step_trigger: Function that accepts an integer and returns ``True`` iff a recording should be started at this step
video_length (int): The length of recorded episodes. If 0, entire episodes are recorded.
Otherwise, snippets of the specified length are captured
name_prefix (str): Will be prepended to the filename of the recordings
fps (int): The frame per second in the video. Provides a custom video fps for environment, if ``None`` then
the environment metadata ``render_fps`` key is used if it exists, otherwise a default value of 30 is used.
disable_logger (bool): Whether to disable moviepy logger or not, default it is disabled
"""
gym.utils.RecordConstructorArgs.__init__(
self,
video_folder=video_folder,
episode_trigger=episode_trigger,
step_trigger=step_trigger,
video_length=video_length,
name_prefix=name_prefix,
disable_logger=disable_logger,
)
gym.Wrapper.__init__(self, env)
if env.render_mode in {None, "human", "ansi"}:
raise ValueError(
f"Render mode is {env.render_mode}, which is incompatible with RecordVideo.",
"Initialize your environment with a render_mode that returns an image, such as rgb_array.",
)
if episode_trigger is None and step_trigger is None:
from gymnasium.utils.save_video import capped_cubic_video_schedule
episode_trigger = capped_cubic_video_schedule
self.episode_trigger = episode_trigger
self.step_trigger = step_trigger
self.disable_logger = disable_logger
self.video_folder = os.path.abspath(video_folder)
if os.path.isdir(self.video_folder):
logger.warn(
f"Overwriting existing videos at {self.video_folder} folder "
f"(try specifying a different `video_folder` for the `RecordVideo` wrapper if this is not desired)"
)
os.makedirs(self.video_folder, exist_ok=True)
if fps is None:
fps = self.metadata.get("render_fps", 30)
self.frames_per_sec: int = fps
self.name_prefix: str = name_prefix
self._video_name: str | None = None
self.video_length: int = video_length if video_length != 0 else float("inf")
self.recording: bool = False
self.recorded_frames: list[RenderFrame] = []
self.render_history: list[RenderFrame] = []
self.step_id = -1
self.episode_id = -1
try:
import moviepy # noqa: F401
except ImportError as e:
raise error.DependencyNotInstalled(
'MoviePy is not installed, run `pip install "gymnasium[other]"`'
) from e
def _capture_frame(self):
assert self.recording, "Cannot capture a frame, recording wasn't started."
frame = self.env.render()
if isinstance(frame, List):
if len(frame) == 0: # render was called
return
self.render_history += frame
frame = frame[-1]
if isinstance(frame, np.ndarray):
self.recorded_frames.append(frame)
else:
self.stop_recording()
logger.warn(
f"Recording stopped: expected type of frame returned by render to be a numpy array, got instead {type(frame)}."
)
def reset(
self, *, seed: int | None = None, options: dict[str, Any] | None = None
) -> tuple[ObsType, dict[str, Any]]:
"""Reset the environment and eventually starts a new recording."""
obs, info = super().reset(seed=seed, options=options)
self.episode_id += 1
if self.recording and self.video_length == float("inf"):
self.stop_recording()
if self.episode_trigger and self.episode_trigger(self.episode_id):
self.start_recording(f"{self.name_prefix}-episode-{self.episode_id}")
if self.recording:
self._capture_frame()
if len(self.recorded_frames) > self.video_length:
self.stop_recording()
return obs, info
def step(
self, action: ActType
) -> tuple[ObsType, SupportsFloat, bool, bool, dict[str, Any]]:
"""Steps through the environment using action, recording observations if :attr:`self.recording`."""
obs, rew, terminated, truncated, info = self.env.step(action)
self.step_id += 1
if self.step_trigger and self.step_trigger(self.step_id):
self.start_recording(f"{self.name_prefix}-step-{self.step_id}")
if self.recording:
self._capture_frame()
if len(self.recorded_frames) > self.video_length:
self.stop_recording()
return obs, rew, terminated, truncated, info
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):
self.recorded_frames += render_out
if len(self.render_history) > 0:
tmp_history = self.render_history
self.render_history = []
return tmp_history + render_out
else:
return render_out
def close(self):
"""Closes the wrapper then the video recorder."""
super().close()
if self.recording:
self.stop_recording()
def start_recording(self, video_name: str):
"""Start a new recording. If it is already recording, stops the current recording before starting the new one."""
if self.recording:
self.stop_recording()
self.recording = True
self._video_name = video_name
def stop_recording(self):
"""Stop current recording and saves the video."""
assert self.recording, "stop_recording was called, but no recording was started"
if len(self.recorded_frames) == 0:
logger.warn("Ignored saving a video as there were zero frames to save.")
else:
try:
from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
except ImportError as e:
raise error.DependencyNotInstalled(
'MoviePy is not installed, run `pip install "gymnasium[other]"`'
) from e
clip = ImageSequenceClip(self.recorded_frames, fps=self.frames_per_sec)
moviepy_logger = None if self.disable_logger else "bar"
path = os.path.join(self.video_folder, f"{self._video_name}.mp4")
clip.write_videofile(path, logger=moviepy_logger)
self.recorded_frames = []
self.recording = False
self._video_name = None
def __del__(self):
"""Warn the user in case last video wasn't saved."""
if len(self.recorded_frames) > 0:
logger.warn("Unable to save last video! Did you call close()?")
[docs]
class HumanRendering(
gym.Wrapper[ObsType, ActType, ObsType, ActType], gym.utils.RecordConstructorArgs
):
"""Allows human like rendering for environments that support "rgb_array" rendering.
This wrapper is particularly useful when you have implemented an environment that can produce
RGB images but haven't implemented any code to render the images to the screen.
If you want to use this wrapper with your environments, remember to specify ``"render_fps"``
in the metadata of your environment.
The ``render_mode`` of the wrapped environment must be either ``'rgb_array'`` or ``'rgb_array_list'``.
No vector version of the wrapper exists.
Example:
>>> import gymnasium as gym
>>> from gymnasium.wrappers import HumanRendering
>>> env = gym.make("LunarLander-v3", render_mode="rgb_array")
>>> wrapped = HumanRendering(env)
>>> obs, _ = wrapped.reset() # This will start rendering to the screen
The wrapper can also be applied directly when the environment is instantiated, simply by passing
``render_mode="human"`` to ``make``. The wrapper will only be applied if the environment does not
implement human-rendering natively (i.e. ``render_mode`` does not contain ``"human"``).
>>> env = gym.make("phys2d/CartPole-v1", render_mode="human") # CartPoleJax-v1 doesn't implement human-rendering natively
>>> obs, _ = env.reset() # This will start rendering to the screen
Warning: If the base environment uses ``render_mode="rgb_array_list"``, its (i.e. the *base environment's*) render method
will always return an empty list:
>>> env = gym.make("LunarLander-v3", render_mode="rgb_array_list")
>>> wrapped = HumanRendering(env)
>>> obs, _ = wrapped.reset()
>>> env.render() # env.render() will always return an empty list!
[]
Change logs:
* v0.25.0 - Initially added
"""
ACCEPTED_RENDER_MODES = [
"rgb_array",
"rgb_array_list",
"depth_array",
"depth_array_list",
]
def __init__(self, env: gym.Env[ObsType, ActType]):
"""Initialize a :class:`HumanRendering` instance.
Args:
env: The environment that is being wrapped
"""
gym.utils.RecordConstructorArgs.__init__(self)
gym.Wrapper.__init__(self, env)
assert (
self.env.render_mode in self.ACCEPTED_RENDER_MODES
), f"Expected env.render_mode to be one of {self.ACCEPTED_RENDER_MODES} but got '{env.render_mode}'"
assert (
"render_fps" in self.env.metadata
), "The base environment must specify 'render_fps' to be used with the HumanRendering wrapper"
self.screen_size = None
self.window = None
self.clock = None
if "human" not in self.metadata["render_modes"]:
self.metadata = deepcopy(self.env.metadata)
self.metadata["render_modes"].append("human")
@property
def render_mode(self):
"""Always returns ``'human'``."""
return "human"
def step(self, action: ActType) -> tuple[ObsType, SupportsFloat, bool, bool, dict]:
"""Perform a step in the base environment and render a frame to the screen."""
result = super().step(action)
self._render_frame()
return result
def reset(
self, *, seed: int | None = None, options: dict[str, Any] | None = None
) -> tuple[ObsType, dict[str, Any]]:
"""Reset the base environment and render a frame to the screen."""
result = super().reset(seed=seed, options=options)
self._render_frame()
return result
def render(self) -> None:
"""This method doesn't do much, actual rendering is performed in :meth:`step` and :meth:`reset`."""
return None
def _render_frame(self):
"""Fetch the last frame from the base environment and render it to the screen."""
try:
import pygame
except ImportError:
raise DependencyNotInstalled(
'pygame is not installed, run `pip install "gymnasium[classic-control]"`'
)
assert self.env.render_mode is not None
if self.env.render_mode.endswith("_list"):
last_rgb_array = self.env.render()
assert isinstance(last_rgb_array, list)
last_rgb_array = last_rgb_array[-1]
else:
last_rgb_array = self.env.render()
assert isinstance(
last_rgb_array, np.ndarray
), f"Expected `env.render()` to return a numpy array, actually returned {type(last_rgb_array)}"
rgb_array = np.transpose(last_rgb_array, axes=(1, 0, 2))
if self.screen_size is None:
self.screen_size = rgb_array.shape[:2]
assert (
self.screen_size == rgb_array.shape[:2]
), f"The shape of the rgb array has changed from {self.screen_size} to {rgb_array.shape[:2]}"
if self.window is None:
pygame.init()
pygame.display.init()
self.window = pygame.display.set_mode(self.screen_size)
if self.clock is None:
self.clock = pygame.time.Clock()
surf = pygame.surfarray.make_surface(rgb_array)
self.window.blit(surf, (0, 0))
pygame.event.pump()
self.clock.tick(self.metadata["render_fps"])
pygame.display.flip()
def close(self):
"""Close the rendering window."""
if self.window is not None:
import pygame
pygame.display.quit()
pygame.quit()
super().close()
