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| """Utilities of visualising an environment.""" | |
| from collections import deque | |
| from typing import Callable, Dict, List, Optional, Tuple, Union | |
| import numpy as np | |
| import gym.error | |
| from gym import Env, logger | |
| from gym.core import ActType, ObsType | |
| from gym.error import DependencyNotInstalled | |
| from gym.logger import deprecation | |
| try: | |
| import pygame | |
| from pygame import Surface | |
| from pygame.event import Event | |
| from pygame.locals import VIDEORESIZE | |
| except ImportError: | |
| raise gym.error.DependencyNotInstalled( | |
| "Pygame is not installed, run `pip install gym[classic_control]`" | |
| ) | |
| try: | |
| import matplotlib | |
| matplotlib.use("TkAgg") | |
| import matplotlib.pyplot as plt | |
| except ImportError: | |
| logger.warn("Matplotlib is not installed, run `pip install gym[other]`") | |
| matplotlib, plt = None, None | |
| class MissingKeysToAction(Exception): | |
| """Raised when the environment does not have a default ``keys_to_action`` mapping.""" | |
| class PlayableGame: | |
| """Wraps an environment allowing keyboard inputs to interact with the environment.""" | |
| def __init__( | |
| self, | |
| env: Env, | |
| keys_to_action: Optional[Dict[Tuple[int, ...], int]] = None, | |
| zoom: Optional[float] = None, | |
| ): | |
| """Wraps an environment with a dictionary of keyboard buttons to action and if to zoom in on the environment. | |
| Args: | |
| env: The environment to play | |
| keys_to_action: The dictionary of keyboard tuples and action value | |
| zoom: If to zoom in on the environment render | |
| """ | |
| if env.render_mode not in {"rgb_array", "rgb_array_list"}: | |
| logger.error( | |
| "PlayableGame wrapper works only with rgb_array and rgb_array_list render modes, " | |
| f"but your environment render_mode = {env.render_mode}." | |
| ) | |
| self.env = env | |
| self.relevant_keys = self._get_relevant_keys(keys_to_action) | |
| self.video_size = self._get_video_size(zoom) | |
| self.screen = pygame.display.set_mode(self.video_size) | |
| self.pressed_keys = [] | |
| self.running = True | |
| def _get_relevant_keys( | |
| self, keys_to_action: Optional[Dict[Tuple[int], int]] = None | |
| ) -> set: | |
| if keys_to_action is None: | |
| if hasattr(self.env, "get_keys_to_action"): | |
| keys_to_action = self.env.get_keys_to_action() | |
| elif hasattr(self.env.unwrapped, "get_keys_to_action"): | |
| keys_to_action = self.env.unwrapped.get_keys_to_action() | |
| else: | |
| raise MissingKeysToAction( | |
| f"{self.env.spec.id} does not have explicit key to action mapping, " | |
| "please specify one manually" | |
| ) | |
| assert isinstance(keys_to_action, dict) | |
| relevant_keys = set(sum((list(k) for k in keys_to_action.keys()), [])) | |
| return relevant_keys | |
| def _get_video_size(self, zoom: Optional[float] = None) -> Tuple[int, int]: | |
| rendered = self.env.render() | |
| 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]) | |
| if zoom is not None: | |
| video_size = (int(video_size[0] * zoom), int(video_size[1] * zoom)) | |
| return video_size | |
| def process_event(self, event: Event): | |
| """Processes a PyGame event. | |
| In particular, this function is used to keep track of which buttons are currently pressed | |
| and to exit the :func:`play` function when the PyGame window is closed. | |
| Args: | |
| event: The event to process | |
| """ | |
| if event.type == pygame.KEYDOWN: | |
| if event.key in self.relevant_keys: | |
| self.pressed_keys.append(event.key) | |
| elif event.key == pygame.K_ESCAPE: | |
| self.running = False | |
| elif event.type == pygame.KEYUP: | |
| if event.key in self.relevant_keys: | |
| self.pressed_keys.remove(event.key) | |
| elif event.type == pygame.QUIT: | |
| self.running = False | |
| elif event.type == VIDEORESIZE: | |
| self.video_size = event.size | |
| self.screen = pygame.display.set_mode(self.video_size) | |
| def display_arr( | |
| screen: Surface, arr: np.ndarray, video_size: Tuple[int, int], transpose: bool | |
| ): | |
| """Displays a numpy array on screen. | |
| Args: | |
| screen: The screen to show the array on | |
| arr: The array to show | |
| video_size: The video size of the screen | |
| transpose: If to transpose the array on the screen | |
| """ | |
| arr_min, arr_max = np.min(arr), np.max(arr) | |
| arr = 255.0 * (arr - arr_min) / (arr_max - arr_min) | |
| pyg_img = pygame.surfarray.make_surface(arr.swapaxes(0, 1) if transpose else arr) | |
| pyg_img = pygame.transform.scale(pyg_img, video_size) | |
| screen.blit(pyg_img, (0, 0)) | |
| def play( | |
| env: Env, | |
| transpose: Optional[bool] = True, | |
| fps: Optional[int] = None, | |
| zoom: Optional[float] = None, | |
| callback: Optional[Callable] = None, | |
| keys_to_action: Optional[Dict[Union[Tuple[Union[str, int]], str], ActType]] = None, | |
| seed: Optional[int] = None, | |
| noop: ActType = 0, | |
| ): | |
| """Allows one to play the game using keyboard. | |
| Example:: | |
| >>> import gym | |
| >>> from gym.utils.play import play | |
| >>> play(gym.make("CarRacing-v1", render_mode="rgb_array"), keys_to_action={ | |
| ... "w": np.array([0, 0.7, 0]), | |
| ... "a": np.array([-1, 0, 0]), | |
| ... "s": np.array([0, 0, 1]), | |
| ... "d": np.array([1, 0, 0]), | |
| ... "wa": np.array([-1, 0.7, 0]), | |
| ... "dw": np.array([1, 0.7, 0]), | |
| ... "ds": np.array([1, 0, 1]), | |
| ... "as": np.array([-1, 0, 1]), | |
| ... }, noop=np.array([0,0,0])) | |
| Above code works also if the environment is wrapped, so it's particularly useful in | |
| verifying that the frame-level preprocessing does not render the game | |
| unplayable. | |
| If you wish to plot real time statistics as you play, you can use | |
| :class:`gym.utils.play.PlayPlot`. Here's a sample code for plotting the reward | |
| for last 150 steps. | |
| >>> def callback(obs_t, obs_tp1, action, rew, terminated, truncated, info): | |
| ... return [rew,] | |
| >>> plotter = PlayPlot(callback, 150, ["reward"]) | |
| >>> play(gym.make("ALE/AirRaid-v5"), callback=plotter.callback) | |
| Args: | |
| env: Environment to use for playing. | |
| transpose: If this is ``True``, the output of observation is transposed. Defaults to ``True``. | |
| fps: Maximum number of steps of the environment executed every second. If ``None`` (the default), | |
| ``env.metadata["render_fps""]`` (or 30, if the environment does not specify "render_fps") is used. | |
| zoom: Zoom the observation in, ``zoom`` amount, should be positive float | |
| callback: If a callback is provided, it will be executed after every step. It takes the following input: | |
| obs_t: observation before performing action | |
| obs_tp1: observation after performing action | |
| action: action that was executed | |
| rew: reward that was received | |
| terminated: whether the environment is terminated or not | |
| truncated: whether the environment is truncated or not | |
| info: debug info | |
| keys_to_action: Mapping from keys pressed to action performed. | |
| Different formats are supported: Key combinations can either be expressed as a tuple of unicode code | |
| points of the keys, as a tuple of characters, or as a string where each character of the string represents | |
| one key. | |
| For example if pressing 'w' and space at the same time is supposed | |
| to trigger action number 2 then ``key_to_action`` dict could look like this: | |
| >>> { | |
| ... # ... | |
| ... (ord('w'), ord(' ')): 2 | |
| ... # ... | |
| ... } | |
| or like this: | |
| >>> { | |
| ... # ... | |
| ... ("w", " "): 2 | |
| ... # ... | |
| ... } | |
| or like this: | |
| >>> { | |
| ... # ... | |
| ... "w ": 2 | |
| ... # ... | |
| ... } | |
| If ``None``, default ``key_to_action`` mapping for that environment is used, if provided. | |
| seed: Random seed used when resetting the environment. If None, no seed is used. | |
| noop: The action used when no key input has been entered, or the entered key combination is unknown. | |
| """ | |
| env.reset(seed=seed) | |
| if keys_to_action is None: | |
| if hasattr(env, "get_keys_to_action"): | |
| keys_to_action = env.get_keys_to_action() | |
| elif hasattr(env.unwrapped, "get_keys_to_action"): | |
| keys_to_action = env.unwrapped.get_keys_to_action() | |
| else: | |
| raise MissingKeysToAction( | |
| f"{env.spec.id} does not have explicit key to action mapping, " | |
| "please specify one manually" | |
| ) | |
| assert keys_to_action is not None | |
| key_code_to_action = {} | |
| for key_combination, action in keys_to_action.items(): | |
| key_code = tuple( | |
| sorted(ord(key) if isinstance(key, str) else key for key in key_combination) | |
| ) | |
| key_code_to_action[key_code] = action | |
| game = PlayableGame(env, key_code_to_action, zoom) | |
| if fps is None: | |
| fps = env.metadata.get("render_fps", 30) | |
| done, obs = True, None | |
| clock = pygame.time.Clock() | |
| while game.running: | |
| if done: | |
| done = False | |
| obs = env.reset(seed=seed) | |
| else: | |
| action = key_code_to_action.get(tuple(sorted(game.pressed_keys)), noop) | |
| prev_obs = obs | |
| obs, rew, terminated, truncated, info = env.step(action) | |
| done = terminated or truncated | |
| if callback is not None: | |
| callback(prev_obs, obs, action, rew, terminated, truncated, info) | |
| if obs is not None: | |
| rendered = env.render() | |
| if isinstance(rendered, List): | |
| rendered = rendered[-1] | |
| assert rendered is not None and isinstance(rendered, np.ndarray) | |
| display_arr( | |
| game.screen, rendered, transpose=transpose, video_size=game.video_size | |
| ) | |
| # process pygame events | |
| for event in pygame.event.get(): | |
| game.process_event(event) | |
| pygame.display.flip() | |
| clock.tick(fps) | |
| pygame.quit() | |
| class PlayPlot: | |
| """Provides a callback to create live plots of arbitrary metrics when using :func:`play`. | |
| This class is instantiated with a function that accepts information about a single environment transition: | |
| - obs_t: observation before performing action | |
| - obs_tp1: observation after performing action | |
| - action: action that was executed | |
| - rew: reward that was received | |
| - terminated: whether the environment is terminated or not | |
| - truncated: whether the environment is truncated or not | |
| - info: debug info | |
| It should return a list of metrics that are computed from this data. | |
| For instance, the function may look like this:: | |
| >>> def compute_metrics(obs_t, obs_tp, action, reward, terminated, truncated, info): | |
| ... return [reward, info["cumulative_reward"], np.linalg.norm(action)] | |
| :class:`PlayPlot` provides the method :meth:`callback` which will pass its arguments along to that function | |
| and uses the returned values to update live plots of the metrics. | |
| Typically, this :meth:`callback` will be used in conjunction with :func:`play` to see how the metrics evolve as you play:: | |
| >>> plotter = PlayPlot(compute_metrics, horizon_timesteps=200, | |
| ... plot_names=["Immediate Rew.", "Cumulative Rew.", "Action Magnitude"]) | |
| >>> play(your_env, callback=plotter.callback) | |
| """ | |
| def __init__( | |
| self, callback: callable, horizon_timesteps: int, plot_names: List[str] | |
| ): | |
| """Constructor of :class:`PlayPlot`. | |
| The function ``callback`` that is passed to this constructor should return | |
| a list of metrics that is of length ``len(plot_names)``. | |
| Args: | |
| callback: Function that computes metrics from environment transitions | |
| horizon_timesteps: The time horizon used for the live plots | |
| plot_names: List of plot titles | |
| Raises: | |
| DependencyNotInstalled: If matplotlib is not installed | |
| """ | |
| deprecation( | |
| "`PlayPlot` is marked as deprecated and will be removed in the near future." | |
| ) | |
| self.data_callback = callback | |
| self.horizon_timesteps = horizon_timesteps | |
| self.plot_names = plot_names | |
| if plt is None: | |
| raise DependencyNotInstalled( | |
| "matplotlib is not installed, run `pip install gym[other]`" | |
| ) | |
| num_plots = len(self.plot_names) | |
| self.fig, self.ax = plt.subplots(num_plots) | |
| if num_plots == 1: | |
| self.ax = [self.ax] | |
| for axis, name in zip(self.ax, plot_names): | |
| axis.set_title(name) | |
| self.t = 0 | |
| self.cur_plot: List[Optional[plt.Axes]] = [None for _ in range(num_plots)] | |
| self.data = [deque(maxlen=horizon_timesteps) for _ in range(num_plots)] | |
| def callback( | |
| self, | |
| obs_t: ObsType, | |
| obs_tp1: ObsType, | |
| action: ActType, | |
| rew: float, | |
| terminated: bool, | |
| truncated: bool, | |
| info: dict, | |
| ): | |
| """The callback that calls the provided data callback and adds the data to the plots. | |
| Args: | |
| obs_t: The observation at time step t | |
| obs_tp1: The observation at time step t+1 | |
| action: The action | |
| rew: The reward | |
| terminated: If the environment is terminated | |
| truncated: If the environment is truncated | |
| info: The information from the environment | |
| """ | |
| points = self.data_callback( | |
| obs_t, obs_tp1, action, rew, terminated, truncated, info | |
| ) | |
| for point, data_series in zip(points, self.data): | |
| data_series.append(point) | |
| self.t += 1 | |
| xmin, xmax = max(0, self.t - self.horizon_timesteps), self.t | |
| for i, plot in enumerate(self.cur_plot): | |
| if plot is not None: | |
| plot.remove() | |
| self.cur_plot[i] = self.ax[i].scatter( | |
| range(xmin, xmax), list(self.data[i]), c="blue" | |
| ) | |
| self.ax[i].set_xlim(xmin, xmax) | |
| if plt is None: | |
| raise DependencyNotInstalled( | |
| "matplotlib is not installed, run `pip install gym[other]`" | |
| ) | |
| plt.pause(0.000001) | |