mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-01 22:11:25 +00:00
c364506710
* Ditch most of the seeding.py and replace np_random with the numpy default_rng. Let's see if tests pass * Updated a bunch of RNG calls from the RandomState API to Generator API * black; didn't expect that, did ya? * Undo a typo * blaaack * More typo fixes * Fixed setting/getting state in multidiscrete spaces * Fix typo, fix a test to work with the new sampling * Correctly (?) pass the randomly generated seed if np_random is called with None as seed * Convert the Discrete sample to a python int (as opposed to np.int64) * Remove some redundant imports * First version of the compatibility layer for old-style RNG. Mainly to trigger tests. * Removed redundant f-strings * Style fixes, removing unused imports * Try to make tests pass by removing atari from the dockerfile * Try to make tests pass by removing atari from the setup * Try to make tests pass by removing atari from the setup * Try to make tests pass by removing atari from the setup * First attempt at deprecating `env.seed` and supporting `env.reset(seed=seed)` instead. Tests should hopefully pass but throw up a million warnings. * black; didn't expect that, didya? * Rename the reset parameter in VecEnvs back to `seed` * Updated tests to use the new seeding method * Removed a bunch of old `seed` calls. Fixed a bug in AsyncVectorEnv * Stop Discrete envs from doing part of the setup (and using the randomness) in init (as opposed to reset) * Add explicit seed to wrappers reset * Remove an accidental return * Re-add some legacy functions with a warning. * Use deprecation instead of regular warnings for the newly deprecated methods/functions
145 lines
4.7 KiB
Python
145 lines
4.7 KiB
Python
from typing import Optional
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import numpy as np
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import gym
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from gym import spaces
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from gym.utils import seeding
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# Unit test environment for CNNs.
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# Looks like this (RGB observations):
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#
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# ---------------------------
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# | |
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# | ****** |
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# | ****** |
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# | ** ** |
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# | ** ** |
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# | ** |
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# | ** |
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# | **** |
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# | **** |
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# | **** |
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# | **** |
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# | ********** |
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# | ********** |
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# | |
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# ---------------------------
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#
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# Agent should hit action 2 to gain reward. Catches off-by-one errors in your agent.
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#
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# To see how it works, run:
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#
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# python examples/agents/keyboard_agent.py MemorizeDigits-v0
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FIELD_W = 32
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FIELD_H = 24
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bogus_mnist = [
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[" **** ", "* *", "* *", "* *", "* *", " **** "],
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[" ** ", " * * ", " * ", " * ", " * ", " *** "],
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[" **** ", "* *", " *", " *** ", "** ", "******"],
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[" **** ", "* *", " ** ", " *", "* *", " **** "],
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[" * * ", " * * ", " * * ", " **** ", " * ", " * "],
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[" **** ", " * ", " **** ", " * ", " * ", " **** "],
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[" *** ", " * ", " **** ", " * * ", " * * ", " **** "],
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[" **** ", " * ", " * ", " * ", " * ", " * "],
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[" **** ", "* *", " **** ", "* *", "* *", " **** "],
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[" **** ", "* *", "* *", " *****", " *", " **** "],
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]
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color_black = np.array((0, 0, 0)).astype("float32")
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color_white = np.array((255, 255, 255)).astype("float32")
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class MemorizeDigits(gym.Env):
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metadata = {
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"render.modes": ["human", "rgb_array"],
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"video.frames_per_second": 60,
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"video.res_w": FIELD_W,
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"video.res_h": FIELD_H,
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}
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use_random_colors = False
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def __init__(self):
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self.viewer = None
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self.observation_space = spaces.Box(
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0, 255, (FIELD_H, FIELD_W, 3), dtype=np.uint8
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)
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self.action_space = spaces.Discrete(10)
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self.bogus_mnist = np.zeros((10, 6, 6), dtype=np.uint8)
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for digit in range(10):
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for y in range(6):
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self.bogus_mnist[digit, y, :] = [
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ord(char) for char in bogus_mnist[digit][y]
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]
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self.reset()
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def random_color(self):
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return np.array(
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[
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self.np_random.integers(low=0, high=255),
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self.np_random.integers(low=0, high=255),
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self.np_random.integers(low=0, high=255),
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]
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).astype("uint8")
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def reset(self, seed: Optional[int] = None):
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super().reset(seed=seed)
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self.digit_x = self.np_random.integers(low=FIELD_W // 5, high=FIELD_W // 5 * 4)
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self.digit_y = self.np_random.integers(low=FIELD_H // 5, high=FIELD_H // 5 * 4)
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self.color_bg = self.random_color() if self.use_random_colors else color_black
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self.step_n = 0
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while 1:
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self.color_digit = (
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self.random_color() if self.use_random_colors else color_white
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)
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if np.linalg.norm(self.color_digit - self.color_bg) < 50:
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continue
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break
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self.digit = -1
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return self.step(0)[0]
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def step(self, action):
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reward = -1
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done = False
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self.step_n += 1
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if self.digit == -1:
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pass
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else:
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if self.digit == action:
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reward = +1
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done = self.step_n > 20 and 0 == self.np_random.integers(low=0, high=5)
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self.digit = self.np_random.integers(low=0, high=10)
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obs = np.zeros((FIELD_H, FIELD_W, 3), dtype=np.uint8)
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obs[:, :, :] = self.color_bg
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digit_img = np.zeros((6, 6, 3), dtype=np.uint8)
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digit_img[:] = self.color_bg
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xxx = self.bogus_mnist[self.digit] == 42
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digit_img[xxx] = self.color_digit
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obs[
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self.digit_y - 3 : self.digit_y + 3, self.digit_x - 3 : self.digit_x + 3
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] = digit_img
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self.last_obs = obs
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return obs, reward, done, {}
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def render(self, mode="human"):
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if mode == "rgb_array":
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return self.last_obs
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elif mode == "human":
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from gym.envs.classic_control import rendering
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if self.viewer is None:
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self.viewer = rendering.SimpleImageViewer()
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self.viewer.imshow(self.last_obs)
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return self.viewer.isopen
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else:
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assert 0, f"Render mode '{mode}' is not supported"
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def close(self):
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if self.viewer is not None:
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self.viewer.close()
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self.viewer = None
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