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https://github.com/Farama-Foundation/Gymnasium.git
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850247f888
* Updated cartpole-v0 to v1 to prevent warning and added pytest.mark.filterwarnings for tests where warnings are unavoidable * Change np.bool to bool as numpy raises a warning and bool is the suggested solution * Seeding randint is deprecated in the future, integers is new solution * Fixed errors thrown when the video recorder is deleted but not closed * spaces.Box expects a floating array, updated all cases where this was not true and modified float32 to float64 as float array default to float64. Otherwise space.Box raises warning that dtype precision (float32) is lower than array precision (float64). * Added pytest.mark.filterwarnings to preventing the raising of an intended warning * Added comment to explain why a warning is raised that can't be prevented without version update to the environment * Added comment to explain why warning is raised * Changed values to float as expected by the box which default to float64 * Removed --forked from pytest as the pytest-forked project is no being maintained and was not raising warnings as expected * When AsyncVectorEnv has shared_memory=True then a ValueError is raised before _state is initialised. Therefore, on the destruction on the env an error is thrown in .close_extra as _state does not exist * Possible fix that was causing an error in test_call_async_vector_env by ensuring that pygame resources are released * Pygame throws an error with ALSA when closed, using a fix from PettingZoo (https://github.com/Farama-Foundation/PettingZoo/blob/master/pettingzoo/__init__.py). We use the dsp audiodriver to prevent this issue * Modification due to running pre-commit locally * Updated cartpole-v0 to v1 to prevent warning and added pytest.mark.filterwarnings for tests where warnings are unavoidable * Change np.bool to bool as numpy raises a warning and bool is the suggested solution * Seeding randint is deprecated in the future, integers is new solution * Fixed errors thrown when the video recorder is deleted but not closed * spaces.Box expects a floating array, updated all cases where this was not true and modified float32 to float64 as float array default to float64. Otherwise space.Box raises warning that dtype precision (float32) is lower than array precision (float64). * Added pytest.mark.filterwarnings to preventing the raising of an intended warning * Added comment to explain why a warning is raised that can't be prevented without version update to the environment * Added comment to explain why warning is raised * Changed values to float as expected by the box which default to float64 * Removed --forked from pytest as the pytest-forked project is no being maintained and was not raising warnings as expected * When AsyncVectorEnv has shared_memory=True then a ValueError is raised before _state is initialised. Therefore, on the destruction on the env an error is thrown in .close_extra as _state does not exist * Possible fix that was causing an error in test_call_async_vector_env by ensuring that pygame resources are released * Pygame throws an error with ALSA when closed, using a fix from PettingZoo (https://github.com/Farama-Foundation/PettingZoo/blob/master/pettingzoo/__init__.py). We use the dsp audiodriver to prevent this issue * Modification due to running pre-commit locally
134 lines
3.4 KiB
Python
134 lines
3.4 KiB
Python
from typing import Optional
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import numpy as np
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import gym
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import time
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from gym.spaces import Box, Discrete, MultiDiscrete, MultiBinary, Tuple, Dict
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spaces = [
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Box(low=np.array(-1.0), high=np.array(1.0), dtype=np.float64),
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Box(low=np.array([0.0]), high=np.array([10.0]), dtype=np.float64),
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Box(
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low=np.array([-1.0, 0.0, 0.0]), high=np.array([1.0, 1.0, 1.0]), dtype=np.float64
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),
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Box(
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low=np.array([[-1.0, 0.0], [0.0, -1.0]]), high=np.ones((2, 2)), dtype=np.float64
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),
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Box(low=0, high=255, shape=(), dtype=np.uint8),
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Box(low=0, high=255, shape=(32, 32, 3), dtype=np.uint8),
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Discrete(2),
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Discrete(5, start=-2),
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Tuple((Discrete(3), Discrete(5))),
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Tuple(
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(
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Discrete(7),
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Box(low=np.array([0.0, -1.0]), high=np.array([1.0, 1.0]), dtype=np.float64),
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)
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),
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MultiDiscrete([11, 13, 17]),
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MultiBinary(19),
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Dict(
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{
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"position": Discrete(23),
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"velocity": Box(
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low=np.array([0.0]), high=np.array([1.0]), dtype=np.float64
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),
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}
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),
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Dict(
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{
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"position": Dict({"x": Discrete(29), "y": Discrete(31)}),
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"velocity": Tuple(
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(Discrete(37), Box(low=0, high=255, shape=(), dtype=np.uint8))
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),
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}
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),
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]
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HEIGHT, WIDTH = 64, 64
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class UnittestSlowEnv(gym.Env):
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def __init__(self, slow_reset=0.3):
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super().__init__()
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self.slow_reset = slow_reset
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self.observation_space = Box(
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low=0, high=255, shape=(HEIGHT, WIDTH, 3), dtype=np.uint8
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)
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self.action_space = Box(low=0.0, high=1.0, shape=(), dtype=np.float32)
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def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
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super().reset(seed=seed)
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if self.slow_reset > 0:
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time.sleep(self.slow_reset)
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return self.observation_space.sample()
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def step(self, action):
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time.sleep(action)
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observation = self.observation_space.sample()
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reward, done = 0.0, False
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return observation, reward, done, {}
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class CustomSpace(gym.Space):
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"""Minimal custom observation space."""
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def sample(self):
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return "sample"
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def contains(self, x):
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return isinstance(x, str)
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def __eq__(self, other):
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return isinstance(other, CustomSpace)
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custom_spaces = [
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CustomSpace(),
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Tuple((CustomSpace(), Box(low=0, high=255, shape=(), dtype=np.uint8))),
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]
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class CustomSpaceEnv(gym.Env):
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def __init__(self):
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super().__init__()
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self.observation_space = CustomSpace()
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self.action_space = CustomSpace()
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def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
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super().reset(seed=seed)
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return "reset"
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def step(self, action):
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observation = f"step({action:s})"
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reward, done = 0.0, False
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return observation, reward, done, {}
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def make_env(env_name, seed):
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def _make():
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env = gym.make(env_name)
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env.reset(seed=seed)
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return env
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return _make
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def make_slow_env(slow_reset, seed):
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def _make():
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env = UnittestSlowEnv(slow_reset=slow_reset)
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env.reset(seed=seed)
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return env
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return _make
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def make_custom_space_env(seed):
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def _make():
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env = CustomSpaceEnv()
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env.reset(seed=seed)
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return env
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return _make
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