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Gymnasium/tests/test_core.py

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Seeding update (#2422) * 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
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from typing import Optional
import numpy as np
import pytest
from gym import core, spaces
from gym.wrappers import OrderEnforcing, TimeLimit
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class ArgumentEnv(core.Env):
calls = 0
def __init__(self, arg):
self.calls += 1
self.arg = arg
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class UnittestEnv(core.Env):
observation_space = spaces.Box(low=0, high=255, shape=(64, 64, 3), dtype=np.uint8)
action_space = spaces.Discrete(3)
def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
Seeding update (#2422) * 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
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super().reset(seed=seed)
return self.observation_space.sample() # Dummy observation
def step(self, action):
observation = self.observation_space.sample() # Dummy observation
return (observation, 0.0, False, {})
class UnknownSpacesEnv(core.Env):
"""This environment defines its observation & action spaces only
after the first call to reset. Although this pattern is sometimes
necessary when implementing a new environment (e.g. if it depends
on external resources), it is not encouraged.
"""
def reset(
self,
*,
seed: Optional[int] = None,
return_info: bool = False,
options: Optional[dict] = None
):
Seeding update (#2422) * 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
2021-12-08 22:14:15 +01:00
super().reset(seed=seed)
self.observation_space = spaces.Box(
low=0, high=255, shape=(64, 64, 3), dtype=np.uint8
)
self.action_space = spaces.Discrete(3)
if not return_info:
return self.observation_space.sample() # Dummy observation
else:
return self.observation_space.sample(), {} # Dummy observation with info
def step(self, action):
observation = self.observation_space.sample() # Dummy observation
return (observation, 0.0, False, {})
class OldStyleEnv(core.Env):
"""This environment doesn't accept any arguments in reset, ideally we want to support this too (for now)"""
def __init__(self):
pass
def reset(self):
super().reset()
return 0
def step(self, action):
return 0, 0, False, {}
class NewPropertyWrapper(core.Wrapper):
def __init__(
self,
env,
observation_space=None,
action_space=None,
reward_range=None,
metadata=None,
):
super().__init__(env)
if observation_space is not None:
# Only set the observation space if not None to test property forwarding
self.observation_space = observation_space
if action_space is not None:
self.action_space = action_space
if reward_range is not None:
self.reward_range = reward_range
if metadata is not None:
self.metadata = metadata
def test_env_instantiation():
# This looks like a pretty trivial, but given our usage of
# __new__, it's worth having.
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env = ArgumentEnv("arg")
assert env.arg == "arg"
assert env.calls == 1
properties = [
{
"observation_space": spaces.Box(
low=0.0, high=1.0, shape=(64, 64, 3), dtype=np.float32
)
},
{"action_space": spaces.Discrete(2)},
{"reward_range": (-1.0, 1.0)},
{"metadata": {"render_modes": ["human", "rgb_array"]}},
{
"observation_space": spaces.Box(
low=0.0, high=1.0, shape=(64, 64, 3), dtype=np.float32
),
"action_space": spaces.Discrete(2),
},
]
@pytest.mark.parametrize("class_", [UnittestEnv, UnknownSpacesEnv])
@pytest.mark.parametrize("props", properties)
def test_wrapper_property_forwarding(class_, props):
env = class_()
env = NewPropertyWrapper(env, **props)
# If UnknownSpacesEnv, then call reset to define the spaces
if isinstance(env.unwrapped, UnknownSpacesEnv):
_ = env.reset()
# Test the properties set by the wrapper
for key, value in props.items():
assert getattr(env, key) == value
# Otherwise, test if the properties are forwarded
all_properties = {"observation_space", "action_space", "reward_range", "metadata"}
for key in all_properties - props.keys():
assert getattr(env, key) == getattr(env.unwrapped, key)
def test_compatibility_with_old_style_env():
env = OldStyleEnv()
env = OrderEnforcing(env)
env = TimeLimit(env)
obs = env.reset()
assert obs == 0