mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-29 17:45:07 +00:00
3a8daafce1
* removed return_info, made info dict mandatory in reset * tenatively removed deprecated seed api for environments * added more info type checks to wrapper tests * formatting/style compliance * addressed some comments * polish to address review * fixed tests after merge, and added a test of the return_info deprecation assertion if found in reset signature * some organization of env_checker tests, reverted a probably merge error * added deprecation check for seed function in env * updated docstring * removed debug prints, tweaked test_check_seed_deprecation * changed return_info deprecation check from assertion to warning * fixes to vector envs, now should be correctly structured * added some explanation and typehints for mockup depcreated return info reset function * re-removed seed function from vector envs * added explanation to _reset_return_info_type and changed the return statement
121 lines
4.0 KiB
Python
121 lines
4.0 KiB
Python
"""Tests for the filter observation wrapper."""
|
|
from typing import Optional
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import gym
|
|
from gym.spaces import Box, Dict, Tuple
|
|
from gym.wrappers import FilterObservation, FlattenObservation
|
|
|
|
|
|
class FakeEnvironment(gym.Env):
|
|
def __init__(self, observation_space, render_mode=None):
|
|
self.observation_space = observation_space
|
|
self.obs_keys = self.observation_space.spaces.keys()
|
|
self.action_space = Box(shape=(1,), low=-1, high=1, dtype=np.float32)
|
|
self.render_mode = render_mode
|
|
|
|
def render(self, mode="human"):
|
|
image_shape = (32, 32, 3)
|
|
return np.zeros(image_shape, dtype=np.uint8)
|
|
|
|
def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
|
|
super().reset(seed=seed)
|
|
observation = self.observation_space.sample()
|
|
return observation, {}
|
|
|
|
def step(self, action):
|
|
del action
|
|
observation = self.observation_space.sample()
|
|
reward, terminal, info = 0.0, False, {}
|
|
return observation, reward, terminal, info
|
|
|
|
|
|
NESTED_DICT_TEST_CASES = (
|
|
(
|
|
Dict(
|
|
{
|
|
"key1": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
|
|
"key2": Dict(
|
|
{
|
|
"subkey1": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
|
|
"subkey2": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
|
|
}
|
|
),
|
|
}
|
|
),
|
|
(6,),
|
|
),
|
|
(
|
|
Dict(
|
|
{
|
|
"key1": Box(shape=(2, 3), low=-1, high=1, dtype=np.float32),
|
|
"key2": Box(shape=(), low=-1, high=1, dtype=np.float32),
|
|
"key3": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
|
|
}
|
|
),
|
|
(9,),
|
|
),
|
|
(
|
|
Dict(
|
|
{
|
|
"key1": Tuple(
|
|
(
|
|
Box(shape=(2,), low=-1, high=1, dtype=np.float32),
|
|
Box(shape=(2,), low=-1, high=1, dtype=np.float32),
|
|
)
|
|
),
|
|
"key2": Box(shape=(), low=-1, high=1, dtype=np.float32),
|
|
"key3": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
|
|
}
|
|
),
|
|
(7,),
|
|
),
|
|
(
|
|
Dict(
|
|
{
|
|
"key1": Tuple((Box(shape=(2,), low=-1, high=1, dtype=np.float32),)),
|
|
"key2": Box(shape=(), low=-1, high=1, dtype=np.float32),
|
|
"key3": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
|
|
}
|
|
),
|
|
(5,),
|
|
),
|
|
(
|
|
Dict(
|
|
{
|
|
"key1": Tuple(
|
|
(Dict({"key9": Box(shape=(2,), low=-1, high=1, dtype=np.float32)}),)
|
|
),
|
|
"key2": Box(shape=(), low=-1, high=1, dtype=np.float32),
|
|
"key3": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
|
|
}
|
|
),
|
|
(5,),
|
|
),
|
|
)
|
|
|
|
|
|
class TestNestedDictWrapper:
|
|
@pytest.mark.parametrize("observation_space, flat_shape", NESTED_DICT_TEST_CASES)
|
|
def test_nested_dicts_size(self, observation_space, flat_shape):
|
|
env = FakeEnvironment(observation_space=observation_space)
|
|
|
|
# Make sure we are testing the right environment for the test.
|
|
observation_space = env.observation_space
|
|
assert isinstance(observation_space, Dict)
|
|
|
|
wrapped_env = FlattenObservation(FilterObservation(env, env.obs_keys))
|
|
assert wrapped_env.observation_space.shape == flat_shape
|
|
|
|
assert wrapped_env.observation_space.dtype == np.float32
|
|
|
|
@pytest.mark.parametrize("observation_space, flat_shape", NESTED_DICT_TEST_CASES)
|
|
def test_nested_dicts_ravel(self, observation_space, flat_shape):
|
|
env = FakeEnvironment(observation_space=observation_space)
|
|
wrapped_env = FlattenObservation(FilterObservation(env, env.obs_keys))
|
|
obs, info = wrapped_env.reset()
|
|
assert obs.shape == wrapped_env.observation_space.shape
|
|
assert isinstance(info, dict)
|