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import pytest
import numpy as np
from easydict import EasyDict
from torch import rand
from dizoo.classic_control.pendulum.envs import PendulumEnv
@pytest.mark.envtest
class TestPendulumEnv:
def test_naive(self):
env = PendulumEnv(EasyDict({'act_scale': True}))
env.seed(314)
assert env._seed == 314
obs = env.reset()
assert obs.shape == (3, )
for i in range(10):
# Both ``env.random_action()``, and utilizing ``np.random`` as well as action space,
# can generate legal random action.
if i < 5:
random_action = np.tanh(np.random.random(1))
else:
random_action = env.random_action()
timestep = env.step(random_action)
assert timestep.obs.shape == (3, )
assert timestep.reward.shape == (1, )
assert timestep.reward >= env.reward_space.low
assert timestep.reward <= env.reward_space.high
# assert isinstance(timestep, tuple)
print(env.observation_space, env.action_space, env.reward_space)
env.close()
def test_discrete(self):
env = PendulumEnv(EasyDict({'act_scale': True, 'continuous': False}))
env.seed(314)
assert env._seed == 314
obs = env.reset()
assert obs.shape == (3, )
for i in range(10):
# Both ``env.random_action()``, and utilizing ``np.random`` as well as action space,
# can generate legal random action.
if i < 5:
random_action = np.array([env.action_space.sample()])
else:
random_action = env.random_action()
timestep = env.step(random_action)
print(env.observation_space, env.action_space, env.reward_space)
print(timestep.reward, timestep.obs, timestep.reward)
assert timestep.reward.shape == (1, )
assert timestep.obs.shape == (3, )
assert timestep.reward >= env.reward_space.low
assert timestep.reward <= env.reward_space.high
# assert isinstance(timestep, tuple)
print(env.observation_space, env.action_space, env.reward_space)
env.close()
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