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test_ding_env_wrapper.py
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test_ding_env_wrapper.py
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import gym
import numpy as np
import pytest
from easydict import EasyDict
from ding.torch_utils import to_ndarray
from ding.envs.env import DingEnvWrapper
class FakeEnvForTest(gym.Env):
def __init__(self):
self.observation_space = gym.spaces.Box(low=-1., high=1., shape=(10, ), dtype=np.float32)
self.action_space = gym.spaces.Tuple(
(
gym.spaces.Discrete(3),
gym.spaces.Box(low=np.array([0., -1.]), high=np.array([1., 1.]), shape=(2, ), dtype=np.float32)
)
)
def step(self, action):
assert self.action_space.contains(action)
self._step_count += 1
obs = self.observation_space.sample()
obs = to_ndarray(obs).astype(np.float32)
done = True if self._step_count == 100 else False
return (obs, 0.5, done, {})
def reset(self):
self._step_count = 0
obs = self.observation_space.sample()
obs = to_ndarray(obs).astype(np.float32)
return obs
def render(self, mode="human", close=False):
pass
def close(self):
pass
gym.envs.registration.register(
id="FakeHybridForTest-v0",
entry_point="ding.envs.env.tests.test_ding_env_wrapper:FakeEnvForTest",
)
class TestDingEnvWrapper:
@pytest.mark.unittest
@pytest.mark.parametrize('env_id', ['CartPole-v0', 'Pendulum-v1'])
def test_cartpole_pendulum(self, env_id):
env = gym.make(env_id)
ding_env = DingEnvWrapper(env=env)
print(ding_env.observation_space, ding_env.action_space, ding_env.reward_space)
cfg = EasyDict(dict(
collector_env_num=16,
evaluator_env_num=3,
is_train=True,
))
l1 = ding_env.create_collector_env_cfg(cfg)
assert isinstance(l1, list)
l1 = ding_env.create_evaluator_env_cfg(cfg)
assert isinstance(l1, list)
obs = ding_env.reset()
assert isinstance(obs, np.ndarray)
action = ding_env.random_action()
# assert isinstance(action, np.ndarray)
print('random_action: {}, action_space: {}'.format(action.shape, ding_env.action_space))
@pytest.mark.envtest
def test_mujoco(self):
env_cfg = EasyDict(
env_id='Ant-v3',
env_wrapper='mujoco_default',
)
ding_env_mujoco = DingEnvWrapper(cfg=env_cfg)
obs = ding_env_mujoco.reset()
assert isinstance(obs, np.ndarray)
# action_dim = ding_env_mujoco.action_space.shape # n
while True:
# action = np.random.random(size=action_dim) # Continuous Action
action = ding_env_mujoco.random_action()
timestep = ding_env_mujoco.step(action)
# print(_, timestep.reward)
assert timestep.reward.shape == (1, ), timestep.reward.shape
if timestep.done:
assert 'eval_episode_return' in timestep.info, timestep.info
break
print(ding_env_mujoco.observation_space, ding_env_mujoco.action_space, ding_env_mujoco.reward_space)
action = ding_env_mujoco.random_action()
# assert isinstance(action, np.ndarray)
assert action.shape == ding_env_mujoco.action_space.shape
@pytest.mark.envtest
@pytest.mark.parametrize('atari_env_id', ['Pong-v4', 'MontezumaRevenge-v4'])
def test_atari(self, atari_env_id):
env_cfg = EasyDict(
env_id=atari_env_id,
env_wrapper='atari_default',
)
ding_env_atari = DingEnvWrapper(cfg=env_cfg)
ding_env_atari.enable_save_replay('atari_path/')
obs = ding_env_atari.reset()
assert isinstance(obs, np.ndarray)
assert obs.shape == ding_env_atari.observation_space.shape # (4, 84, 84)
# action_dim = ding_env_atari.action_space.n
while True:
# action = np.random.choice(range(action_dim), size=(1, )) # Discrete Action
action = ding_env_atari.random_action()
timestep = ding_env_atari.step(action)
# print(timestep.reward)
assert timestep.reward.shape == ding_env_atari.reward_space.shape, timestep.reward.shape # (1, )
if timestep.done:
assert 'eval_episode_return' in timestep.info, timestep.info
break
print(ding_env_atari.observation_space, ding_env_atari.action_space, ding_env_atari.reward_space)
action = ding_env_atari.random_action()
# assert isinstance(action, np.ndarray)
assert action.shape == (1, )
@pytest.mark.unittest
@pytest.mark.parametrize('lun_bip_env_id', ['LunarLander-v2', 'LunarLanderContinuous-v2', 'BipedalWalker-v3'])
def test_lunarlander_bipedalwalker(self, lun_bip_env_id):
env_cfg = EasyDict(
env_id=lun_bip_env_id,
env_wrapper='default',
)
ding_env_lun_bip = DingEnvWrapper(cfg=env_cfg)
obs = ding_env_lun_bip.reset()
assert isinstance(obs, np.ndarray)
assert obs.shape == ding_env_lun_bip.observation_space.shape
# action_space = ding_env_lun_bip.action_space
# if lun_bip_env_id in ['LunarLanderContinuous-v2', 'BipedalWalker-v3']:
# action_dim = action_space.shape
# else:
# action_dim = action_space.n
while True:
# if lun_bip_env_id in ['LunarLanderContinuous-v2', 'BipedalWalker-v3']:
# action = np.random.random(size=action_dim) # Continuous Action
# else:
# action = np.random.choice(range(action_dim), size=(1, )) # Discrete Action
action = ding_env_lun_bip.random_action()
timestep = ding_env_lun_bip.step(action)
# print(timestep.reward)
assert timestep.reward.shape == ding_env_lun_bip.reward_space.shape, timestep.reward.shape # (1, )
if timestep.done:
assert 'eval_episode_return' in timestep.info, timestep.info
break
print(ding_env_lun_bip.observation_space, ding_env_lun_bip.action_space, ding_env_lun_bip.reward_space)
action = ding_env_lun_bip.random_action()
# assert isinstance(action, np.ndarray)
print('random_action: {}, action_space: {}'.format(action.shape, ding_env_lun_bip.action_space))
@pytest.mark.unittest
def test_hybrid(self):
env_cfg = EasyDict(env_id='FakeHybridForTest-v0', env_wrapper='gym_hybrid_default')
ding_env_hybrid = DingEnvWrapper(cfg=env_cfg)
obs = ding_env_hybrid.reset()
assert isinstance(obs, np.ndarray)
assert obs.shape == ding_env_hybrid.observation_space.shape
while True:
action = ding_env_hybrid.random_action()
# print('random_action:', action)
for k, v in action.items():
if isinstance(v, int):
continue
# print('before: {}, after: {}'.format(v.shape, ding_env_hybrid.action_space[k].shape))
v.shape = ding_env_hybrid.action_space[k].shape
timestep = ding_env_hybrid.step(action)
# print(timestep.reward)
assert timestep.reward.shape == ding_env_hybrid.reward_space.shape, timestep.reward.shape # (1, )
if timestep.done:
assert 'eval_episode_return' in timestep.info, timestep.info
break
print(ding_env_hybrid.observation_space, ding_env_hybrid.action_space, ding_env_hybrid.reward_space)
action = ding_env_hybrid.random_action()
print('random_action', action)
assert isinstance(action, dict)
@pytest.mark.envtest
def test_AllinObsWrapper(self):
env_cfg = EasyDict(env_id='PongNoFrameskip-v4', env_wrapper='reward_in_obs')
ding_env_aio = DingEnvWrapper(cfg=env_cfg)
data = ding_env_aio.reset()
assert isinstance(data, dict)
assert 'obs' in data.keys() and 'reward' in data.keys()
assert data['obs'].shape == ding_env_aio.observation_space
while True:
action = ding_env_aio.random_action()
timestep = ding_env_aio.step(action)
# print(timestep.reward)
assert isinstance(timestep.obs, dict)
if timestep.done:
assert 'eval_episode_return' in timestep.info, timestep.info
break
print(ding_env_aio.observation_space, ding_env_aio.action_space, ding_env_aio.reward_space)