def __init__(self, desc='4x4', max_episode_length=None):
"""Initialize the environment.
Args:
desc (str): grid configuration key.
max_episode_length (int): The maximum steps allowed for an episode.
"""
if isinstance(desc, str):
desc = MAPS[desc]
desc = np.array(list(map(list, desc)))
desc[desc == '.'] = 'F'
desc[desc == 'o'] = 'H'
desc[desc == 'x'] = 'W'
self._desc = desc
self._n_row, self._n_col = desc.shape
(start_x, ), (start_y, ) = np.nonzero(desc == 'S')
self._start_state = start_x * self._n_col + start_y
self._state = None
self._domain_fig = None
self._step_cnt = None
self._max_episode_length = max_episode_length
self._action_space = akro.Discrete(4)
self._observation_space = akro.Discrete(self._n_row * self._n_col)
self._spec = EnvSpec(action_space=self.action_space,
observation_space=self.observation_space,
max_episode_length=max_episode_length)
def action_space(self):
"""Return the action space.
Returns:
akro.Box: Action space.
"""
if self.act_space_type == 'box':
return akro.Box(low=-5.0, high=5.0, shape=(1, ), dtype=np.float32)
else:
return akro.Discrete(5)
def eps_data():
# spaces
obs_space = akro.Box(low=1, high=np.inf, shape=(4, 3, 2), dtype=np.float32)
act_space = akro.Discrete(2)
env_spec = EnvSpec(obs_space, act_space)
# generate data
lens = np.array([10, 20, 7, 25, 25, 40, 10, 5])
n_t = lens.sum()
obs = np.stack([obs_space.low] * n_t)
last_obs = np.stack([obs_space.low] * len(lens))
act = np.stack([1] * n_t)
rew = np.arange(n_t)
# env_infos
env_infos = dict()
env_infos['goal'] = np.stack([[1, 1]] * n_t)
env_infos['foo'] = np.arange(n_t)
# agent_infos
agent_infos = dict()
agent_infos['prev_action'] = act
agent_infos['hidden'] = np.arange(n_t)
# step_types
step_types = []
for size in lens:
step_types.extend([StepType.FIRST] + [StepType.MID] * (size - 2) +
[StepType.TERMINAL])
step_types = np.array(step_types, dtype=StepType)
# episode_infos
episode_infos = dict()
episode_infos['task_one_hot'] = np.stack([[1, 1]] * len(lens))
return {
'env_spec': env_spec,
'episode_infos': episode_infos,
'observations': obs,
'last_observations': last_obs,
'actions': act,
'rewards': rew,
'env_infos': env_infos,
'agent_infos': agent_infos,
'step_types': step_types,
'lengths': lens
}
def test_act_env_spec_mismatch_time_step(sample_data):
with pytest.raises(ValueError,
match='action must conform to action_space'):
sample_data['action'] = sample_data['action'][:-1]
s = TimeStep(**sample_data)
del s
obs_space = akro.Box(low=1, high=10, shape=(4, 3, 2), dtype=np.float32)
act_space = akro.Discrete(5)
env_spec = EnvSpec(obs_space, act_space)
sample_data['env_spec'] = env_spec
with pytest.raises(ValueError,
match='action should have the same dimensionality'):
sample_data['action'] = sample_data['action'][:-1]
s = TimeStep(**sample_data)
del s
def __init__(self, env, name=None):
"""Create a DMControlEnv.
Args:
env (dm_control.suite.Task): The wrapped dm_control environment.
name (str): Name of the environment.
"""
self._env = env
self._name = name or type(env.task).__name__
self._viewer = None
self._step_cnt = None
self._max_episode_length = self._env._step_limit
# action space
action_spec = self._env.action_spec()
if (len(action_spec.shape) == 1) and (-np.inf in action_spec.minimum or
np.inf in action_spec.maximum):
self._action_space = akro.Discrete(np.prod(action_spec.shape))
else:
self._action_space = akro.Box(low=action_spec.minimum,
high=action_spec.maximum,
dtype=np.float32)
# observation_space
flat_dim = _flat_shape(self._env.observation_spec())
self._observation_space = akro.Box(low=-np.inf,
high=np.inf,
shape=[flat_dim],
dtype=np.float32)
# spec
self._spec = EnvSpec(action_space=self.action_space,
observation_space=self.observation_space,
max_episode_length=self._max_episode_length)
def action_space(self):
"""akro.Discrete: an action space."""
return akro.Discrete(self._action_dim)
