def test_discrete_space():
discrete_space = bindings.Discrete(12)
assert not discrete_space.contains(bindings.Sample([-1])), \
"Discrete object failed to verify if a sample belongs to its space"
assert not discrete_space.contains(bindings.Sample([13])), \
"Discrete object failed to verify if a sample belongs to its space"
for n in range(50):
sample = discrete_space.sample()
assert sample.getBuffer_i().size() == 1, \
"Wrong size of the sample extracted from a discrete space"
assert isinstance(sample.getBuffer_i()[0], int), \
"Wrong data type of the sample extracted from a discrete space"
assert discrete_space.contains(sample), \
"Sampled data is not contained in the discrete space object that created it"
def test_sample(create_std_vector):
vector = create_std_vector
sample = bindings.Sample(vector)
for i in range(0, vector.size()-1):
assert sample.getBuffer_d()[i] == vector[i], \
"Sample object does not contain correct data"
sample.getBuffer_d()[2] = 42
assert sample.get_d(2).value() == 42, "Failed to insert data in the Sample object"
assert sample.getBuffer_d()[2] == 42, "Failed to update data of a Sample object"
def test_box_space():
size = 4
box = bindings.Box(-1, 42, [size])
# By default the data precision of python list is float. Force double.
assert box.contains(bindings.Sample(bindings.Vector_d([0, pi, 12, 42]))), \
"Box object failed to verify if a sample belongs to its space"
assert not box.contains(bindings.Sample(bindings.Vector_d([0, pi, 12, 43]))), \
"Box object failed to verify if a sample belongs to its space"
assert not box.contains(bindings.Sample(bindings.Vector_d([0]))), \
"Box object failed to verify if a sample belongs to its space"
assert not box.contains(bindings.Sample(bindings.Vector_d([0, pi, 12, 43, 0]))), \
"Box object failed to verify if a sample belongs to its space"
for n in range(50):
sample = box.sample()
assert sample.getBuffer_d().size() == size, \
"Wrong size of the sample extracted from a box space"
assert isinstance(sample.getBuffer_d()[0], float), \
"Wrong data type of the sample extracted from the box space"
assert box.contains(sample), \
"Sampled data is not contained in the box space object that created it"
def step(self, action: Action) -> State:
assert self.action_space.contains(action), \
"The action does not belong to the action space"
# The bindings do not accept yet numpy types as arguments. We need to covert
# numpy variables to the closer python type.
# Check if the input variable is a numpy type
is_numpy = type(action).__module__ == np.__name__
if is_numpy:
if isinstance(action, np.ndarray):
action = action.tolist()
elif isinstance(action, np.number):
action = action.item()
else:
assert False
# Actions must be std::vector objects, so if the passed action is a scalar
# we have to store it inside a list object before passing it to the bindings
if isinstance(action, Number):
action_list = [action]
else:
action_list = list(action)
# Create the gympp::Sample object
action_buffer = getattr(bindings, 'Vector' + self._act_dt)(action_list)
action_sample = bindings.Sample(action_buffer)
# Execute the step and get the std::optional<gympp::State> object
state_optional = self.gympp_env.step(action_sample)
assert state_optional.has_value()
# Get the gympp::State
state = state_optional.value()
# Get the std::vector buffer of gympp::Observation
observation_vector = getattr(state.observation, 'getBuffer' + self._obs_dt)()
assert observation_vector, "Failed to get the observation buffer"
assert observation_vector.size() > 0, "The observation does not contain elements"
# Convert the SWIG type to a list
observation_list = list(observation_vector)
# Convert the observation to a numpy array (this is the only required copy)
if isinstance(self.observation_space, gym.spaces.Box):
observation = np.array(observation_list)
elif isinstance(self.observation_space, gym.spaces.Discrete):
assert observation_vector.size() == 1, "The buffer has the wrong dimension"
observation = observation_list[0]
else:
assert False, "Space not supported"
assert self.observation_space.contains(observation), \
"The returned observation does not belong to the space"
# Create the info dict
info = {'gympp': state.info}
# Return the tuple
return State((observation, state.reward, state.done, info))