def __init__(self, inputs, model, input_lens):
# skip TFActionDistribution init
ActionDistribution.__init__(self, inputs, model)
self.cats = [
Categorical(input_, model)
for input_ in tf.split(inputs, input_lens, axis=1)
]
self.sample_op = self._build_sample_op()
def __init__(self, inputs, model, action_space, child_distributions,
input_lens):
# skip TFActionDistribution init
ActionDistribution.__init__(self, inputs, model)
self.input_lens = input_lens
split_inputs = tf.split(inputs, self.input_lens, axis=1)
child_list = []
for i, distribution in enumerate(child_distributions):
child_list.append(distribution(split_inputs[i], model))
self.child_distributions = child_list
def __init__(self, inputs, model):
# skip TFActionDistribution init
ActionDistribution.__init__(self, inputs, model)
input_lens = inputs.get_shape().as_list()[1]
self.gausses = [
SquashedGaussian(tf.squeeze(input_, axis=1), model)
for input_ in tf.split(inputs, input_lens, axis=1)
]
self.sample_op = self._build_sample_op()
self.sampled_action_logp_op = self.logp(self.sample_op)
def __init__(self, inputs, model):
# skip TFActionDistribution init
ActionDistribution.__init__(self, inputs, model)
input_lens = inputs.get_shape().as_list()[1]
self.cats = [
Categorical(input_, model)
for input_ in tf.unstack(inputs, input_lens, axis=1)
]
self.sample_op = self._build_sample_op()
self.sampled_action_logp_op = self.logp(self.sample_op)
def __init__(self, inputs: List[TensorType], model: ModelV2,
input_lens: Union[List[int], np.ndarray, Tuple[int, ...]]):
# skip TFActionDistribution init
ActionDistribution.__init__(self, inputs, model)
self.cats = [
Categorical(input_, model)
for input_ in tf.split(inputs, input_lens, axis=1)
]
self.sample_op = self._build_sample_op()
self.sampled_action_logp_op = self.logp(self.sample_op)
def __init__(self, inputs, model, *, child_distributions, input_lens, action_space):
ActionDistribution.__init__(self, inputs, model)
self.action_space_struct = get_base_struct_from_space(action_space)
self.input_lens = np.array(input_lens, dtype=np.int32)
split_inputs = tf.split(inputs, self.input_lens, axis=1)
self.flat_child_distributions = tree.map_structure(
lambda dist, input_: dist(input_, model), child_distributions, split_inputs
)
def __init__(self,
inputs: List[TensorType],
model: ModelV2,
input_lens: Union[List[int], np.ndarray, Tuple[int, ...]],
action_space=None):
# skip TFActionDistribution init
ActionDistribution.__init__(self, inputs, model)
self.cats = [
Categorical(input_, model)
for input_ in tf.split(inputs, input_lens, axis=1)
]
self.action_space = action_space
if self.action_space is None:
self.action_space = gym.spaces.MultiDiscrete(
[c.inputs.shape[1] for c in self.cats])
self.sample_op = self._build_sample_op()
self.sampled_action_logp_op = self.logp(self.sample_op)