def make_mpo_networks(
action_spec,
policy_layer_sizes=(256, 256, 256),
critic_layer_sizes=(512, 512, 256),
policy_init_std=1e-9,
obs_network=None):
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
critic_layer_sizes = list(critic_layer_sizes) + [1]
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes),
networks.MultivariateNormalDiagHead(
num_dimensions,
init_scale=policy_init_std,
min_scale=1e-10)
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
critic_network = networks.CriticMultiplexer(
critic_network=networks.LayerNormMLP(critic_layer_sizes),
action_network=networks.ClipToSpec(action_spec))
if obs_network is None:
obs_network = tf_utils.batch_concat
return {
'policy': policy_network,
'critic': critic_network,
'observation': obs_network,
}
def make_d4pg_networks(
action_spec,
policy_layer_sizes=(256, 256, 256),
critic_layer_sizes=(512, 512, 256),
vmin=-150.,
vmax=150.,
num_atoms=201):
"""Creates networks used by the d4pg agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_layer_sizes = list(policy_layer_sizes) + [int(num_dimensions)]
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes),
networks.TanhToSpec(action_spec)
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
critic_network = snt.Sequential([
networks.CriticMultiplexer(
critic_network=networks.LayerNormMLP(
critic_layer_sizes, activate_final=True)),
networks.DiscreteValuedHead(vmin, vmax, num_atoms)
])
return {
'policy': policy_network,
'critic': critic_network,
'observation': tf2_utils.batch_concat,
}
def make_dmpo_networks(
action_spec,
policy_layer_sizes = (300, 200),
critic_layer_sizes = (400, 300),
vmin = -150.,
vmax = 150.,
num_atoms = 51,
):
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes),
networks.MultivariateNormalDiagHead(num_dimensions)
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
critic_network = networks.CriticMultiplexer(
critic_network=networks.LayerNormMLP(critic_layer_sizes),
action_network=networks.ClipToSpec(action_spec))
critic_network = snt.Sequential(
[critic_network,
networks.DiscreteValuedHead(vmin, vmax, num_atoms)])
return {
'policy': policy_network,
'critic': critic_network,
'observation': tf_utils.batch_concat,
}
def make_networks(
action_spec: types.NestedSpec,
policy_layer_sizes: Sequence[int] = (10, 10),
critic_layer_sizes: Sequence[int] = (10, 10),
) -> Dict[str, snt.Module]:
"""Creates networks used by the agent."""
# Get total number of action dimensions from action spec.
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_network = snt.Sequential([
tf2_utils.batch_concat,
networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
networks.MultivariateNormalDiagHead(
num_dimensions,
tanh_mean=True,
min_scale=0.3,
init_scale=0.7,
fixed_scale=False,
use_tfd_independent=False)
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
multiplexer = networks.CriticMultiplexer()
critic_network = snt.Sequential([
multiplexer,
networks.LayerNormMLP(critic_layer_sizes, activate_final=True),
networks.NearZeroInitializedLinear(1),
])
return {
'policy': policy_network,
'critic': critic_network,
}
def make_networks(
action_spec: specs.BoundedArray,
policy_layer_sizes: Sequence[int] = (256, 256, 256),
critic_layer_sizes: Sequence[int] = (512, 512, 256),
vmin: float = -150.,
vmax: float = 150.,
num_atoms: int = 51,
) -> Dict[str, Union[snt.Module, Callable[[tf.Tensor], tf.Tensor]]]:
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
networks.NearZeroInitializedLinear(num_dimensions),
networks.TanhToSpec(action_spec)
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
critic_network = snt.Sequential([
networks.CriticMultiplexer(),
networks.LayerNormMLP(critic_layer_sizes, activate_final=True),
networks.DiscreteValuedHead(vmin, vmax, num_atoms),
])
return {
'policy': policy_network,
'critic': critic_network,
'observation': tf2_utils.batch_concat,
}
def make_default_networks(
action_spec: specs.BoundedArray,
policy_layer_sizes: Sequence[int] = (256, 256, 256),
critic_layer_sizes: Sequence[int] = (512, 512, 256),
) -> Mapping[str, types.TensorTransformation]:
"""Creates networks used by the agent."""
# Get total number of action dimensions from action spec.
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_network = snt.Sequential([
tf2_utils.batch_concat,
networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
networks.MultivariateNormalDiagHead(
num_dimensions,
tanh_mean=True,
min_scale=0.3,
init_scale=0.7,
fixed_scale=False,
use_tfd_independent=False)
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
multiplexer = networks.CriticMultiplexer(
action_network=networks.ClipToSpec(action_spec))
critic_network = snt.Sequential([
multiplexer,
networks.LayerNormMLP(critic_layer_sizes, activate_final=True),
networks.NearZeroInitializedLinear(1),
])
return {
"policy": policy_network,
"critic": critic_network,
}
def make_networks(
action_spec: specs.BoundedArray,
policy_layer_sizes: Sequence[int] = (50, ),
critic_layer_sizes: Sequence[int] = (50, ),
vmin: float = -150.,
vmax: float = 150.,
num_atoms: int = 51,
):
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
networks.MultivariateNormalDiagHead(num_dimensions,
tanh_mean=True,
init_scale=0.3,
fixed_scale=True,
use_tfd_independent=False)
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
critic_network = networks.CriticMultiplexer(
critic_network=networks.LayerNormMLP(critic_layer_sizes,
activate_final=True),
action_network=networks.ClipToSpec(action_spec))
critic_network = snt.Sequential(
[critic_network,
networks.DiscreteValuedHead(vmin, vmax, num_atoms)])
return {
'policy': policy_network,
'critic': critic_network,
'observation': tf2_utils.batch_concat,
}
def make_networks(action_spec: specs.BoundedArray):
"""Creates simple networks for testing.."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
# Create the observation network shared between the policy and critic.
observation_network = tf2_utils.batch_concat
# Create the policy network (head) and the evaluation network.
policy_network = snt.Sequential([
networks.LayerNormMLP([50], activate_final=True),
networks.NearZeroInitializedLinear(num_dimensions),
networks.TanhToSpec(action_spec)
])
evaluator_network = snt.Sequential([observation_network, policy_network])
# Create the critic network.
critic_network = snt.Sequential([
# The multiplexer concatenates the observations/actions.
networks.CriticMultiplexer(),
networks.LayerNormMLP([50], activate_final=True),
networks.NearZeroInitializedLinear(1),
])
return {
'policy': policy_network,
'critic': critic_network,
'observation': observation_network,
'evaluator': evaluator_network,
}
def make_networks(
environment_spec: specs.EnvironmentSpec,
policy_layer_sizes: Sequence[int] = (256, 256),
critic_layer_sizes: Sequence[int] = (256, 256),
) -> Mapping[str, types.TensorTransformation]:
"""Creates the networks used by the agent."""
# Get total number of action dimensions from action spec.
num_dimensions = np.prod(environment_spec.actions.shape, dtype=int)
# Create the shared observation network; here simply a state-less operation.
observation_network = tf.identity
# Create the policy network.
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
sac.model.SquashedGaussianValueHead(num_dimensions),
])
# Create the critic network.
critic_network = snt.Sequential([
# The multiplexer concatenates the observations/actions.
networks.CriticMultiplexer(),
networks.LayerNormMLP(critic_layer_sizes, activate_final=True),
networks.NearZeroInitializedLinear(1),
])
return {
'policy': policy_network,
'critic': critic_network,
'observation': observation_network,
}
def make_networks(
action_spec: specs.Array,
policy_layer_sizes: Sequence[int] = (300, 200),
critic_layer_sizes: Sequence[int] = (400, 300),
) -> Dict[str, snt.Module]:
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
critic_layer_sizes = list(critic_layer_sizes)
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes),
networks.MultivariateNormalDiagHead(num_dimensions),
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
critic_network = snt.Sequential([
networks.CriticMultiplexer(
critic_network=networks.LayerNormMLP(critic_layer_sizes)),
networks.DiscreteValuedHead(0., 1., 10),
])
return {
'policy': policy_network,
'critic': critic_network,
}
def make_networks(
action_spec: specs.BoundedArray,
policy_layer_sizes: Sequence[int] = (256, 256, 256),
critic_layer_sizes: Sequence[int] = (512, 512, 256),
) -> Dict[str, types.TensorTransformation]:
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
networks.MultivariateNormalDiagHead(num_dimensions,
init_scale=0.7,
use_tfd_independent=True)
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
multiplexer = networks.CriticMultiplexer(
action_network=networks.ClipToSpec(action_spec))
critic_network = snt.Sequential([
multiplexer,
networks.LayerNormMLP(critic_layer_sizes, activate_final=True),
networks.NearZeroInitializedLinear(1),
])
return {
'policy': policy_network,
'critic': critic_network,
'observation': tf2_utils.batch_concat,
}
def make_networks(
action_spec: types.NestedSpec,
policy_layer_sizes: Sequence[int] = (10, 10),
critic_layer_sizes: Sequence[int] = (10, 10),
vmin: float = -150.,
vmax: float = 150.,
num_atoms: int = 51,
) -> Dict[str, snt.Module]:
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_layer_sizes = list(policy_layer_sizes) + [num_dimensions]
policy_network = snt.Sequential(
[networks.LayerNormMLP(policy_layer_sizes), tf.tanh])
critic_network = snt.Sequential([
networks.CriticMultiplexer(critic_network=networks.LayerNormMLP(
critic_layer_sizes, activate_final=True)),
networks.DiscreteValuedHead(vmin, vmax, num_atoms)
])
return {
'policy': policy_network,
'critic': critic_network,
}
def make_networks(
action_spec: specs.BoundedArray,
policy_layer_sizes: Sequence[int] = (256, 256, 256),
critic_layer_sizes: Sequence[int] = (512, 512, 256),
) -> Dict[str, types.TensorTransformation]:
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
networks.NearZeroInitializedLinear(num_dimensions),
networks.TanhToSpec(action_spec)
])
critic_network = snt.Sequential([
# The multiplexer concatenates the observations/actions.
networks.CriticMultiplexer(),
networks.LayerNormMLP(critic_layer_sizes, activate_final=True),
networks.NearZeroInitializedLinear(1),
])
return {
'policy': policy_network,
'critic': critic_network,
'observation': tf2_utils.batch_concat,
}
def make_networks(
action_spec: specs.BoundedArray,
num_critic_heads: int,
policy_layer_sizes: Sequence[int] = (50, ),
critic_layer_sizes: Sequence[int] = (50, ),
num_layers_shared: int = 1,
distributional_critic: bool = True,
vmin: float = -150.,
vmax: float = 150.,
num_atoms: int = 51,
):
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
networks.MultivariateNormalDiagHead(num_dimensions,
tanh_mean=False,
init_scale=0.69)
])
if not distributional_critic:
critic_layer_sizes = list(critic_layer_sizes) + [1]
if not num_layers_shared:
# No layers are shared
critic_network_base = None
else:
critic_network_base = networks.LayerNormMLP(
critic_layer_sizes[:num_layers_shared], activate_final=True)
critic_network_heads = [
snt.nets.MLP(critic_layer_sizes,
activation=tf.nn.elu,
activate_final=False) for _ in range(num_critic_heads)
]
if distributional_critic:
critic_network_heads = [
snt.Sequential(
[c, networks.DiscreteValuedHead(vmin, vmax, num_atoms)])
for c in critic_network_heads
]
# The multiplexer concatenates the (maybe transformed) observations/actions.
critic_network = snt.Sequential([
networks.CriticMultiplexer(
critic_network=critic_network_base,
action_network=networks.ClipToSpec(action_spec)),
networks.Multihead(network_heads=critic_network_heads),
])
return {
'policy': policy_network,
'critic': critic_network,
'observation': tf2_utils.batch_concat,
}
def make_lstm_mpo_agent(env_spec: specs.EnvironmentSpec, logger: Logger,
hyperparams: Dict, checkpoint_path: str):
params = DEFAULT_PARAMS.copy()
params.update(hyperparams)
action_size = np.prod(env_spec.actions.shape, dtype=int).item()
policy_network = snt.Sequential([
networks.LayerNormMLP(
layer_sizes=[*params.pop('policy_layers'), action_size]),
networks.MultivariateNormalDiagHead(num_dimensions=action_size)
])
critic_network = snt.Sequential([
networks.CriticMultiplexer(critic_network=networks.LayerNormMLP(
layer_sizes=[*params.pop('critic_layers'), 1]))
])
observation_network = networks.DeepRNN([
networks.LayerNormMLP(layer_sizes=params.pop('observation_layers')),
networks.LSTM(hidden_size=200)
])
loss_param_keys = list(
filter(lambda key: key.startswith('loss_'), params.keys()))
loss_params = dict([(k.replace('loss_', ''), params.pop(k))
for k in loss_param_keys])
policy_loss_module = losses.MPO(**loss_params)
# Create a replay server to add data to.
# Make sure observation network is a Sonnet Module.
observation_network = tf2_utils.to_sonnet_module(observation_network)
# Create optimizers.
policy_optimizer = Adam(params.pop('policy_lr'))
critic_optimizer = Adam(params.pop('critic_lr'))
actor = RecurrentActor(
networks.DeepRNN([
observation_network, policy_network,
networks.StochasticModeHead()
]))
# The learner updates the parameters (and initializes them).
return RecurrentMPO(environment_spec=env_spec,
policy_network=policy_network,
critic_network=critic_network,
observation_network=observation_network,
policy_loss_module=policy_loss_module,
policy_optimizer=policy_optimizer,
critic_optimizer=critic_optimizer,
logger=logger,
checkpoint_path=checkpoint_path,
**params), actor
def make_network_with_prior(
action_spec: specs.BoundedArray,
policy_layer_sizes: Sequence[int] = (200, 100),
critic_layer_sizes: Sequence[int] = (400, 300),
prior_layer_sizes: Sequence[int] = (200, 100),
policy_keys: Optional[Sequence[str]] = None,
prior_keys: Optional[Sequence[str]] = None,
) -> Mapping[str, types.TensorTransformation]:
"""Creates networks used by the agent."""
# Get total number of action dimensions from action spec.
num_dimensions = np.prod(action_spec.shape, dtype=int)
flatten_concat_policy = functools.partial(
svg0_utils.batch_concat_selection, concat_keys=policy_keys)
flatten_concat_prior = functools.partial(
svg0_utils.batch_concat_selection, concat_keys=prior_keys)
policy_network = snt.Sequential([
flatten_concat_policy,
networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
networks.MultivariateNormalDiagHead(
num_dimensions,
tanh_mean=True,
min_scale=0.1,
init_scale=0.7,
fixed_scale=False,
use_tfd_independent=False)
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
multiplexer = networks.CriticMultiplexer(
observation_network=flatten_concat_policy,
action_network=networks.ClipToSpec(action_spec))
critic_network = snt.Sequential([
multiplexer,
networks.LayerNormMLP(critic_layer_sizes, activate_final=True),
networks.NearZeroInitializedLinear(1),
])
prior_network = snt.Sequential([
flatten_concat_prior,
networks.LayerNormMLP(prior_layer_sizes, activate_final=True),
networks.MultivariateNormalDiagHead(
num_dimensions,
tanh_mean=True,
min_scale=0.1,
init_scale=0.7,
fixed_scale=False,
use_tfd_independent=False)
])
return {
"policy": policy_network,
"critic": critic_network,
"prior": prior_network,
}
def make_default_networks(
environment_spec: specs.EnvironmentSpec,
*,
policy_layer_sizes: Sequence[int] = (256, 256, 256),
critic_layer_sizes: Sequence[int] = (512, 512, 256),
policy_init_scale: float = 0.7,
critic_init_scale: float = 1e-3,
critic_num_components: int = 5,
) -> Mapping[str, snt.Module]:
"""Creates networks used by the agent."""
# Unpack the environment spec to get appropriate shapes, dtypes, etc.
act_spec = environment_spec.actions
obs_spec = environment_spec.observations
num_dimensions = np.prod(act_spec.shape, dtype=int)
# Create the observation network and make sure it's a Sonnet module.
observation_network = tf2_utils.batch_concat
observation_network = tf2_utils.to_sonnet_module(observation_network)
# Create the policy network.
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
networks.MultivariateNormalDiagHead(num_dimensions,
init_scale=policy_init_scale,
use_tfd_independent=True)
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
critic_network = snt.Sequential([
networks.CriticMultiplexer(
action_network=networks.ClipToSpec(act_spec)),
networks.LayerNormMLP(critic_layer_sizes, activate_final=True),
networks.GaussianMixtureHead(num_dimensions=1,
num_components=critic_num_components,
init_scale=critic_init_scale)
])
# Create network variables.
# Get embedding spec by creating observation network variables.
emb_spec = tf2_utils.create_variables(observation_network, [obs_spec])
tf2_utils.create_variables(policy_network, [emb_spec])
tf2_utils.create_variables(critic_network, [emb_spec, act_spec])
return {
'policy': policy_network,
'critic': critic_network,
'observation': observation_network,
}
def make_bc_network(
action_spec,
policy_layer_sizes=(256, 256, 256),
policy_init_std=1e-9,
binary_grip_action=False):
"""Residual BC network in Sonnet, equivalent to residual policy network."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
if policy_layer_sizes:
policy_network = snt.Sequential([
tf_utils.batch_concat,
networks.LayerNormMLP([int(l) for l in policy_layer_sizes]),
networks.MultivariateNormalDiagHead(
num_dimensions,
init_scale=policy_init_std,
min_scale=1e-10)
])
else:
policy_network = snt.Sequential([
tf_utils.batch_concat,
ArmPolicyNormalDiagHead(
binary_grip_action=binary_grip_action,
num_dimensions=num_dimensions,
init_scale=policy_init_std,
min_scale=1e-10)
])
return {
# 'observation': tf_utils.batch_concat,
'policy': policy_network,
}
def __init__(self, layer_sizes: Sequence[int]):
super(InstrumentalFeature, self).__init__()
self._net = snt.Sequential([
networks.CriticMultiplexer(),
networks.LayerNormMLP(layer_sizes, activate_final=True)
])
self._feature_dim = layer_sizes[-1] + 1
def __init__(self, layer_sizes: Sequence[int]):
super(TerminatePredictor, self).__init__()
self._net = snt.Sequential([
networks.CriticMultiplexer(),
networks.LayerNormMLP(layer_sizes + [1], activate_final=False),
tf.sigmoid
])
def test_snapshot_distribution(self):
"""Test that snapshotter correctly calls saves/restores snapshots."""
# Create a test network.
net1 = snt.Sequential([
networks.LayerNormMLP([10, 10]),
networks.MultivariateNormalDiagHead(1)
])
spec = specs.Array([10], dtype=np.float32)
tf2_utils.create_variables(net1, [spec])
# Save the test network.
directory = self.get_tempdir()
objects_to_save = {'net': net1}
snapshotter = tf2_savers.Snapshotter(objects_to_save,
directory=directory)
snapshotter.save()
# Reload the test network.
net2 = tf.saved_model.load(os.path.join(snapshotter.directory, 'net'))
inputs = tf2_utils.add_batch_dim(tf2_utils.zeros_like(spec))
with tf.GradientTape() as tape:
dist1 = net1(inputs)
loss1 = tf.math.reduce_sum(dist1.mean() + dist1.variance())
grads1 = tape.gradient(loss1, net1.trainable_variables)
with tf.GradientTape() as tape:
dist2 = net2(inputs)
loss2 = tf.math.reduce_sum(dist2.mean() + dist2.variance())
grads2 = tape.gradient(loss2, net2.trainable_variables)
assert all(tree.map_structure(np.allclose, list(grads1), list(grads2)))
def make_d4pg_agent(env_spec: specs.EnvironmentSpec, logger: Logger, checkpoint_path: str, hyperparams: Dict):
params = DEFAULT_PARAMS.copy()
params.update(hyperparams)
action_size = np.prod(env_spec.actions.shape, dtype=int).item()
policy_network = snt.Sequential([
networks.LayerNormMLP(layer_sizes=[*params.pop('policy_layers'), action_size]),
networks.NearZeroInitializedLinear(output_size=action_size),
networks.TanhToSpec(env_spec.actions),
])
critic_network = snt.Sequential([
networks.CriticMultiplexer(
critic_network=networks.LayerNormMLP(layer_sizes=[*params.pop('critic_layers'), 1])
),
networks.DiscreteValuedHead(vmin=-100.0, vmax=100.0, num_atoms=params.pop('atoms'))
])
observation_network = tf.identity
# Make sure observation network is a Sonnet Module.
observation_network = tf2_utils.to_sonnet_module(observation_network)
actor = FeedForwardActor(policy_network=snt.Sequential([
observation_network,
policy_network
]))
# Create optimizers.
policy_optimizer = Adam(params.pop('policy_lr'))
critic_optimizer = Adam(params.pop('critic_lr'))
# The learner updates the parameters (and initializes them).
agent = D4PG(
environment_spec=env_spec,
policy_network=policy_network,
critic_network=critic_network,
observation_network=observation_network,
policy_optimizer=policy_optimizer,
critic_optimizer=critic_optimizer,
logger=logger,
checkpoint_path=checkpoint_path,
**params
)
agent.__setattr__('eval_actor', actor)
return agent
def make_value_func_dm_control(layer_sizes: str = '512,512,256') -> snt.Module:
layer_sizes = list(map(int, layer_sizes.split(',')))
value_function = snt.Sequential([
networks.CriticMultiplexer(),
networks.LayerNormMLP(layer_sizes, activate_final=True),
snt.Linear(1)
])
return value_function
def make_dmpo_networks(
action_spec,
policy_layer_sizes=(256, 256, 256),
critic_layer_sizes=(512, 512, 256),
vmin=-150.,
vmax=150.,
num_atoms=51,
policy_init_std=1e-9,
obs_network=None,
binary_grip_action=False):
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
if policy_layer_sizes:
policy_network = snt.Sequential([
networks.LayerNormMLP([int(l) for l in policy_layer_sizes]),
networks.MultivariateNormalDiagHead(
num_dimensions,
init_scale=policy_init_std,
min_scale=1e-10)
])
else:
# Useful when initializing from a trained BC network.
policy_network = snt.Sequential([
ArmPolicyNormalDiagHead(
binary_grip_action=binary_grip_action,
num_dimensions=num_dimensions,
init_scale=policy_init_std,
min_scale=1e-10)
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
critic_network = networks.CriticMultiplexer(
critic_network=networks.LayerNormMLP(critic_layer_sizes),
action_network=networks.ClipToSpec(action_spec))
critic_network = snt.Sequential(
[critic_network,
networks.DiscreteValuedHead(vmin, vmax, num_atoms)])
if obs_network is None:
obs_network = tf_utils.batch_concat
return {
'policy': policy_network,
'critic': critic_network,
'observation': obs_network,
}
def make_feed_forward_networks(
action_spec: specs.BoundedArray,
z_spec: specs.BoundedArray,
policy_layer_sizes: Tuple[int, ...] = (256, 256),
critic_layer_sizes: Tuple[int, ...] = (256, 256),
discriminator_layer_sizes: Tuple[int, ...] = (256, 256),
hierarchical_controller_layer_sizes: Tuple[int, ...] = (256, 256),
vmin: float = -150., # Minimum value for the Critic distribution.
vmax: float = 150., # Maximum value for the Critic distribution.
num_atoms: int = 51, # Number of atoms for the discrete value distribution.
) -> Dict[str, types.TensorTransformation]:
num_dimensions = np.prod(action_spec.shape, dtype=int)
z_dim = np.prod(z_spec.shape, dtype=int)
observation_network = tf2_utils.batch_concat
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes),
networks.MultivariateNormalDiagHead(num_dimensions)
])
critic_multiplexer = networks.CriticMultiplexer(
critic_network=networks.LayerNormMLP(critic_layer_sizes),
action_network=networks.ClipToSpec(action_spec))
critic_network = snt.Sequential([
critic_multiplexer,
networks.DiscreteValuedHead(vmin, vmax, num_atoms),
])
# The discriminator in DIAYN uses the same architecture as the critic.
discriminator_network = networks.LayerNormMLP(discriminator_layer_sizes +
(z_dim, ))
hierarchical_controller_network = networks.LayerNormMLP(
hierarchical_controller_layer_sizes + (z_dim, ))
return {
'policy': policy_network,
'critic': critic_network,
'observation': observation_network,
'discriminator': discriminator_network,
'hierarchical_controller': hierarchical_controller_network,
}
def make_value_func_dm_control(
value_layer_sizes: str = '512,512,256',
adversarial_layer_sizes: str = '512,512,256',
) -> Tuple[snt.Module, snt.Module]:
layer_sizes = list(map(int, value_layer_sizes.split(',')))
value_function = snt.Sequential([
networks.CriticMultiplexer(),
networks.LayerNormMLP(layer_sizes, activate_final=True),
snt.Linear(1)
])
layer_sizes = list(map(int, adversarial_layer_sizes.split(',')))
advsarial_function = snt.Sequential([
networks.CriticMultiplexer(),
networks.LayerNormMLP(layer_sizes, activate_final=True),
snt.Linear(1)
])
return value_function, advsarial_function
def make_networks(
action_spec: specs.BoundedArray,
policy_layer_sizes: Sequence[int] = (50, 1024, 1024),
critic_layer_sizes: Sequence[int] = (50, 1024, 1024),
vmin: float = -150.,
vmax: float = 150.,
num_atoms: int = 51,
) -> Dict[str, snt.Module]:
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes,
w_init=snt.initializers.Orthogonal(),
activation=tf.nn.relu,
activate_final=True),
networks.MultivariateNormalDiagHead(
num_dimensions,
tanh_mean=False,
init_scale=1.0,
fixed_scale=False,
use_tfd_independent=True,
w_init=snt.initializers.Orthogonal())
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
critic_network = networks.CriticMultiplexer(
observation_network=snt.Sequential([
snt.Linear(critic_layer_sizes[0],
w_init=snt.initializers.Orthogonal()),
snt.LayerNorm(axis=slice(1, None),
create_scale=True,
create_offset=True), tf.nn.tanh
]),
critic_network=snt.nets.MLP(critic_layer_sizes[1:],
w_init=snt.initializers.Orthogonal(),
activation=tf.nn.relu,
activate_final=True),
action_network=networks.ClipToSpec(action_spec))
critic_network = snt.Sequential([
critic_network,
networks.DiscreteValuedHead(vmin,
vmax,
num_atoms,
w_init=snt.initializers.Orthogonal())
])
observation_network = networks.DrQTorso()
return {
'policy': policy_network,
'critic': critic_network,
'observation': observation_network,
}
def make_networks(
action_spec: types.NestedSpec,
policy_layer_sizes: Sequence[int] = (10, 10),
critic_layer_sizes: Sequence[int] = (10, 10),
) -> Dict[str, snt.Module]:
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_layer_sizes = list(policy_layer_sizes) + [num_dimensions]
critic_layer_sizes = list(critic_layer_sizes) + [1]
policy_network = snt.Sequential(
[networks.LayerNormMLP(policy_layer_sizes), tf.tanh])
# The multiplexer concatenates the (maybe transformed) observations/actions.
critic_network = networks.CriticMultiplexer(
critic_network=networks.LayerNormMLP(critic_layer_sizes))
return {
'policy': policy_network,
'critic': critic_network,
}
def make_networks(
action_spec,
policy_layer_sizes=(10, 10),
critic_layer_sizes=(10, 10),
):
"""Creates networks used by the agent."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
critic_layer_sizes = list(critic_layer_sizes) + [1]
policy_network = snt.Sequential([
networks.LayerNormMLP(policy_layer_sizes),
networks.MultivariateNormalDiagHead(num_dimensions)
])
critic_network = networks.CriticMultiplexer(
critic_network=networks.LayerNormMLP(critic_layer_sizes))
return {
'policy': policy_network,
'critic': critic_network,
}
def make_networks(action_spec: specs.BoundedArray):
"""Simple networks for testing.."""
num_dimensions = np.prod(action_spec.shape, dtype=int)
policy_network = snt.Sequential([
networks.LayerNormMLP([50], activate_final=True),
networks.NearZeroInitializedLinear(num_dimensions),
networks.TanhToSpec(action_spec)
])
# The multiplexer concatenates the (maybe transformed) observations/actions.
critic_network = snt.Sequential([
networks.CriticMultiplexer(
critic_network=networks.LayerNormMLP([50], activate_final=True)),
networks.DiscreteValuedHead(-1., 1., 10)
])
return {
'policy': policy_network,
'critic': critic_network,
'observation': tf2_utils.batch_concat,
}
