class RNDNetwork(torch.nn.Module):
EPSILON = 1e-10
def __init__(self, specs: BehaviorSpec, settings: RNDSettings) -> None:
super().__init__()
state_encoder_settings = NetworkSettings(
normalize=True,
hidden_units=settings.encoding_size,
num_layers=3,
vis_encode_type=EncoderType.SIMPLE,
memory=None,
)
self._encoder = NetworkBody(specs.observation_shapes,
state_encoder_settings)
def forward(self, mini_batch: AgentBuffer) -> torch.Tensor:
n_vis = len(self._encoder.visual_processors)
hidden, _ = self._encoder.forward(
vec_inputs=[
ModelUtils.list_to_tensor(mini_batch["vector_obs"],
dtype=torch.float)
],
vis_inputs=[
ModelUtils.list_to_tensor(mini_batch["visual_obs%d" % i],
dtype=torch.float)
for i in range(n_vis)
],
)
self._encoder.update_normalization(
torch.tensor(mini_batch["vector_obs"]))
return hidden
class RNDNetwork(torch.nn.Module):
EPSILON = 1e-10
def __init__(self, specs: BehaviorSpec, settings: RNDSettings) -> None:
super().__init__()
state_encoder_settings = NetworkSettings(
normalize=True,
hidden_units=settings.encoding_size,
num_layers=3,
vis_encode_type=EncoderType.SIMPLE,
memory=None,
)
self._encoder = NetworkBody(specs.observation_specs, state_encoder_settings)
def forward(self, mini_batch: AgentBuffer) -> torch.Tensor:
n_obs = len(self._encoder.processors)
np_obs = ObsUtil.from_buffer(mini_batch, n_obs)
# Convert to tensors
tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
hidden, _ = self._encoder.forward(tensor_obs)
self._encoder.update_normalization(mini_batch)
return hidden
class RNDNetwork(torch.nn.Module):
EPSILON = 1e-10
def __init__(self, specs: BehaviorSpec, settings: RNDSettings) -> None:
super().__init__()
state_encoder_settings = settings.network_settings
if state_encoder_settings.memory is not None:
state_encoder_settings.memory = None
logger.warning(
"memory was specified in network_settings but is not supported by RND. It is being ignored."
)
self._encoder = NetworkBody(specs.observation_specs,
state_encoder_settings)
def forward(self, mini_batch: AgentBuffer) -> torch.Tensor:
n_obs = len(self._encoder.processors)
np_obs = ObsUtil.from_buffer(mini_batch, n_obs)
# Convert to tensors
tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
hidden, _ = self._encoder.forward(tensor_obs)
self._encoder.update_normalization(mini_batch)
return hidden
class CuriosityNetwork(torch.nn.Module):
EPSILON = 1e-10
def __init__(self, specs: BehaviorSpec,
settings: CuriositySettings) -> None:
super().__init__()
self._policy_specs = specs
state_encoder_settings = NetworkSettings(
normalize=False,
hidden_units=settings.encoding_size,
num_layers=2,
vis_encode_type=EncoderType.SIMPLE,
memory=None,
)
self._state_encoder = NetworkBody(specs.observation_shapes,
state_encoder_settings)
self._action_flattener = ModelUtils.ActionFlattener(specs)
self.inverse_model_action_prediction = torch.nn.Sequential(
LinearEncoder(2 * settings.encoding_size, 1, 256),
linear_layer(256, self._action_flattener.flattened_size),
)
self.forward_model_next_state_prediction = torch.nn.Sequential(
LinearEncoder(
settings.encoding_size + self._action_flattener.flattened_size,
1, 256),
linear_layer(256, settings.encoding_size),
)
def get_current_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Extracts the current state embedding from a mini_batch.
"""
n_vis = len(self._state_encoder.visual_processors)
hidden, _ = self._state_encoder.forward(
vec_inputs=[
ModelUtils.list_to_tensor(mini_batch["vector_obs"],
dtype=torch.float)
],
vis_inputs=[
ModelUtils.list_to_tensor(mini_batch["visual_obs%d" % i],
dtype=torch.float)
for i in range(n_vis)
],
)
return hidden
def get_next_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Extracts the next state embedding from a mini_batch.
"""
n_vis = len(self._state_encoder.visual_processors)
hidden, _ = self._state_encoder.forward(
vec_inputs=[
ModelUtils.list_to_tensor(mini_batch["next_vector_in"],
dtype=torch.float)
],
vis_inputs=[
ModelUtils.list_to_tensor(mini_batch["next_visual_obs%d" % i],
dtype=torch.float)
for i in range(n_vis)
],
)
return hidden
def predict_action(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
In the continuous case, returns the predicted action.
In the discrete case, returns the logits.
"""
inverse_model_input = torch.cat((self.get_current_state(mini_batch),
self.get_next_state(mini_batch)),
dim=1)
hidden = self.inverse_model_action_prediction(inverse_model_input)
if self._policy_specs.is_action_continuous():
return hidden
else:
branches = ModelUtils.break_into_branches(
hidden, self._policy_specs.discrete_action_branches)
branches = [torch.softmax(b, dim=1) for b in branches]
return torch.cat(branches, dim=1)
def predict_next_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Uses the current state embedding and the action of the mini_batch to predict
the next state embedding.
"""
if self._policy_specs.is_action_continuous():
action = ModelUtils.list_to_tensor(mini_batch["actions"],
dtype=torch.float)
else:
action = torch.cat(
ModelUtils.actions_to_onehot(
ModelUtils.list_to_tensor(mini_batch["actions"],
dtype=torch.long),
self._policy_specs.discrete_action_branches,
),
dim=1,
)
forward_model_input = torch.cat(
(self.get_current_state(mini_batch), action), dim=1)
return self.forward_model_next_state_prediction(forward_model_input)
def compute_inverse_loss(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Computes the inverse loss for a mini_batch. Corresponds to the error on the
action prediction (given the current and next state).
"""
predicted_action = self.predict_action(mini_batch)
if self._policy_specs.is_action_continuous():
sq_difference = (ModelUtils.list_to_tensor(mini_batch["actions"],
dtype=torch.float) -
predicted_action)**2
sq_difference = torch.sum(sq_difference, dim=1)
return torch.mean(
ModelUtils.dynamic_partition(
sq_difference,
ModelUtils.list_to_tensor(mini_batch["masks"],
dtype=torch.float),
2,
)[1])
else:
true_action = torch.cat(
ModelUtils.actions_to_onehot(
ModelUtils.list_to_tensor(mini_batch["actions"],
dtype=torch.long),
self._policy_specs.discrete_action_branches,
),
dim=1,
)
cross_entropy = torch.sum(
-torch.log(predicted_action + self.EPSILON) * true_action,
dim=1)
return torch.mean(
ModelUtils.dynamic_partition(
cross_entropy,
ModelUtils.list_to_tensor(
mini_batch["masks"],
dtype=torch.float), # use masks not action_masks
2,
)[1])
def compute_reward(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Calculates the curiosity reward for the mini_batch. Corresponds to the error
between the predicted and actual next state.
"""
predicted_next_state = self.predict_next_state(mini_batch)
target = self.get_next_state(mini_batch)
sq_difference = 0.5 * (target - predicted_next_state)**2
sq_difference = torch.sum(sq_difference, dim=1)
return sq_difference
def compute_forward_loss(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Computes the loss for the next state prediction
"""
return torch.mean(
ModelUtils.dynamic_partition(
self.compute_reward(mini_batch),
ModelUtils.list_to_tensor(mini_batch["masks"],
dtype=torch.float),
2,
)[1])
class DiscriminatorNetwork(torch.nn.Module):
gradient_penalty_weight = 10.0
z_size = 128
alpha = 0.0005
mutual_information = 0.5
EPSILON = 1e-7
initial_beta = 0.0
def __init__(self, specs: BehaviorSpec, settings: GAILSettings) -> None:
super().__init__()
self._policy_specs = specs
self._use_vail = settings.use_vail
self._settings = settings
state_encoder_settings = NetworkSettings(
normalize=False,
hidden_units=settings.encoding_size,
num_layers=2,
vis_encode_type=EncoderType.SIMPLE,
memory=None,
)
self._state_encoder = NetworkBody(specs.observation_shapes,
state_encoder_settings)
self._action_flattener = ModelUtils.ActionFlattener(specs)
encoder_input_size = settings.encoding_size
if settings.use_actions:
encoder_input_size += (self._action_flattener.flattened_size + 1
) # + 1 is for done
self.encoder = torch.nn.Sequential(
linear_layer(encoder_input_size, settings.encoding_size),
Swish(),
linear_layer(settings.encoding_size, settings.encoding_size),
Swish(),
)
estimator_input_size = settings.encoding_size
if settings.use_vail:
estimator_input_size = self.z_size
self._z_sigma = torch.nn.Parameter(torch.ones((self.z_size),
dtype=torch.float),
requires_grad=True)
self._z_mu_layer = linear_layer(
settings.encoding_size,
self.z_size,
kernel_init=Initialization.KaimingHeNormal,
kernel_gain=0.1,
)
self._beta = torch.nn.Parameter(torch.tensor(self.initial_beta,
dtype=torch.float),
requires_grad=False)
self._estimator = torch.nn.Sequential(
linear_layer(estimator_input_size, 1), torch.nn.Sigmoid())
def get_action_input(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Creates the action Tensor. In continuous case, corresponds to the action. In
the discrete case, corresponds to the concatenation of one hot action Tensors.
"""
return self._action_flattener.forward(
torch.as_tensor(mini_batch["actions"], dtype=torch.float))
def get_state_encoding(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Creates the observation input.
"""
n_vis = len(self._state_encoder.visual_encoders)
hidden, _ = self._state_encoder.forward(
vec_inputs=[
torch.as_tensor(mini_batch["vector_obs"], dtype=torch.float)
],
vis_inputs=[
torch.as_tensor(mini_batch["visual_obs%d" % i],
dtype=torch.float) for i in range(n_vis)
],
)
return hidden
def compute_estimate(self,
mini_batch: AgentBuffer,
use_vail_noise: bool = False) -> torch.Tensor:
"""
Given a mini_batch, computes the estimate (How much the discriminator believes
the data was sampled from the demonstration data).
:param mini_batch: The AgentBuffer of data
:param use_vail_noise: Only when using VAIL : If true, will sample the code, if
false, will return the mean of the code.
"""
encoder_input = self.get_state_encoding(mini_batch)
if self._settings.use_actions:
actions = self.get_action_input(mini_batch)
dones = torch.as_tensor(mini_batch["done"], dtype=torch.float)
encoder_input = torch.cat([encoder_input, actions, dones], dim=1)
hidden = self.encoder(encoder_input)
z_mu: Optional[torch.Tensor] = None
if self._settings.use_vail:
z_mu = self._z_mu_layer(hidden)
hidden = torch.normal(z_mu, self._z_sigma * use_vail_noise)
estimate = self._estimator(hidden)
return estimate, z_mu
def compute_loss(self, policy_batch: AgentBuffer,
expert_batch: AgentBuffer) -> torch.Tensor:
"""
Given a policy mini_batch and an expert mini_batch, computes the loss of the discriminator.
"""
total_loss = torch.zeros(1)
stats_dict: Dict[str, np.ndarray] = {}
policy_estimate, policy_mu = self.compute_estimate(policy_batch,
use_vail_noise=True)
expert_estimate, expert_mu = self.compute_estimate(expert_batch,
use_vail_noise=True)
stats_dict["Policy/GAIL Policy Estimate"] = (
policy_estimate.mean().detach().cpu().numpy())
stats_dict["Policy/GAIL Expert Estimate"] = (
expert_estimate.mean().detach().cpu().numpy())
discriminator_loss = -(
torch.log(expert_estimate + self.EPSILON) +
torch.log(1.0 - policy_estimate + self.EPSILON)).mean()
stats_dict["Losses/GAIL Loss"] = discriminator_loss.detach().cpu(
).numpy()
total_loss += discriminator_loss
if self._settings.use_vail:
# KL divergence loss (encourage latent representation to be normal)
kl_loss = torch.mean(-torch.sum(
1 + (self._z_sigma**2).log() - 0.5 * expert_mu**2 -
0.5 * policy_mu**2 - (self._z_sigma**2),
dim=1,
))
vail_loss = self._beta * (kl_loss - self.mutual_information)
with torch.no_grad():
self._beta.data = torch.max(
self._beta + self.alpha *
(kl_loss - self.mutual_information),
torch.tensor(0.0),
)
total_loss += vail_loss
stats_dict["Policy/GAIL Beta"] = self._beta.detach().cpu().numpy()
stats_dict["Losses/GAIL KL Loss"] = kl_loss.detach().cpu().numpy()
if self.gradient_penalty_weight > 0.0:
total_loss += (
self.gradient_penalty_weight *
self.compute_gradient_magnitude(policy_batch, expert_batch))
return total_loss, stats_dict
def compute_gradient_magnitude(self, policy_batch: AgentBuffer,
expert_batch: AgentBuffer) -> torch.Tensor:
"""
Gradient penalty from https://arxiv.org/pdf/1704.00028. Adds stability esp.
for off-policy. Compute gradients w.r.t randomly interpolated input.
"""
policy_obs = self.get_state_encoding(policy_batch)
expert_obs = self.get_state_encoding(expert_batch)
obs_epsilon = torch.rand(policy_obs.shape)
encoder_input = obs_epsilon * policy_obs + (1 -
obs_epsilon) * expert_obs
if self._settings.use_actions:
policy_action = self.get_action_input(policy_batch)
expert_action = self.get_action_input(policy_batch)
action_epsilon = torch.rand(policy_action.shape)
policy_dones = torch.as_tensor(policy_batch["done"],
dtype=torch.float)
expert_dones = torch.as_tensor(expert_batch["done"],
dtype=torch.float)
dones_epsilon = torch.rand(policy_dones.shape)
encoder_input = torch.cat(
[
encoder_input,
action_epsilon * policy_action +
(1 - action_epsilon) * expert_action,
dones_epsilon * policy_dones +
(1 - dones_epsilon) * expert_dones,
],
dim=1,
)
hidden = self.encoder(encoder_input)
if self._settings.use_vail:
use_vail_noise = True
z_mu = self._z_mu_layer(hidden)
hidden = torch.normal(z_mu, self._z_sigma * use_vail_noise)
hidden = self._estimator(hidden)
estimate = torch.mean(torch.sum(hidden, dim=1))
gradient = torch.autograd.grad(estimate, encoder_input)[0]
# Norm's gradient could be NaN at 0. Use our own safe_norm
safe_norm = (torch.sum(gradient**2, dim=1) + self.EPSILON).sqrt()
gradient_mag = torch.mean((safe_norm - 1)**2)
return gradient_mag
class CuriosityNetwork(torch.nn.Module):
EPSILON = 1e-10
def __init__(self, specs: BehaviorSpec,
settings: CuriositySettings) -> None:
super().__init__()
self._action_spec = specs.action_spec
state_encoder_settings = NetworkSettings(
normalize=False,
hidden_units=settings.encoding_size,
num_layers=2,
vis_encode_type=EncoderType.SIMPLE,
memory=None,
)
self._state_encoder = NetworkBody(specs.observation_specs,
state_encoder_settings)
self._action_flattener = ActionFlattener(self._action_spec)
self.inverse_model_action_encoding = torch.nn.Sequential(
LinearEncoder(2 * settings.encoding_size, 1, 256))
if self._action_spec.continuous_size > 0:
self.continuous_action_prediction = linear_layer(
256, self._action_spec.continuous_size)
if self._action_spec.discrete_size > 0:
self.discrete_action_prediction = linear_layer(
256, sum(self._action_spec.discrete_branches))
self.forward_model_next_state_prediction = torch.nn.Sequential(
LinearEncoder(
settings.encoding_size + self._action_flattener.flattened_size,
1, 256),
linear_layer(256, settings.encoding_size),
)
def get_current_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Extracts the current state embedding from a mini_batch.
"""
n_obs = len(self._state_encoder.processors)
np_obs = ObsUtil.from_buffer(mini_batch, n_obs)
# Convert to tensors
tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
hidden, _ = self._state_encoder.forward(tensor_obs)
return hidden
def get_next_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Extracts the next state embedding from a mini_batch.
"""
n_obs = len(self._state_encoder.processors)
np_obs = ObsUtil.from_buffer_next(mini_batch, n_obs)
# Convert to tensors
tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
hidden, _ = self._state_encoder.forward(tensor_obs)
return hidden
def predict_action(self, mini_batch: AgentBuffer) -> ActionPredictionTuple:
"""
In the continuous case, returns the predicted action.
In the discrete case, returns the logits.
"""
inverse_model_input = torch.cat((self.get_current_state(mini_batch),
self.get_next_state(mini_batch)),
dim=1)
continuous_pred = None
discrete_pred = None
hidden = self.inverse_model_action_encoding(inverse_model_input)
if self._action_spec.continuous_size > 0:
continuous_pred = self.continuous_action_prediction(hidden)
if self._action_spec.discrete_size > 0:
raw_discrete_pred = self.discrete_action_prediction(hidden)
branches = ModelUtils.break_into_branches(
raw_discrete_pred, self._action_spec.discrete_branches)
branches = [torch.softmax(b, dim=1) for b in branches]
discrete_pred = torch.cat(branches, dim=1)
return ActionPredictionTuple(continuous_pred, discrete_pred)
def predict_next_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Uses the current state embedding and the action of the mini_batch to predict
the next state embedding.
"""
actions = AgentAction.from_buffer(mini_batch)
flattened_action = self._action_flattener.forward(actions)
forward_model_input = torch.cat(
(self.get_current_state(mini_batch), flattened_action), dim=1)
return self.forward_model_next_state_prediction(forward_model_input)
def compute_inverse_loss(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Computes the inverse loss for a mini_batch. Corresponds to the error on the
action prediction (given the current and next state).
"""
predicted_action = self.predict_action(mini_batch)
actions = AgentAction.from_buffer(mini_batch)
_inverse_loss = 0
if self._action_spec.continuous_size > 0:
sq_difference = (actions.continuous_tensor -
predicted_action.continuous)**2
sq_difference = torch.sum(sq_difference, dim=1)
_inverse_loss += torch.mean(
ModelUtils.dynamic_partition(
sq_difference,
ModelUtils.list_to_tensor(mini_batch[BufferKey.MASKS],
dtype=torch.float),
2,
)[1])
if self._action_spec.discrete_size > 0:
true_action = torch.cat(
ModelUtils.actions_to_onehot(
actions.discrete_tensor,
self._action_spec.discrete_branches),
dim=1,
)
cross_entropy = torch.sum(
-torch.log(predicted_action.discrete + self.EPSILON) *
true_action,
dim=1,
)
_inverse_loss += torch.mean(
ModelUtils.dynamic_partition(
cross_entropy,
ModelUtils.list_to_tensor(
mini_batch[BufferKey.MASKS],
dtype=torch.float), # use masks not action_masks
2,
)[1])
return _inverse_loss
def compute_reward(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Calculates the curiosity reward for the mini_batch. Corresponds to the error
between the predicted and actual next state.
"""
predicted_next_state = self.predict_next_state(mini_batch)
target = self.get_next_state(mini_batch)
sq_difference = 0.5 * (target - predicted_next_state)**2
sq_difference = torch.sum(sq_difference, dim=1)
return sq_difference
def compute_forward_loss(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Computes the loss for the next state prediction
"""
return torch.mean(
ModelUtils.dynamic_partition(
self.compute_reward(mini_batch),
ModelUtils.list_to_tensor(mini_batch[BufferKey.MASKS],
dtype=torch.float),
2,
)[1])