from typing import Optional, Tuple
import torch
import torch.nn as nn
from ding.torch_utils import MLP
from ding.utils import MODEL_REGISTRY, SequenceType
from numpy import ndarray
from .common import EZNetworkOutput, RepresentationNetworkMLP, PredictionNetworkMLP
from .utils import renormalize, get_params_mean, get_dynamic_mean, get_reward_mean
@MODEL_REGISTRY.register('EfficientZeroModelMLP')
class EfficientZeroModelMLP(nn.Module):
def __init__(
self,
observation_shape: int = 2,
action_space_size: int = 6,
lstm_hidden_size: int = 512,
latent_state_dim: int = 256,
fc_reward_layers: SequenceType = [32],
fc_value_layers: SequenceType = [32],
fc_policy_layers: SequenceType = [32],
reward_support_size: int = 601,
value_support_size: int = 601,
proj_hid: int = 1024,
proj_out: int = 1024,
pred_hid: int = 512,
pred_out: int = 1024,
self_supervised_learning_loss: bool = True,
categorical_distribution: bool = True,
last_linear_layer_init_zero: bool = True,
state_norm: bool = False,
activation: Optional[nn.Module] = nn.ReLU(inplace=True),
norm_type: Optional[str] = 'BN',
discrete_action_encoding_type: str = 'one_hot',
res_connection_in_dynamics: bool = False,
*args,
**kwargs,
):
"""
Overview:
The definition of the network model of EfficientZero, which is a generalization version for 1D vector obs.
The networks are mainly built on fully connected layers.
Sampled EfficientZero model consists of a representation network, a dynamics network and a prediction network.
The representation network is an MLP network which maps the raw observation to a latent state.
The dynamics network is an MLP+LSTM network which predicts the next latent state, reward_hidden_state and value_prefix given the current latent state and action.
The prediction network is an MLP network which predicts the value and policy given the current latent state.
Arguments:
- observation_shape (:obj:`int`): Observation space shape, e.g. 8 for Lunarlander.
- action_space_size: (:obj:`int`): Action space size, e.g. 4 for Lunarlander.
- lstm_hidden_size (:obj:`int`): The hidden size of LSTM in dynamics network to predict value_prefix.
- latent_state_dim (:obj:`int`): The dimension of latent state, such as 256.
- fc_reward_layers (:obj:`SequenceType`): The number of hidden layers of the reward head (MLP head).
- fc_value_layers (:obj:`SequenceType`): The number of hidden layers used in value head (MLP head).
- fc_policy_layers (:obj:`SequenceType`): The number of hidden layers used in policy head (MLP head).
- reward_support_size (:obj:`int`): The size of categorical reward output
- value_support_size (:obj:`int`): The size of categorical value output.
- proj_hid (:obj:`int`): The size of projection hidden layer.
- proj_out (:obj:`int`): The size of projection output layer.
- pred_hid (:obj:`int`): The size of prediction hidden layer.
- pred_out (:obj:`int`): The size of prediction output layer.
- self_supervised_learning_loss (:obj:`bool`): Whether to use self_supervised_learning related networks in Sampled EfficientZero model, default set it to False.
- categorical_distribution (:obj:`bool`): Whether to use discrete support to represent categorical distribution for value, reward/value_prefix.
- last_linear_layer_init_zero (:obj:`bool`): Whether to use zero initializations for the last layer of value/policy mlp, default sets it to True.
- state_norm (:obj:`bool`): Whether to use normalization for latent states, default sets it to True.
- activation (:obj:`Optional[nn.Module]`): Activation function used in network, which often use in-place \
operation to speedup, e.g. ReLU(inplace=True).
- discrete_action_encoding_type (:obj:`str`): The type of encoding for discrete action. Default sets it to 'one_hot'. options = {'one_hot', 'not_one_hot'}
- norm_type (:obj:`str`): The type of normalization in networks. defaults to 'BN'.
- res_connection_in_dynamics (:obj:`bool`): Whether to use residual connection for dynamics network, default set it to False.
"""
super(EfficientZeroModelMLP, self).__init__()
if not categorical_distribution:
self.reward_support_size = 1
self.value_support_size = 1
else:
self.reward_support_size = reward_support_size
self.value_support_size = value_support_size
self.action_space_size = action_space_size
self.continuous_action_space = False
# The dim of action space. For discrete action space, it is 1.
# For continuous action space, it is the dimension of continuous action.
self.action_space_dim = action_space_size if self.continuous_action_space else 1
assert discrete_action_encoding_type in ['one_hot', 'not_one_hot'], discrete_action_encoding_type
self.discrete_action_encoding_type = discrete_action_encoding_type
if self.continuous_action_space:
self.action_encoding_dim = action_space_size
else:
if self.discrete_action_encoding_type == 'one_hot':
self.action_encoding_dim = action_space_size
elif self.discrete_action_encoding_type == 'not_one_hot':
self.action_encoding_dim = 1
self.lstm_hidden_size = lstm_hidden_size
self.proj_hid = proj_hid
self.proj_out = proj_out
self.pred_hid = pred_hid
self.pred_out = pred_out
self.self_supervised_learning_loss = self_supervised_learning_loss
self.last_linear_layer_init_zero = last_linear_layer_init_zero
self.state_norm = state_norm
self.res_connection_in_dynamics = res_connection_in_dynamics
self.representation_network = RepresentationNetworkMLP(
observation_shape=observation_shape, hidden_channels=latent_state_dim, norm_type=norm_type
)
self.dynamics_network = DynamicsNetworkMLP(
action_encoding_dim=self.action_encoding_dim,
num_channels=latent_state_dim + self.action_encoding_dim,
common_layer_num=2,
lstm_hidden_size=lstm_hidden_size,
fc_reward_layers=fc_reward_layers,
output_support_size=self.reward_support_size,
last_linear_layer_init_zero=self.last_linear_layer_init_zero,
norm_type=norm_type,
res_connection_in_dynamics=self.res_connection_in_dynamics,
)
self.prediction_network = PredictionNetworkMLP(
action_space_size=action_space_size,
num_channels=latent_state_dim,
fc_value_layers=fc_value_layers,
fc_policy_layers=fc_policy_layers,
output_support_size=self.value_support_size,
last_linear_layer_init_zero=self.last_linear_layer_init_zero,
norm_type=norm_type
)
if self.self_supervised_learning_loss:
# self_supervised_learning_loss related network proposed in EfficientZero
self.projection_input_dim = latent_state_dim
self.projection = nn.Sequential(
nn.Linear(self.projection_input_dim, self.proj_hid), nn.BatchNorm1d(self.proj_hid), activation,
nn.Linear(self.proj_hid, self.proj_hid), nn.BatchNorm1d(self.proj_hid), activation,
nn.Linear(self.proj_hid, self.proj_out), nn.BatchNorm1d(self.proj_out)
)
self.prediction_head = nn.Sequential(
nn.Linear(self.proj_out, self.pred_hid),
nn.BatchNorm1d(self.pred_hid),
activation,
nn.Linear(self.pred_hid, self.pred_out),
)
def initial_inference(self, obs: torch.Tensor) -> EZNetworkOutput:
"""
Overview:
Initial inference of EfficientZero model, which is the first step of the EfficientZero model.
To perform the initial inference, we first use the representation network to obtain the "latent_state" of the observation.
Then we use the prediction network to predict the "value" and "policy_logits" of the "latent_state", and
also prepare the zeros-like ``reward_hidden_state`` for the next step of the EfficientZero model.
Arguments:
- obs (:obj:`torch.Tensor`): The 1D vector observation data.
Returns (EZNetworkOutput):
- value (:obj:`torch.Tensor`): The output value of input state to help policy improvement and evaluation.
- value_prefix (:obj:`torch.Tensor`): The predicted prefix sum of value for input state. \
In initial inference, we set it to zero vector.
- policy_logits (:obj:`torch.Tensor`): The output logit to select discrete action.
- latent_state (:obj:`torch.Tensor`): The encoding latent state of input state.
- reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The hidden state of LSTM about reward. In initial inference, \
we set it to the zeros-like hidden state (H and C).
Shapes:
- obs (:obj:`torch.Tensor`): :math:`(B, obs_shape)`, where B is batch_size.
- value (:obj:`torch.Tensor`): :math:`(B, value_support_size)`, where B is batch_size.
- value_prefix (:obj:`torch.Tensor`): :math:`(B, reward_support_size)`, where B is batch_size.
- policy_logits (:obj:`torch.Tensor`): :math:`(B, action_dim)`, where B is batch_size.
- latent_state (:obj:`torch.Tensor`): :math:`(B, H)`, where B is batch_size, H is the dimension of latent state.
- reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The shape of each element is :math:`(1, B, lstm_hidden_size)`, where B is batch_size.
"""
batch_size = obs.size(0)
latent_state = self._representation(obs)
policy_logits, value = self._prediction(latent_state)
# zero initialization for reward hidden states
# (hn, cn), each element shape is (layer_num=1, batch_size, lstm_hidden_size)
reward_hidden_state = (
torch.zeros(1, batch_size,
self.lstm_hidden_size).to(obs.device), torch.zeros(1, batch_size,
self.lstm_hidden_size).to(obs.device)
)
return EZNetworkOutput(value, [0. for _ in range(batch_size)], policy_logits, latent_state, reward_hidden_state)
def recurrent_inference(
self, latent_state: torch.Tensor, reward_hidden_state: torch.Tensor, action: torch.Tensor
) -> EZNetworkOutput:
"""
Overview:
Recurrent inference of EfficientZero model, which is the rollout step of the EfficientZero model.
To perform the recurrent inference, we first use the dynamics network to predict ``next_latent_state``,
``reward_hidden_state``, ``value_prefix`` by the given current ``latent_state`` and ``action``.
We then use the prediction network to predict the ``value`` and ``policy_logits``.
Arguments:
- latent_state (:obj:`torch.Tensor`): The encoding latent state of input state.
- reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The input hidden state of LSTM about reward.
- action (:obj:`torch.Tensor`): The predicted action to rollout.
Returns (EZNetworkOutput):
- value (:obj:`torch.Tensor`): The output value of input state to help policy improvement and evaluation.
- value_prefix (:obj:`torch.Tensor`): The predicted prefix sum of value for input state.
- policy_logits (:obj:`torch.Tensor`): The output logit to select discrete action.
- next_latent_state (:obj:`torch.Tensor`): The predicted next latent state.
- reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The output hidden state of LSTM about reward.
Shapes:
- action (:obj:`torch.Tensor`): :math:`(B, )`, where B is batch_size.
- value (:obj:`torch.Tensor`): :math:`(B, value_support_size)`, where B is batch_size.
- value_prefix (:obj:`torch.Tensor`): :math:`(B, reward_support_size)`, where B is batch_size.
- policy_logits (:obj:`torch.Tensor`): :math:`(B, action_dim)`, where B is batch_size.
- latent_state (:obj:`torch.Tensor`): :math:`(B, H)`, where B is batch_size, H is the dimension of latent state.
- next_latent_state (:obj:`torch.Tensor`): :math:`(B, H)`, where B is batch_size, H is the dimension of latent state.
- reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The shape of each element is :math:`(1, B, lstm_hidden_size)`, where B is batch_size.
"""
next_latent_state, reward_hidden_state, value_prefix = self._dynamics(latent_state, reward_hidden_state, action)
policy_logits, value = self._prediction(next_latent_state)
return EZNetworkOutput(value, value_prefix, policy_logits, next_latent_state, reward_hidden_state)
def _representation(self, observation: torch.Tensor) -> Tuple[torch.Tensor]:
"""
Overview:
Use the representation network to encode the observations into latent state.
Arguments:
- obs (:obj:`torch.Tensor`): The 1D vector observation data.
Returns:
- latent_state (:obj:`torch.Tensor`): The encoding latent state of input state.
Shapes:
- obs (:obj:`torch.Tensor`): :math:`(B, obs_shape)`, where B is batch_size.
- latent_state (:obj:`torch.Tensor`): :math:`(B, H)`, where B is batch_size, H is the dimension of latent state.
"""
latent_state = self.representation_network(observation)
if self.state_norm:
latent_state = renormalize(latent_state)
return latent_state
def _prediction(self, latent_state: torch.Tensor) -> Tuple[torch.Tensor]:
"""
Overview:
Use the representation network to encode the observations into latent state.
Arguments:
- obs (:obj:`torch.Tensor`): The 1D vector observation data.
Returns:
- policy_logits (:obj:`torch.Tensor`): The output logit to select discrete action.
- value (:obj:`torch.Tensor`): The output value of input state to help policy improvement and evaluation.
Shapes:
- latent_state (:obj:`torch.Tensor`): :math:`(B, H)`, where B is batch_size, H is the dimension of latent state.
- policy_logits (:obj:`torch.Tensor`): :math:`(B, action_dim)`, where B is batch_size.
- value (:obj:`torch.Tensor`): :math:`(B, value_support_size)`, where B is batch_size.
"""
policy_logits, value = self.prediction_network(latent_state)
return policy_logits, value
def _dynamics(self, latent_state: torch.Tensor, reward_hidden_state: Tuple,
action: torch.Tensor) -> Tuple[torch.Tensor, Tuple[torch.Tensor], torch.Tensor]:
"""
Overview:
Concatenate ``latent_state`` and ``action`` and use the dynamics network to predict ``next_latent_state``
``value_prefix`` and ``next_reward_hidden_state``.
Arguments:
- latent_state (:obj:`torch.Tensor`): The encoding latent state of input state.
- reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The input hidden state of LSTM about reward.
- action (:obj:`torch.Tensor`): The predicted action to rollout.
Returns:
- next_latent_state (:obj:`torch.Tensor`): The predicted latent state of the next timestep.
- next_reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The output hidden state of LSTM about reward.
- value_prefix (:obj:`torch.Tensor`): The predicted prefix sum of value for input state.
Shapes:
- latent_state (:obj:`torch.Tensor`): :math:`(B, H)`, where B is batch_size, H is the dimension of latent state.
- action (:obj:`torch.Tensor`): :math:`(B, )`, where B is batch_size.
- next_latent_state (:obj:`torch.Tensor`): :math:`(B, H)`, where B is batch_size, H is the dimension of latent state.
- value_prefix (:obj:`torch.Tensor`): :math:`(B, reward_support_size)`, where B is batch_size.
"""
# NOTE: the discrete action encoding type is important for some environments
# discrete action space
if self.discrete_action_encoding_type == 'one_hot':
# Stack latent_state with the one hot encoded action
if len(action.shape) == 1:
# (batch_size, ) -> (batch_size, 1)
# e.g., torch.Size([8]) -> torch.Size([8, 1])
action = action.unsqueeze(-1)
# transform action to one-hot encoding.
# action_one_hot shape: (batch_size, action_space_size), e.g., (8, 4)
action_one_hot = torch.zeros(action.shape[0], self.action_space_size, device=action.device)
# transform action to torch.int64
action = action.long()
action_one_hot.scatter_(1, action, 1)
action_encoding = action_one_hot
elif self.discrete_action_encoding_type == 'not_one_hot':
action_encoding = action / self.action_space_size
if len(action_encoding.shape) == 1:
# (batch_size, ) -> (batch_size, 1)
# e.g., torch.Size([8]) -> torch.Size([8, 1])
action_encoding = action_encoding.unsqueeze(-1)
action_encoding = action_encoding.to(latent_state.device).float()
# state_action_encoding shape: (batch_size, latent_state[1] + action_dim]) or
# (batch_size, latent_state[1] + action_space_size]) depending on the discrete_action_encoding_type.
state_action_encoding = torch.cat((latent_state, action_encoding), dim=1)
# NOTE: the key difference with MuZero
next_latent_state, next_reward_hidden_state, value_prefix = self.dynamics_network(
state_action_encoding, reward_hidden_state
)
if self.state_norm:
next_latent_state = renormalize(next_latent_state)
return next_latent_state, next_reward_hidden_state, value_prefix
def project(self, latent_state: torch.Tensor, with_grad=True):
"""
Overview:
Project the latent state to a lower dimension to calculate the self-supervised loss, which is proposed in EfficientZero.
For more details, please refer to the paper ``Exploring Simple Siamese Representation Learning``.
Arguments:
- latent_state (:obj:`torch.Tensor`): The encoding latent state of input state.
- with_grad (:obj:`bool`): Whether to calculate gradient for the projection result.
Returns:
- proj (:obj:`torch.Tensor`): The result embedding vector of projection operation.
Shapes:
- latent_state (:obj:`torch.Tensor`): :math:`(B, H)`, where B is batch_size, H is the dimension of latent state.
- proj (:obj:`torch.Tensor`): :math:`(B, projection_output_dim)`, where B is batch_size.
Examples:
>>> latent_state = torch.randn(256, 64)
>>> output = self.project(latent_state)
>>> output.shape # (256, 1024)
"""
proj = self.projection(latent_state)
if with_grad:
# with grad, use prediction_head
return self.prediction_head(proj)
else:
return proj.detach()
def get_params_mean(self) -> float:
return get_params_mean(self)
[docs]class DynamicsNetworkMLP(nn.Module):
[docs] def __init__(
self,
action_encoding_dim: int = 2,
num_channels: int = 64,
common_layer_num: int = 2,
fc_reward_layers: SequenceType = [32],
output_support_size: int = 601,
lstm_hidden_size: int = 512,
last_linear_layer_init_zero: bool = True,
activation: Optional[nn.Module] = nn.ReLU(inplace=True),
norm_type: Optional[str] = 'BN',
res_connection_in_dynamics: bool = False,
):
"""
Overview:
The definition of dynamics network in EfficientZero algorithm, which is used to predict next latent state
value_prefix and reward_hidden_state by the given current latent state and action.
The networks are mainly built on fully connected layers.
Arguments:
- action_encoding_dim (:obj:`int`): The dimension of action encoding.
- num_channels (:obj:`int`): The num of channels in latent states.
- common_layer_num (:obj:`int`): The number of common layers in dynamics network.
- fc_reward_layers (:obj:`SequenceType`): The number of hidden layers of the reward head (MLP head).
- output_support_size (:obj:`int`): The size of categorical reward output.
- lstm_hidden_size (:obj:`int`): The hidden size of lstm in dynamics network.
- last_linear_layer_init_zero (:obj:`bool`): Whether to use zero initializationss for the last layer of value/policy head, default sets it to True.
- activation (:obj:`Optional[nn.Module]`): Activation function used in network, which often use in-place \
operation to speedup, e.g. ReLU(inplace=True).
- norm_type (:obj:`str`): The type of normalization in networks. defaults to 'BN'.
- res_connection_in_dynamics (:obj:`bool`): Whether to use residual connection in dynamics network.
"""
super().__init__()
assert num_channels > action_encoding_dim, f'num_channels:{num_channels} <= action_encoding_dim:{action_encoding_dim}'
self.num_channels = num_channels
self.action_encoding_dim = action_encoding_dim
self.latent_state_dim = self.num_channels - self.action_encoding_dim
self.lstm_hidden_size = lstm_hidden_size
self.activation = activation
self.res_connection_in_dynamics = res_connection_in_dynamics
if self.res_connection_in_dynamics:
self.fc_dynamics_1 = MLP(
in_channels=self.num_channels,
hidden_channels=self.latent_state_dim,
layer_num=common_layer_num,
out_channels=self.latent_state_dim,
activation=activation,
norm_type=norm_type,
output_activation=True,
output_norm=True,
# last_linear_layer_init_zero=False is important for convergence
last_linear_layer_init_zero=False,
)
self.fc_dynamics_2 = MLP(
in_channels=self.latent_state_dim,
hidden_channels=self.latent_state_dim,
layer_num=common_layer_num,
out_channels=self.latent_state_dim,
activation=activation,
norm_type=norm_type,
output_activation=True,
output_norm=True,
# last_linear_layer_init_zero=False is important for convergence
last_linear_layer_init_zero=False,
)
else:
self.fc_dynamics = MLP(
in_channels=self.num_channels,
hidden_channels=self.latent_state_dim,
layer_num=common_layer_num,
out_channels=self.latent_state_dim,
activation=activation,
norm_type=norm_type,
output_activation=True,
output_norm=True,
# last_linear_layer_init_zero=False is important for convergence
last_linear_layer_init_zero=False,
)
# input_shape: （sequence_length，batch_size，input_size)
# output_shape: (sequence_length, batch_size, hidden_size)
self.lstm = nn.LSTM(input_size=self.latent_state_dim, hidden_size=self.lstm_hidden_size)
self.fc_reward_head = MLP(
in_channels=self.lstm_hidden_size,
hidden_channels=fc_reward_layers[0],
layer_num=2,
out_channels=output_support_size,
activation=self.activation,
norm_type=norm_type,
output_activation=False,
output_norm=False,
last_linear_layer_init_zero=last_linear_layer_init_zero
)
[docs] def forward(self, state_action_encoding: torch.Tensor, reward_hidden_state):
"""
Overview:
Forward computation of the dynamics network. Predict next latent state given current state_action_encoding and reward hidden state.
Arguments:
- state_action_encoding (:obj:`torch.Tensor`): The state-action encoding, which is the concatenation of \
latent state and action encoding, with shape (batch_size, num_channels, height, width).
- reward_hidden_state (:obj:`Tuple[torch.Tensor, torch.Tensor]`): The input hidden state of LSTM about reward.
Returns:
- next_latent_state (:obj:`torch.Tensor`): The next latent state, with shape (batch_size, latent_state_dim).
- next_reward_hidden_state (:obj:`torch.Tensor`): The input hidden state of LSTM about reward.
- value_prefix (:obj:`torch.Tensor`): The predicted prefix sum of value for input state.
"""
if self.res_connection_in_dynamics:
# take the state encoding (latent_state), state_action_encoding[:, -self.action_encoding_dim]
# is action encoding
latent_state = state_action_encoding[:, :-self.action_encoding_dim]
x = self.fc_dynamics_1(state_action_encoding)
# the residual link: add state encoding to the state_action encoding
next_latent_state = x + latent_state
next_latent_state_ = self.fc_dynamics_2(next_latent_state)
else:
next_latent_state = self.fc_dynamics(state_action_encoding)
next_latent_state_ = next_latent_state
next_latent_state_unsqueeze = next_latent_state_.unsqueeze(0)
value_prefix, next_reward_hidden_state = self.lstm(next_latent_state_unsqueeze, reward_hidden_state)
value_prefix = self.fc_reward_head(value_prefix.squeeze(0))
return next_latent_state, next_reward_hidden_state, value_prefix
[docs] def get_dynamic_mean(self) -> float:
return get_dynamic_mean(self)
[docs] def get_reward_mean(self) -> Tuple[ndarray, float]:
return get_reward_mean(self)