import math
from typing import Optional, Tuple
import torch
import torch.nn as nn
from ding.model.common import ReparameterizationHead
from ding.torch_utils import MLP, ResBlock
from ding.utils import MODEL_REGISTRY, SequenceType
from .common import EZNetworkOutput, RepresentationNetwork
from .efficientzero_model import DynamicsNetwork
from .utils import renormalize, get_params_mean
# use ModelRegistry to register the model, for more details about ModelRegistry, please refer to DI-engine's document.
@MODEL_REGISTRY.register('SampledEfficientZeroModel')
class SampledEfficientZeroModel(nn.Module):
def __init__(
self,
observation_shape: SequenceType = (12, 96, 96),
action_space_size: int = 6,
num_res_blocks: int = 1,
num_channels: int = 64,
lstm_hidden_size: int = 512,
reward_head_channels: int = 16,
value_head_channels: int = 16,
policy_head_channels: int = 16,
fc_reward_layers: SequenceType = [256],
fc_value_layers: SequenceType = [256],
fc_policy_layers: SequenceType = [256],
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,
activation: Optional[nn.Module] = nn.GELU(approximate='tanh'),
last_linear_layer_init_zero: bool = True,
state_norm: bool = False,
downsample: bool = False,
# ==============================================================
# specific sampled related config
# ==============================================================
continuous_action_space: bool = False,
num_of_sampled_actions: int = 6,
sigma_type='conditioned',
fixed_sigma_value: float = 0.3,
bound_type: str = None,
norm_type: str = 'LN',
discrete_action_encoding_type: str = 'one_hot',
use_sim_norm: bool = False,
*args,
**kwargs,
):
"""
Overview:
The definition of the network model of Sampled EfficientZero, which is a generalization version for 2D image obs.
The networks are mainly built on convolution residual blocks and 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:`SequenceType`): Observation space shape, e.g. [C, W, H]=[12, 96, 96] for Atari.
- action_space_size: (:obj:`int`): Action space size, which is an integer number. For discrete action space, it is the num of discrete actions, \
e.g. 4 for Lunarlander. For continuous action space, it is the dimension of the continuous action, e.g. 4 for bipedalwalker.
- num_res_blocks (:obj:`int`): The number of res blocks in Sampled EfficientZero model.
- num_channels (:obj:`int`): The channels of hidden states.
- lstm_hidden_size (:obj:`int`): dim of lstm hidden state in dynamics network.
- reward_head_channels (:obj:`int`): The channels of reward head.
- value_head_channels (:obj:`int`): The channels of value head.
- policy_head_channels (:obj:`int`): The channels of policy head.
- 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 model, default set it to False.
- categorical_distribution (:obj:`bool`): Whether to use discrete support to represent categorical distribution \
for value, reward/value_prefix.
- activation (:obj:`Optional[nn.Module]`): Activation function used in network, which often use in-place \
operation to speedup, e.g. ReLU(inplace=True).
- 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 hidden states, default sets it to True.
- downsample (:obj:`bool`): Whether to do downsampling for observations in ``representation_network``, \
defaults to True. This option is often used in video games like Atari. In board games like go, \
we don't need this module.
# ==============================================================
# specific sampled related config
# ==============================================================
- continuous_action_space (:obj:`bool`): The type of action space. default set it to False.
- num_of_sampled_actions (:obj:`int`): the number of sampled actions, i.e. the K in original Sampled MuZero paper.
# Please see ``ReparameterizationHead`` in ``ding.model.common.head`` for more details about the following arguments.
- sigma_type (:obj:`str`): the type of sigma in policy head of prediction network, options={'conditioned', 'fixed'}.
- fixed_sigma_value (:obj:`float`): the fixed sigma value in policy head of prediction network,
- bound_type (:obj:`str`): The type of bound in networks, default set it to None.
- norm_type (:obj:`str`): The type of normalization in networks, default sets it to 'BN'.
- 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'}
"""
super(SampledEfficientZeroModel, self).__init__()
if isinstance(observation_shape, int) or len(observation_shape) == 1:
# for vector obs input, e.g. classical control and box2d environments
# to be compatible with LightZero model/policy, transform to shape: [C, W, H]
observation_shape = [1, observation_shape, 1]
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.continuous_action_space = continuous_action_space
self.action_space_size = action_space_size
# The dim of action space. For discrete action space, it's 1.
# For continuous action space, it is the dim of 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.last_linear_layer_init_zero = last_linear_layer_init_zero
self.state_norm = state_norm
self.downsample = downsample
self.self_supervised_learning_loss = self_supervised_learning_loss
self.sigma_type = sigma_type
self.fixed_sigma_value = fixed_sigma_value
self.bound_type = bound_type
self.norm_type = norm_type
self.num_of_sampled_actions = num_of_sampled_actions
if observation_shape[1] == 96:
latent_size = math.ceil(observation_shape[1] / 16) * math.ceil(observation_shape[2] / 16)
elif observation_shape[1] == 84:
latent_size = math.ceil(observation_shape[1] / 14) * math.ceil(observation_shape[2] / 14)
elif observation_shape[1] == 64:
latent_size = math.ceil(observation_shape[1] / 8) * math.ceil(observation_shape[2] / 8)
else:
raise ValueError("Invalid observation shape, only support 64, 84, 96 for now.")
flatten_output_size_for_reward_head = (
(reward_head_channels * latent_size) if downsample else
(reward_head_channels * observation_shape[1] * observation_shape[2])
)
flatten_output_size_for_value_head = (
(value_head_channels * latent_size) if downsample else
(value_head_channels * observation_shape[1] * observation_shape[2])
)
flatten_output_size_for_policy_head = (
(policy_head_channels * latent_size) if downsample else
(policy_head_channels * observation_shape[1] * observation_shape[2])
)
self.representation_network = RepresentationNetwork(
observation_shape,
num_res_blocks,
num_channels,
downsample,
norm_type=self.norm_type,
use_sim_norm=use_sim_norm,
)
self.dynamics_network = DynamicsNetwork(
observation_shape,
self.action_encoding_dim,
num_res_blocks,
num_channels + self.action_encoding_dim,
reward_head_channels,
fc_reward_layers,
self.reward_support_size,
flatten_output_size_for_reward_head,
downsample,
lstm_hidden_size=self.lstm_hidden_size,
last_linear_layer_init_zero=self.last_linear_layer_init_zero,
activation=activation,
norm_type=norm_type
)
self.prediction_network = PredictionNetwork(
observation_shape,
self.continuous_action_space,
action_space_size,
num_res_blocks,
num_channels,
value_head_channels,
policy_head_channels,
fc_value_layers,
fc_policy_layers,
self.value_support_size,
flatten_output_size_for_value_head,
flatten_output_size_for_policy_head,
downsample,
last_linear_layer_init_zero=self.last_linear_layer_init_zero,
sigma_type=self.sigma_type,
fixed_sigma_value=self.fixed_sigma_value,
bound_type=self.bound_type,
norm_type=self.norm_type,
)
if self.self_supervised_learning_loss:
# self_supervised_learning_loss related network proposed in EfficientZero
if self.downsample:
# In Atari, if the observation_shape is set to (12, 96, 96), which indicates the original shape of
# (3,96,96), and frame_stack_num is 4. Due to downsample, the encoding of observation (latent_state) is
# (64, 96/16, 96/16), where 64 is the number of channels, 96/16 is the size of the latent state. Thus,
# self.projection_input_dim = 64 * 96/16 * 96/16 = 64*6*6 = 2304
self.projection_input_dim = num_channels * math.ceil(observation_shape[1] / 16
) * math.ceil(observation_shape[2] / 16)
else:
self.projection_input_dim = num_channels * observation_shape[1] * observation_shape[2]
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 SampledEfficientZero model, which is the first step of the SampledEfficientZero 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 SampledEfficientZero model.
Arguments:
- obs (:obj:`torch.Tensor`): The 2D image 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, num_channel, obs_shape[1], obs_shape[2])`, 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_, W_)`, where B is batch_size, H_ is the height of \
latent state, W_ is the width 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 Sampled EfficientZero model, which is the rollout step of the Sampled 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.
- latent_state (:obj:`torch.Tensor`): The encoding latent state of input state.
- 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:
- obs (:obj:`torch.Tensor`): :math:`(B, num_channel, obs_shape[1], obs_shape[2])`, where B is batch_size.
- 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_, W_)`, where B is batch_size, H_ is the height of \
latent state, W_ is the width of latent state.
- next_latent_state (:obj:`torch.Tensor`): :math:`(B, H_, W_)`, where B is batch_size, H_ is the height of \
latent state, W_ is the width 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 2D image observation data.
Returns:
- latent_state (:obj:`torch.Tensor`): The encoding latent state of input state.
Shapes:
- obs (:obj:`torch.Tensor`): :math:`(B, num_channel, obs_shape[1], obs_shape[2])`, where B is batch_size.
- latent_state (:obj:`torch.Tensor`): :math:`(B, H_, W_)`, where B is batch_size, H_ is the height of \
latent state, W_ is the width 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, torch.Tensor]:
"""
Overview:
use the prediction network to predict the "value" and "policy_logits" of the "latent_state".
Arguments:
- latent_state (:obj:`torch.Tensor`): The encoding latent state of input obs.
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_, W_)`, where B is batch_size, H_ is the height of \
latent state, W_ is the width 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.
"""
return self.prediction_network(latent_state)
def _dynamics(self, latent_state: torch.Tensor, reward_hidden_state: Tuple[torch.Tensor],
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_, W_)`, where B is batch_size, H_ is the height of \
latent state, W_ is the width of latent state.
- action (:obj:`torch.Tensor`): :math:`(B, )`, where B is batch_size.
- next_latent_state (:obj:`torch.Tensor`): :math:`(B, H_, W_)`, where B is batch_size, H_ is the height of \
latent state, W_ is the width 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
if not self.continuous_action_space:
# discrete action space
if self.discrete_action_encoding_type == 'one_hot':
# Stack latent_state with the one hot encoded action.
# The final action_encoding shape is (batch_size, action_space_size, latent_state[2], latent_state[3]), e.g. (8, 2, 4, 1).
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_tmp = action_one_hot.unsqueeze(-1).unsqueeze(-1)
action_encoding = action_encoding_tmp.expand(
latent_state.shape[0], self.action_space_size, latent_state.shape[2], latent_state.shape[3]
)
elif self.discrete_action_encoding_type == 'not_one_hot':
# Stack latent_state with the normalized encoded action.
# The final action_encoding shape is (batch_size, 1, latent_state[2], latent_state[3]), e.g. (8, 1, 4, 1).
if len(action.shape) == 2:
# (batch_size, action_dim=1) -> (batch_size, 1, 1, 1)
# e.g., torch.Size([8, 1]) -> torch.Size([8, 1, 1, 1])
action = action.unsqueeze(-1).unsqueeze(-1)
elif len(action.shape) == 1:
# (batch_size,) -> (batch_size, 1, 1, 1)
# e.g., torch.Size([8]) -> torch.Size([8, 1, 1, 1])
action = action.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
action_encoding = action.expand(
latent_state.shape[0], 1, latent_state.shape[2], latent_state.shape[3]
) / self.action_space_size
else:
# continuous action space
if len(action.shape) == 1:
# (batch_size,) -> (batch_size, action_dim=1, 1, 1)
# e.g., torch.Size([8]) -> torch.Size([8, 1, 1, 1])
action = action.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
elif len(action.shape) == 2:
# (batch_size, action_dim) -> (batch_size, action_dim, 1, 1)
# e.g., torch.Size([8, 2]) -> torch.Size([8, 2, 1, 1])
action = action.unsqueeze(-1).unsqueeze(-1)
elif len(action.shape) == 3:
# (batch_size, action_dim, 1) -> (batch_size, action_dim)
# e.g., torch.Size([8, 2, 1]) -> torch.Size([8, 2, 1, 1])
action = action.unsqueeze(-1)
action_encoding_tmp = action
action_encoding = action_encoding_tmp.expand(
latent_state.shape[0], self.action_space_size, latent_state.shape[2], latent_state.shape[3]
)
action_encoding = action_encoding.to(latent_state.device).float()
# state_action_encoding shape: (batch_size, latent_state[1] + action_dim, latent_state[2], latent_state[3]) or
# (batch_size, latent_state[1] + action_space_size, latent_state[2], latent_state[3]) depending on the discrete_action_encoding_type.
state_action_encoding = torch.cat((latent_state, action_encoding), dim=1)
next_latent_state, next_reward_hidden_state, value_prefix = self.dynamics_network(
state_action_encoding, reward_hidden_state
)
if not self.state_norm:
return next_latent_state, next_reward_hidden_state, value_prefix
else:
next_latent_state_normalized = renormalize(next_latent_state)
return next_latent_state_normalized, next_reward_hidden_state, value_prefix
def project(self, latent_state: torch.Tensor, with_grad=True) -> torch.Tensor:
"""
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 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_, W_)`, where B is batch_size, H_ is the height of \
latent state, W_ is the width of latent state.
- proj (:obj:`torch.Tensor`): :math:`(B, projection_output_dim)`, where B is batch_size.
Examples:
>>> latent_state = torch.randn(256, 64, 6, 6)
>>> output = self.project(latent_state)
>>> output.shape # (256, 1024)
.. note::
for Atari:
observation_shape = (12, 96, 96), # original shape is (3,96,96), frame_stack_num=4
if downsample is True, latent_state.shape: (batch_size, num_channel, obs_shape[1] / 16, obs_shape[2] / 16)
i.e., (256, 64, 96 / 16, 96 / 16) = (256, 64, 6, 6)
latent_state reshape: (256, 64, 6, 6) -> (256,64*6*6) = (256, 2304)
# self.projection_input_dim = 64*6*6 = 2304
# self.projection_output_dim = 1024
"""
latent_state = latent_state.reshape(latent_state.shape[0], -1)
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):
return get_params_mean(self)
[docs]class PredictionNetwork(nn.Module):
[docs] def __init__(
self,
observation_shape: SequenceType,
continuous_action_space,
action_space_size,
num_res_blocks,
num_channels,
value_head_channels,
policy_head_channels,
fc_value_layers,
fc_policy_layers,
output_support_size,
flatten_output_size_for_value_head,
flatten_output_size_for_policy_head,
downsample: bool = False,
last_linear_layer_init_zero: bool = True,
activation: Optional[nn.Module] = nn.GELU(approximate='tanh'),
# ==============================================================
# specific sampled related config
# ==============================================================
sigma_type='conditioned',
fixed_sigma_value: float = 0.3,
bound_type: str = None,
norm_type: str = 'LN',
):
"""
Overview:
The definition of policy and value prediction network, which is used to predict value and policy by the
given latent state.
The networks are mainly build on res_conv_blocks and fully connected layers.
Arguments:
- observation_shape (:obj:`SequenceType`): The shape of observation space, e.g. (C, H, W) for image.
- continuous_action_space (:obj:`bool`): The type of action space. Default sets it to False.
- action_space_size: (:obj:`int`): Action space size, usually an integer number. For discrete action \
space, it is the number of discrete actions. For continuous action space, it is the dimension of \
continuous action.
- num_res_blocks (:obj:`int`): number of res blocks in model.
- num_channels (:obj:`int`): channels of hidden states.
- value_head_channels (:obj:`int`): channels of value head.
- policy_head_channels (:obj:`int`): channels of policy head.
- fc_value_layers (:obj:`SequenceType`): hidden layers of the value prediction head (MLP head).
- fc_policy_layers (:obj:`SequenceType`): hidden layers of the policy prediction head (MLP head).
- output_support_size (:obj:`int`): dim of value output.
- flatten_output_size_for_value_head (:obj:`int`): dim of flatten hidden states.
- flatten_output_size_for_policy_head (:obj:`int`): dim of flatten hidden states.
- downsample (:obj:`bool`): Whether to do downsampling for observations in ``representation_network``.
- last_linear_layer_init_zero (:obj:`bool`): Whether to use zero initializationss for the last layer of value/policy mlp, default sets it to True.
# ==============================================================
# specific sampled related config
# ==============================================================
# see ``ReparameterizationHead`` in ``ding.model.common.head`` for more details about the following arguments.
- sigma_type (:obj:`str`): the type of sigma in policy head of prediction network, options={'conditioned', 'fixed'}.
- fixed_sigma_value (:obj:`float`): the fixed sigma value in policy head of prediction network,
- bound_type (:obj:`str`): The type of bound in networks. Default sets it to None.
- norm_type (:obj:`str`): The type of normalization in networks. Default sets it to 'BN'.
"""
super().__init__()
self.continuous_action_space = continuous_action_space
self.flatten_output_size_for_value_head = flatten_output_size_for_value_head
self.flatten_output_size_for_policy_head = flatten_output_size_for_policy_head
self.norm_type = norm_type
self.sigma_type = sigma_type
self.fixed_sigma_value = fixed_sigma_value
self.bound_type = bound_type
self.activation = activation
self.resblocks = nn.ModuleList(
[
ResBlock(
in_channels=num_channels,
activation=activation,
norm_type=self.norm_type,
res_type='basic',
bias=False
) for _ in range(num_res_blocks)
]
)
self.conv1x1_value = nn.Conv2d(num_channels, value_head_channels, 1)
self.conv1x1_policy = nn.Conv2d(num_channels, policy_head_channels, 1)
if norm_type == 'BN':
self.norm_value = nn.BatchNorm2d(value_head_channels)
self.norm_policy = nn.BatchNorm2d(policy_head_channels)
elif norm_type == 'LN':
if downsample:
self.norm_value = nn.LayerNorm(
[value_head_channels, math.ceil(observation_shape[-2] / 16), math.ceil(observation_shape[-1] / 16)])
self.norm_policy = nn.LayerNorm([policy_head_channels, math.ceil(observation_shape[-2] / 16),
math.ceil(observation_shape[-1] / 16)])
else:
self.norm_value = nn.LayerNorm([value_head_channels, observation_shape[-2], observation_shape[-1]])
self.norm_policy = nn.LayerNorm([policy_head_channels, observation_shape[-2], observation_shape[-1]])
self.fc_value_head = MLP(
in_channels=self.flatten_output_size_for_value_head,
hidden_channels=fc_value_layers[0],
out_channels=output_support_size,
layer_num=len(fc_value_layers) + 1,
activation=activation,
norm_type=self.norm_type,
output_activation=False,
output_norm=False,
# last_linear_layer_init_zero=True is beneficial for convergence speed.
last_linear_layer_init_zero=last_linear_layer_init_zero
)
# sampled related core code
if self.continuous_action_space:
self.fc_policy_head = ReparameterizationHead(
input_size=self.flatten_output_size_for_policy_head,
output_size=action_space_size,
layer_num=len(fc_policy_layers) + 1,
sigma_type=self.sigma_type,
fixed_sigma_value=self.fixed_sigma_value,
activation=activation,
norm_type=None,
bound_type=self.bound_type
)
else:
self.fc_policy_head = MLP(
in_channels=self.flatten_output_size_for_policy_head,
hidden_channels=fc_policy_layers[0],
out_channels=action_space_size,
layer_num=len(fc_policy_layers) + 1,
activation=activation,
norm_type=self.norm_type,
output_activation=False,
output_norm=False,
# last_linear_layer_init_zero=True is beneficial for convergence speed.
last_linear_layer_init_zero=last_linear_layer_init_zero
)
[docs] def forward(self, latent_state: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Overview:
Forward computation of the prediction network.
Arguments:
- latent_state (:obj:`torch.Tensor`): input tensor with shape (B, in_channels).
Returns:
- policy (:obj:`torch.Tensor`): policy tensor. If action space is discrete, shape is (B, action_space_size).
If action space is continuous, shape is (B, action_space_size * 2).
- value (:obj:`torch.Tensor`): value tensor with shape (B, output_support_size).
"""
for res_block in self.resblocks:
latent_state = res_block(latent_state)
value = self.conv1x1_value(latent_state)
value = self.norm_value(value)
value = self.activation(value)
policy = self.conv1x1_policy(latent_state)
policy = self.norm_policy(policy)
policy = self.activation(policy)
value = value.reshape(-1, self.flatten_output_size_for_value_head)
policy = policy.reshape(-1, self.flatten_output_size_for_policy_head)
value = self.fc_value_head(value)
# sampled related core code
policy = self.fc_policy_head(policy)
if self.continuous_action_space:
policy = torch.cat([policy['mu'], policy['sigma']], dim=-1)
return policy, value