Source code for ding.model.template.vac
from typing import Union, Dict, Optional
from easydict import EasyDict
import torch
import torch.nn as nn
from copy import deepcopy
from ding.utils import SequenceType, squeeze, MODEL_REGISTRY
from ..common import ReparameterizationHead, RegressionHead, DiscreteHead, MultiHead, \
FCEncoder, ConvEncoder, IMPALAConvEncoder
from ding.torch_utils.network.dreamer import ActionHead, DenseHead
[docs]@MODEL_REGISTRY.register('vac')
class VAC(nn.Module):
"""
Overview:
The neural network and computation graph of algorithms related to (state) Value Actor-Critic (VAC), such as \
A2C/PPO/IMPALA. This model now supports discrete, continuous and hybrid action space. The VAC is composed of \
four parts: ``actor_encoder``, ``critic_encoder``, ``actor_head`` and ``critic_head``. Encoders are used to \
extract the feature from various observation. Heads are used to predict corresponding value or action logit. \
In high-dimensional observation space like 2D image, we often use a shared encoder for both ``actor_encoder`` \
and ``critic_encoder``. In low-dimensional observation space like 1D vector, we often use different encoders.
Interfaces:
``__init__``, ``forward``, ``compute_actor``, ``compute_critic``, ``compute_actor_critic``.
"""
mode = ['compute_actor', 'compute_critic', 'compute_actor_critic']
[docs] def __init__(
self,
obs_shape: Union[int, SequenceType],
action_shape: Union[int, SequenceType, EasyDict],
action_space: str = 'discrete',
share_encoder: bool = True,
encoder_hidden_size_list: SequenceType = [128, 128, 64],
actor_head_hidden_size: int = 64,
actor_head_layer_num: int = 1,
critic_head_hidden_size: int = 64,
critic_head_layer_num: int = 1,
activation: Optional[nn.Module] = nn.ReLU(),
norm_type: Optional[str] = None,
sigma_type: Optional[str] = 'independent',
fixed_sigma_value: Optional[int] = 0.3,
bound_type: Optional[str] = None,
encoder: Optional[torch.nn.Module] = None,
impala_cnn_encoder: bool = False,
) -> None:
"""
Overview:
Initialize the VAC model according to corresponding input arguments.
Arguments:
- obs_shape (:obj:`Union[int, SequenceType]`): Observation space shape, such as 8 or [4, 84, 84].
- action_shape (:obj:`Union[int, SequenceType]`): Action space shape, such as 6 or [2, 3, 3].
- action_space (:obj:`str`): The type of different action spaces, including ['discrete', 'continuous', \
'hybrid'], then will instantiate corresponding head, including ``DiscreteHead``, \
``ReparameterizationHead``, and hybrid heads.
- share_encoder (:obj:`bool`): Whether to share observation encoders between actor and decoder.
- encoder_hidden_size_list (:obj:`SequenceType`): Collection of ``hidden_size`` to pass to ``Encoder``, \
the last element must match ``head_hidden_size``.
- actor_head_hidden_size (:obj:`Optional[int]`): The ``hidden_size`` of ``actor_head`` network, defaults \
to 64, it must match the last element of ``encoder_hidden_size_list``.
- actor_head_layer_num (:obj:`int`): The num of layers used in the ``actor_head`` network to compute action.
- critic_head_hidden_size (:obj:`Optional[int]`): The ``hidden_size`` of ``critic_head`` network, defaults \
to 64, it must match the last element of ``encoder_hidden_size_list``.
- critic_head_layer_num (:obj:`int`): The num of layers used in the ``critic_head`` network.
- activation (:obj:`Optional[nn.Module]`): The type of activation function in networks \
if ``None`` then default set it to ``nn.ReLU()``.
- norm_type (:obj:`Optional[str]`): The type of normalization in networks, see \
``ding.torch_utils.fc_block`` for more details. you can choose one of ['BN', 'IN', 'SyncBN', 'LN']
- sigma_type (:obj:`Optional[str]`): The type of sigma in continuous action space, see \
``ding.torch_utils.network.dreamer.ReparameterizationHead`` for more details, in A2C/PPO, it defaults \
to ``independent``, which means state-independent sigma parameters.
- fixed_sigma_value (:obj:`Optional[int]`): If ``sigma_type`` is ``fixed``, then use this value as sigma.
- bound_type (:obj:`Optional[str]`): The type of action bound methods in continuous action space, defaults \
to ``None``, which means no bound.
- encoder (:obj:`Optional[torch.nn.Module]`): The encoder module, defaults to ``None``, you can define \
your own encoder module and pass it into VAC to deal with different observation space.
- impala_cnn_encoder (:obj:`bool`): Whether to use IMPALA CNN encoder, defaults to ``False``.
"""
super(VAC, self).__init__()
obs_shape: int = squeeze(obs_shape)
action_shape = squeeze(action_shape)
self.obs_shape, self.action_shape = obs_shape, action_shape
self.impala_cnn_encoder = impala_cnn_encoder
self.share_encoder = share_encoder
# Encoder Type
def new_encoder(outsize, activation):
if impala_cnn_encoder:
return IMPALAConvEncoder(obs_shape=obs_shape, channels=encoder_hidden_size_list, outsize=outsize)
else:
if isinstance(obs_shape, int) or len(obs_shape) == 1:
return FCEncoder(
obs_shape=obs_shape,
hidden_size_list=encoder_hidden_size_list,
activation=activation,
norm_type=norm_type
)
elif len(obs_shape) == 3:
return ConvEncoder(
obs_shape=obs_shape,
hidden_size_list=encoder_hidden_size_list,
activation=activation,
norm_type=norm_type
)
else:
raise RuntimeError(
"not support obs_shape for pre-defined encoder: {}, please customize your own encoder".
format(obs_shape)
)
if self.share_encoder:
assert actor_head_hidden_size == critic_head_hidden_size, \
"actor and critic network head should have same size."
if encoder:
if isinstance(encoder, torch.nn.Module):
self.encoder = encoder
else:
raise ValueError("illegal encoder instance.")
else:
self.encoder = new_encoder(actor_head_hidden_size, activation)
else:
if encoder:
if isinstance(encoder, torch.nn.Module):
self.actor_encoder = encoder
self.critic_encoder = deepcopy(encoder)
else:
raise ValueError("illegal encoder instance.")
else:
self.actor_encoder = new_encoder(actor_head_hidden_size, activation)
self.critic_encoder = new_encoder(critic_head_hidden_size, activation)
# Head Type
self.critic_head = RegressionHead(
critic_head_hidden_size, 1, critic_head_layer_num, activation=activation, norm_type=norm_type
)
self.action_space = action_space
assert self.action_space in ['discrete', 'continuous', 'hybrid'], self.action_space
if self.action_space == 'continuous':
self.multi_head = False
self.actor_head = ReparameterizationHead(
actor_head_hidden_size,
action_shape,
actor_head_layer_num,
sigma_type=sigma_type,
activation=activation,
norm_type=norm_type,
bound_type=bound_type
)
elif self.action_space == 'discrete':
actor_head_cls = DiscreteHead
multi_head = not isinstance(action_shape, int)
self.multi_head = multi_head
if multi_head:
self.actor_head = MultiHead(
actor_head_cls,
actor_head_hidden_size,
action_shape,
layer_num=actor_head_layer_num,
activation=activation,
norm_type=norm_type
)
else:
self.actor_head = actor_head_cls(
actor_head_hidden_size,
action_shape,
actor_head_layer_num,
activation=activation,
norm_type=norm_type
)
elif self.action_space == 'hybrid': # HPPO
# hybrid action space: action_type(discrete) + action_args(continuous),
# such as {'action_type_shape': torch.LongTensor([0]), 'action_args_shape': torch.FloatTensor([0.1, -0.27])}
action_shape.action_args_shape = squeeze(action_shape.action_args_shape)
action_shape.action_type_shape = squeeze(action_shape.action_type_shape)
actor_action_args = ReparameterizationHead(
actor_head_hidden_size,
action_shape.action_args_shape,
actor_head_layer_num,
sigma_type=sigma_type,
fixed_sigma_value=fixed_sigma_value,
activation=activation,
norm_type=norm_type,
bound_type=bound_type,
)
actor_action_type = DiscreteHead(
actor_head_hidden_size,
action_shape.action_type_shape,
actor_head_layer_num,
activation=activation,
norm_type=norm_type,
)
self.actor_head = nn.ModuleList([actor_action_type, actor_action_args])
if self.share_encoder:
self.actor = [self.encoder, self.actor_head]
self.critic = [self.encoder, self.critic_head]
else:
self.actor = [self.actor_encoder, self.actor_head]
self.critic = [self.critic_encoder, self.critic_head]
# Convenient for calling some apis (e.g. self.critic.parameters()),
# but may cause misunderstanding when `print(self)`
self.actor = nn.ModuleList(self.actor)
self.critic = nn.ModuleList(self.critic)
[docs] def forward(self, x: torch.Tensor, mode: str) -> Dict:
"""
Overview:
VAC forward computation graph, input observation tensor to predict state value or action logit. Different \
``mode`` will forward with different network modules to get different outputs and save computation.
Arguments:
- x (:obj:`torch.Tensor`): The input observation tensor data.
- mode (:obj:`str`): The forward mode, all the modes are defined in the beginning of this class.
Returns:
- outputs (:obj:`Dict`): The output dict of VAC's forward computation graph, whose key-values vary from \
different ``mode``.
Examples (Actor):
>>> model = VAC(64, 128)
>>> inputs = torch.randn(4, 64)
>>> actor_outputs = model(inputs,'compute_actor')
>>> assert actor_outputs['logit'].shape == torch.Size([4, 128])
Examples (Critic):
>>> model = VAC(64, 64)
>>> inputs = torch.randn(4, 64)
>>> critic_outputs = model(inputs,'compute_critic')
>>> assert actor_outputs['logit'].shape == torch.Size([4, 64])
Examples (Actor-Critic):
>>> model = VAC(64, 64)
>>> inputs = torch.randn(4, 64)
>>> outputs = model(inputs,'compute_actor_critic')
>>> assert critic_outputs['value'].shape == torch.Size([4])
>>> assert outputs['logit'].shape == torch.Size([4, 64])
"""
assert mode in self.mode, "not support forward mode: {}/{}".format(mode, self.mode)
return getattr(self, mode)(x)
[docs] def compute_actor(self, x: torch.Tensor) -> Dict:
"""
Overview:
VAC forward computation graph for actor part, input observation tensor to predict action logit.
Arguments:
- x (:obj:`torch.Tensor`): The input observation tensor data.
Returns:
- outputs (:obj:`Dict`): The output dict of VAC's forward computation graph for actor, including ``logit``.
ReturnsKeys:
- logit (:obj:`torch.Tensor`): The predicted action logit tensor, for discrete action space, it will be \
the same dimension real-value ranged tensor of possible action choices, and for continuous action \
space, it will be the mu and sigma of the Gaussian distribution, and the number of mu and sigma is the \
same as the number of continuous actions. Hybrid action space is a kind of combination of discrete \
and continuous action space, so the logit will be a dict with ``action_type`` and ``action_args``.
Shapes:
- logit (:obj:`torch.Tensor`): :math:`(B, N)`, where B is batch size and N is ``action_shape``
Examples:
>>> model = VAC(64, 64)
>>> inputs = torch.randn(4, 64)
>>> actor_outputs = model(inputs,'compute_actor')
>>> assert actor_outputs['logit'].shape == torch.Size([4, 64])
"""
if self.share_encoder:
x = self.encoder(x)
else:
x = self.actor_encoder(x)
if self.action_space == 'discrete':
return self.actor_head(x)
elif self.action_space == 'continuous':
x = self.actor_head(x) # mu, sigma
return {'logit': x}
elif self.action_space == 'hybrid':
action_type = self.actor_head[0](x)
action_args = self.actor_head[1](x)
return {'logit': {'action_type': action_type['logit'], 'action_args': action_args}}
[docs] def compute_critic(self, x: torch.Tensor) -> Dict:
"""
Overview:
VAC forward computation graph for critic part, input observation tensor to predict state value.
Arguments:
- x (:obj:`torch.Tensor`): The input observation tensor data.
Returns:
- outputs (:obj:`Dict`): The output dict of VAC's forward computation graph for critic, including ``value``.
ReturnsKeys:
- value (:obj:`torch.Tensor`): The predicted state value tensor.
Shapes:
- value (:obj:`torch.Tensor`): :math:`(B, )`, where B is batch size, (B, 1) is squeezed to (B, ).
Examples:
>>> model = VAC(64, 64)
>>> inputs = torch.randn(4, 64)
>>> critic_outputs = model(inputs,'compute_critic')
>>> assert critic_outputs['value'].shape == torch.Size([4])
"""
if self.share_encoder:
x = self.encoder(x)
else:
x = self.critic_encoder(x)
x = self.critic_head(x)
return {'value': x['pred']}
[docs] def compute_actor_critic(self, x: torch.Tensor) -> Dict:
"""
Overview:
VAC forward computation graph for both actor and critic part, input observation tensor to predict action \
logit and state value.
Arguments:
- x (:obj:`torch.Tensor`): The input observation tensor data.
Returns:
- outputs (:obj:`Dict`): The output dict of VAC's forward computation graph for both actor and critic, \
including ``logit`` and ``value``.
ReturnsKeys:
- logit (:obj:`torch.Tensor`): The predicted action logit tensor, for discrete action space, it will be \
the same dimension real-value ranged tensor of possible action choices, and for continuous action \
space, it will be the mu and sigma of the Gaussian distribution, and the number of mu and sigma is the \
same as the number of continuous actions. Hybrid action space is a kind of combination of discrete \
and continuous action space, so the logit will be a dict with ``action_type`` and ``action_args``.
- value (:obj:`torch.Tensor`): The predicted state value tensor.
Shapes:
- logit (:obj:`torch.Tensor`): :math:`(B, N)`, where B is batch size and N is ``action_shape``
- value (:obj:`torch.Tensor`): :math:`(B, )`, where B is batch size, (B, 1) is squeezed to (B, ).
Examples:
>>> model = VAC(64, 64)
>>> inputs = torch.randn(4, 64)
>>> outputs = model(inputs,'compute_actor_critic')
>>> assert critic_outputs['value'].shape == torch.Size([4])
>>> assert outputs['logit'].shape == torch.Size([4, 64])
.. note::
``compute_actor_critic`` interface aims to save computation when shares encoder and return the combination \
dict output.
"""
if self.share_encoder:
actor_embedding = critic_embedding = self.encoder(x)
else:
actor_embedding = self.actor_encoder(x)
critic_embedding = self.critic_encoder(x)
value = self.critic_head(critic_embedding)['pred']
if self.action_space == 'discrete':
logit = self.actor_head(actor_embedding)['logit']
return {'logit': logit, 'value': value}
elif self.action_space == 'continuous':
x = self.actor_head(actor_embedding)
return {'logit': x, 'value': value}
elif self.action_space == 'hybrid':
action_type = self.actor_head[0](actor_embedding)
action_args = self.actor_head[1](actor_embedding)
return {'logit': {'action_type': action_type['logit'], 'action_args': action_args}, 'value': value}
[docs]@MODEL_REGISTRY.register('dreamervac')
class DREAMERVAC(nn.Module):
"""
Overview:
The neural network and computation graph of DreamerV3 (state) Value Actor-Critic (VAC).
This model now supports discrete, continuous action space.
Interfaces:
``__init__``, ``forward``.
"""
mode = ['compute_actor', 'compute_critic', 'compute_actor_critic']
[docs] def __init__(
self,
obs_shape: Union[int, SequenceType],
action_shape: Union[int, SequenceType, EasyDict],
dyn_stoch=32,
dyn_deter=512,
dyn_discrete=32,
actor_layers=2,
value_layers=2,
units=512,
act='SiLU',
norm='LayerNorm',
actor_dist='normal',
actor_init_std=1.0,
actor_min_std=0.1,
actor_max_std=1.0,
actor_temp=0.1,
action_unimix_ratio=0.01,
) -> None:
"""
Overview:
Initialize the ``DREAMERVAC`` model according to arguments.
Arguments:
- obs_shape (:obj:`Union[int, SequenceType]`): Observation space shape, such as 8 or [4, 84, 84].
- action_shape (:obj:`Union[int, SequenceType]`): Action space shape, such as 6 or [2, 3, 3].
"""
super(DREAMERVAC, self).__init__()
obs_shape: int = squeeze(obs_shape)
action_shape = squeeze(action_shape)
self.obs_shape, self.action_shape = obs_shape, action_shape
if dyn_discrete:
feat_size = dyn_stoch * dyn_discrete + dyn_deter
else:
feat_size = dyn_stoch + dyn_deter
self.actor = ActionHead(
feat_size, # pytorch version
action_shape,
actor_layers,
units,
act,
norm,
actor_dist,
actor_init_std,
actor_min_std,
actor_max_std,
actor_temp,
outscale=1.0,
unimix_ratio=action_unimix_ratio,
)
self.critic = DenseHead(
feat_size, # pytorch version
(255, ),
value_layers,
units,
'SiLU', # act
'LN', # norm
'twohot_symlog',
outscale=0.0,
device='cuda' if torch.cuda.is_available() else 'cpu',
)