Source code for ding.model.template.mavac
from typing import Union, Dict, Tuple, Optional
import torch
import torch.nn as nn
from ding.utils import SequenceType, squeeze, MODEL_REGISTRY
from ..common import ReparameterizationHead, RegressionHead, DiscreteHead
[docs]@MODEL_REGISTRY.register('mavac')
class MAVAC(nn.Module):
"""
Overview:
The neural network and computation graph of algorithms related to (state) Value Actor-Critic (VAC) for \
multi-agent, such as MAPPO(https://arxiv.org/abs/2103.01955). This model now supports discrete and \
continuous action space. The MAVAC 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.
Interfaces:
``__init__``, ``forward``, ``compute_actor``, ``compute_critic``, ``compute_actor_critic``.
"""
mode = ['compute_actor', 'compute_critic', 'compute_actor_critic']
[docs] def __init__(
self,
agent_obs_shape: Union[int, SequenceType],
global_obs_shape: Union[int, SequenceType],
action_shape: Union[int, SequenceType],
agent_num: int,
actor_head_hidden_size: int = 256,
actor_head_layer_num: int = 2,
critic_head_hidden_size: int = 512,
critic_head_layer_num: int = 1,
action_space: str = 'discrete',
activation: Optional[nn.Module] = nn.ReLU(),
norm_type: Optional[str] = None,
sigma_type: Optional[str] = 'independent',
bound_type: Optional[str] = None,
encoder: Optional[Tuple[torch.nn.Module, torch.nn.Module]] = None,
) -> None:
"""
Overview:
Init the MAVAC Model according to arguments.
Arguments:
- agent_obs_shape (:obj:`Union[int, SequenceType]`): Observation's space for single agent, \
such as 8 or [4, 84, 84].
- global_obs_shape (:obj:`Union[int, SequenceType]`): Global observation's space, such as 8 or [4, 84, 84].
- action_shape (:obj:`Union[int, SequenceType]`): Action space shape for single agent, such as 6 \
or [2, 3, 3].
- agent_num (:obj:`int`): This parameter is temporarily reserved. This parameter may be required for \
subsequent changes to the model
- actor_head_hidden_size (:obj:`Optional[int]`): The ``hidden_size`` of ``actor_head`` network, defaults \
to 256, it must match the last element of ``agent_obs_shape``.
- 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 512, it must match the last element of ``global_obs_shape``.
- critic_head_layer_num (:obj:`int`): The num of layers used in the network to compute Q value output for \
critic's nn.
- action_space (:obj:`Union[int, SequenceType]`): The type of different action spaces, including \
['discrete', 'continuous'], then will instantiate corresponding head, including ``DiscreteHead`` \
and ``ReparameterizationHead``.
- activation (:obj:`Optional[nn.Module]`): The type of activation function to use in ``MLP`` the after \
``layer_fn``, if ``None`` then default set 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 MAPPO, it defaults \
to ``independent``, which means state-independent sigma parameters.
- 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[Tuple[torch.nn.Module, torch.nn.Module]]`): The encoder module list, defaults \
to ``None``, you can define your own actor and critic encoder module and pass it into MAVAC to \
deal with different observation space.
"""
super(MAVAC, self).__init__()
agent_obs_shape: int = squeeze(agent_obs_shape)
global_obs_shape: int = squeeze(global_obs_shape)
action_shape: int = squeeze(action_shape)
self.global_obs_shape, self.agent_obs_shape, self.action_shape = global_obs_shape, agent_obs_shape, action_shape
self.action_space = action_space
# Encoder Type
if encoder:
self.actor_encoder, self.critic_encoder = encoder
else:
# We directly connect the Head after a Liner layer instead of using the 3-layer FCEncoder.
# In SMAC task it can obviously improve the performance.
# Users can change the model according to their own needs.
self.actor_encoder = nn.Sequential(
nn.Linear(agent_obs_shape, actor_head_hidden_size),
activation,
)
self.critic_encoder = nn.Sequential(
nn.Linear(global_obs_shape, 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
)
assert self.action_space in ['discrete', 'continuous'], self.action_space
if self.action_space == 'discrete':
self.actor_head = DiscreteHead(
actor_head_hidden_size, action_shape, actor_head_layer_num, activation=activation, norm_type=norm_type
)
elif self.action_space == 'continuous':
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
)
# must use list, not nn.ModuleList
self.actor = [self.actor_encoder, self.actor_head]
self.critic = [self.critic_encoder, self.critic_head]
# for convenience of call some apis(such as: 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, inputs: Union[torch.Tensor, Dict], mode: str) -> Dict:
"""
Overview:
MAVAC forward computation graph, input observation tensor to predict state value or action logit. \
``mode`` includes ``compute_actor``, ``compute_critic``, ``compute_actor_critic``.
Different ``mode`` will forward with different network modules to get different outputs and save \
computation.
Arguments:
- inputs (:obj:`Dict`): The input dict including observation and related info, \
whose key-values vary from different ``mode``.
- 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 MAVAC's forward computation graph, whose key-values vary from \
different ``mode``.
Examples (Actor):
>>> model = MAVAC(agent_obs_shape=64, global_obs_shape=128, action_shape=14)
>>> inputs = {
'agent_state': torch.randn(10, 8, 64),
'global_state': torch.randn(10, 8, 128),
'action_mask': torch.randint(0, 2, size=(10, 8, 14))
}
>>> actor_outputs = model(inputs,'compute_actor')
>>> assert actor_outputs['logit'].shape == torch.Size([10, 8, 14])
Examples (Critic):
>>> model = MAVAC(agent_obs_shape=64, global_obs_shape=128, action_shape=14)
>>> inputs = {
'agent_state': torch.randn(10, 8, 64),
'global_state': torch.randn(10, 8, 128),
'action_mask': torch.randint(0, 2, size=(10, 8, 14))
}
>>> critic_outputs = model(inputs,'compute_critic')
>>> assert actor_outputs['value'].shape == torch.Size([10, 8])
Examples (Actor-Critic):
>>> model = MAVAC(64, 64)
>>> inputs = {
'agent_state': torch.randn(10, 8, 64),
'global_state': torch.randn(10, 8, 128),
'action_mask': torch.randint(0, 2, size=(10, 8, 14))
}
>>> outputs = model(inputs,'compute_actor_critic')
>>> assert outputs['value'].shape == torch.Size([10, 8, 14])
>>> assert outputs['logit'].shape == torch.Size([10, 8])
"""
assert mode in self.mode, "not support forward mode: {}/{}".format(mode, self.mode)
return getattr(self, mode)(inputs)
[docs] def compute_actor(self, x: Dict) -> Dict:
"""
Overview:
MAVAC forward computation graph for actor part, \
predicting action logit with agent observation tensor in ``x``.
Arguments:
- x (:obj:`Dict`): Input data dict with keys ['agent_state', 'action_mask'(optional)].
- agent_state: (:obj:`torch.Tensor`): Each agent local state(obs).
- action_mask(optional): (:obj:`torch.Tensor`): When ``action_space`` is discrete, action_mask needs \
to be provided to mask illegal actions.
Returns:
- outputs (:obj:`Dict`): The output dict of the 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.
Shapes:
- logit (:obj:`torch.FloatTensor`): :math:`(B, M, N)`, where B is batch size and N is ``action_shape`` \
and M is ``agent_num``.
Examples:
>>> model = MAVAC(agent_obs_shape=64, global_obs_shape=128, action_shape=14)
>>> inputs = {
'agent_state': torch.randn(10, 8, 64),
'global_state': torch.randn(10, 8, 128),
'action_mask': torch.randint(0, 2, size=(10, 8, 14))
}
>>> actor_outputs = model(inputs,'compute_actor')
>>> assert actor_outputs['logit'].shape == torch.Size([10, 8, 14])
"""
if self.action_space == 'discrete':
action_mask = x['action_mask']
x = x['agent_state']
x = self.actor_encoder(x)
x = self.actor_head(x)
logit = x['logit']
logit[action_mask == 0.0] = -99999999
elif self.action_space == 'continuous':
x = x['agent_state']
x = self.actor_encoder(x)
x = self.actor_head(x)
logit = x
return {'logit': logit}
[docs] def compute_critic(self, x: Dict) -> Dict:
"""
Overview:
MAVAC forward computation graph for critic part. \
Predict state value with global observation tensor in ``x``.
Arguments:
- x (:obj:`Dict`): Input data dict with keys ['global_state'].
- global_state: (:obj:`torch.Tensor`): Global state(obs).
Returns:
- outputs (:obj:`Dict`): The output dict of MAVAC's forward computation graph for critic, \
including ``value``.
ReturnsKeys:
- value (:obj:`torch.Tensor`): The predicted state value tensor.
Shapes:
- value (:obj:`torch.FloatTensor`): :math:`(B, M)`, where B is batch size and M is ``agent_num``.
Examples:
>>> model = MAVAC(agent_obs_shape=64, global_obs_shape=128, action_shape=14)
>>> inputs = {
'agent_state': torch.randn(10, 8, 64),
'global_state': torch.randn(10, 8, 128),
'action_mask': torch.randint(0, 2, size=(10, 8, 14))
}
>>> critic_outputs = model(inputs,'compute_critic')
>>> assert critic_outputs['value'].shape == torch.Size([10, 8])
"""
x = self.critic_encoder(x['global_state'])
x = self.critic_head(x)
return {'value': x['pred']}
[docs] def compute_actor_critic(self, x: Dict) -> Dict:
"""
Overview:
MAVAC forward computation graph for both actor and critic part, input observation to predict action \
logit and state value.
Arguments:
- x (:obj:`Dict`): The input dict contains ``agent_state``, ``global_state`` and other related info.
Returns:
- outputs (:obj:`Dict`): The output dict of MAVAC's forward computation graph for both actor and critic, \
including ``logit`` and ``value``.
ReturnsKeys:
- logit (:obj:`torch.Tensor`): Logit encoding tensor, with same size as input ``x``.
- value (:obj:`torch.Tensor`): Q value tensor with same size as batch size.
Shapes:
- logit (:obj:`torch.FloatTensor`): :math:`(B, M, N)`, where B is batch size and N is ``action_shape`` \
and M is ``agent_num``.
- value (:obj:`torch.FloatTensor`): :math:`(B, M)`, where B is batch sizeand M is ``agent_num``.
Examples:
>>> model = MAVAC(64, 64)
>>> inputs = {
'agent_state': torch.randn(10, 8, 64),
'global_state': torch.randn(10, 8, 128),
'action_mask': torch.randint(0, 2, size=(10, 8, 14))
}
>>> outputs = model(inputs,'compute_actor_critic')
>>> assert outputs['value'].shape == torch.Size([10, 8])
>>> assert outputs['logit'].shape == torch.Size([10, 8, 14])
"""
x_actor = self.actor_encoder(x['agent_state'])
x_critic = self.critic_encoder(x['global_state'])
if self.action_space == 'discrete':
action_mask = x['action_mask']
x = self.actor_head(x_actor)
logit = x['logit']
logit[action_mask == 0.0] = -99999999
elif self.action_space == 'continuous':
x = self.actor_head(x_actor)
logit = x
value = self.critic_head(x_critic)['pred']
return {'logit': logit, 'value': value}