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from typing import Union, Optional
from easydict import EasyDict
import torch
import torch.nn as nn
import treetensor.torch as ttorch
from copy import deepcopy
from ding.utils import SequenceType, squeeze
from ding.model.common import ReparameterizationHead, RegressionHead, MultiHead, \
FCEncoder, ConvEncoder, IMPALAConvEncoder, PopArtVHead
from ding.torch_utils import MLP, fc_block
class DiscretePolicyHead(nn.Module):
def __init__(
self,
hidden_size: int,
output_size: int,
layer_num: int = 1,
activation: Optional[nn.Module] = nn.ReLU(),
norm_type: Optional[str] = None,
) -> None:
super(DiscretePolicyHead, self).__init__()
self.main = nn.Sequential(
MLP(
hidden_size,
hidden_size,
hidden_size,
layer_num,
layer_fn=nn.Linear,
activation=activation,
norm_type=norm_type
), fc_block(hidden_size, output_size)
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.main(x)
class PPOFModel(nn.Module):
mode = ['compute_actor', 'compute_critic', 'compute_actor_critic']
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,
popart_head=False,
) -> None:
super(PPOFModel, self).__init__()
obs_shape = squeeze(obs_shape)
action_shape = squeeze(action_shape)
self.obs_shape, self.action_shape = obs_shape, action_shape
self.share_encoder = share_encoder
# Encoder Type
def new_encoder(outsize):
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)
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)
self.critic_encoder = new_encoder(critic_head_hidden_size)
# Head Type
if not popart_head:
self.critic_head = RegressionHead(
critic_head_hidden_size, 1, critic_head_layer_num, activation=activation, norm_type=norm_type
)
else:
self.critic_head = PopArtVHead(
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 = DiscretePolicyHead
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 = DiscretePolicyHead(
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])
# must use list, not nn.ModuleList
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)
def forward(self, inputs: ttorch.Tensor, mode: str) -> ttorch.Tensor:
assert mode in self.mode, "not support forward mode: {}/{}".format(mode, self.mode)
return getattr(self, mode)(inputs)
def compute_actor(self, x: ttorch.Tensor) -> ttorch.Tensor:
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 ttorch.as_tensor(x)
elif self.action_space == 'hybrid':
action_type = self.actor_head[0](x)
action_args = self.actor_head[1](x)
return ttorch.as_tensor({'action_type': action_type, 'action_args': action_args})
def compute_critic(self, x: ttorch.Tensor) -> ttorch.Tensor:
if self.share_encoder:
x = self.encoder(x)
else:
x = self.critic_encoder(x)
x = self.critic_head(x)
return x
def compute_actor_critic(self, x: ttorch.Tensor) -> ttorch.Tensor:
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)
if self.action_space == 'discrete':
logit = self.actor_head(actor_embedding)
return ttorch.as_tensor({'logit': logit, 'value': value['pred']})
elif self.action_space == 'continuous':
x = self.actor_head(actor_embedding)
return ttorch.as_tensor({'logit': x, 'value': value['pred']})
elif self.action_space == 'hybrid':
action_type = self.actor_head[0](actor_embedding)
action_args = self.actor_head[1](actor_embedding)
return ttorch.as_tensor(
{
'logit': {
'action_type': action_type,
'action_args': action_args
},
'value': value['pred']
}
)
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