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import sys
import numpy as np
import torch
from torch import nn
sys.path.insert(0, sys.path[0]+"/../")
from typing import (
Any,
Dict,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
no_type_check,
)
import torch.nn as nn
from tianshou.utils.net.discrete import NoisyLinear
ModuleType = Type[nn.Module]
import random
from collections import namedtuple, deque
from itertools import count
import math
import torch
import torch.optim as optim
from transformers import AutoModel, AutoTokenizer
import torch.nn.functional as F
from tianshou.utils.net.common import ModuleType, Net, MLP
def bert_embedding(x, max_length=512, device='cuda'):
from transformers import logging
logging.set_verbosity_error()
model_name = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(model_name)
bert_model = AutoModel.from_pretrained(model_name)
text = x
if isinstance(text, np.ndarray):
text = list(text)
tokens = tokenizer(text, max_length=max_length, padding='max_length', truncation=True, return_tensors='pt')
input_ids = tokens['input_ids']
attention_mask = tokens['attention_mask']
with torch.no_grad():
outputs = bert_model(input_ids, attention_mask=attention_mask)
embeddings = outputs.last_hidden_state
return embeddings
class Net_GRU(nn.Module):
def __init__(self, input_size, n_actions, hidden_dim, n_layers, dropout, bidirectional):
super(Net_GRU, self).__init__()
self.input_size = input_size
self.hidden_dim = hidden_dim
self.num_classes = n_actions
self.n_layers = n_layers
self.dropout = dropout
self.bidirectional = bidirectional
# Layers
self.gru = nn.GRU(self.input_size, self.hidden_dim, self.n_layers,
batch_first=True, dropout=self.dropout, bidirectional=self.bidirectional)
self.final_layer = nn.Linear(self.hidden_dim*(1 + int(self.bidirectional)), self.num_classes)
def forward(self, x):
# Input shape: (batch_size, seq_length)
batch_size, seq_length, emb_size = x.size()
gru_out, hidden = self.gru(x)
# Use the final state
# hidden -> (num_direction, batch, hidden_size)
if self.bidirectional:
hidden = hidden.view(self.n_layers, 2, batch_size, self.hidden_dim)
final_hidden = torch.cat((hidden[:, -1, :, :].squeeze(0), hidden[:, 0, :, :].squeeze(0)), 1)
else:
final_hidden = hidden.squeeze(0)
# final_hidden -> (batch_size, num_classes)
logits = self.final_layer(final_hidden)
return logits
class MyGRU(nn.Module):
def __init__(self, input_size, hidden_dim, n_layers, dropout, bidirectional, output_dim):
super(MyGRU, self).__init__()
self.input_size = input_size
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.dropout = dropout
self.bidirectional = bidirectional
# Layers
self.gru = nn.GRU(self.input_size, self.hidden_dim, self.n_layers,
batch_first=True, dropout=self.dropout, bidirectional=self.bidirectional)
self.final_layer = nn.Linear(self.hidden_dim*(1 + int(self.bidirectional)), output_dim)
def forward(self, x):
batch_size, seq_length, emb_size = x.size()
gru_out, hidden = self.gru(x)
# Use the final state
# hidden -> (num_direction, batch, hidden_size)
if self.bidirectional:
hidden = hidden.view(self.n_layers, 2, batch_size, self.hidden_dim)
final_hidden = torch.cat((hidden[:, -1, :, :].squeeze(0), hidden[:, 0, :, :].squeeze(0)), 1)
else:
final_hidden = hidden.squeeze(0)
# final_hidden -> (batch_size, num_classes)
logits = self.final_layer(final_hidden)
return logits
class MyCNN(nn.Module):
def __init__(self,
input_dim: int,
output_dim: int = 0,
hidden_sizes: Sequence[int] = (),
norm_layer: Optional[Union[ModuleType, Sequence[ModuleType]]] = None,
activation: ModuleType = nn.ReLU,
device: Optional[Union[str, int, torch.device]] = None,
linear_layer: Type[nn.Linear] = nn.Linear,
flatten_input: bool = True,) -> None:
super().__init__()
self.model = []
input_dim_temp = input_dim
for h in hidden_sizes:
self.model.append(nn.Conv1d(in_channels=input_dim_temp, out_channels=h, kernel_size=3, padding=1))
self.model.append(activation())
self.model.append(nn.MaxPool1d(kernel_size=2))
input_dim_temp = h
self.model = nn.Sequential(*self.model)
self.fc = nn.Linear(in_features=input_dim_temp, out_features=output_dim)
def forward(self, x):
x = self.model(x.transpose(1, 2))
x.transpose_(1, 2)
x = self.fc(x)
return x
class Net_GRU_Bert_tianshou(Net):
def __init__(
self,
state_shape: Union[int, Sequence[int]],
action_shape: Union[int, Sequence[int]] = 0,
hidden_sizes: Sequence[int] = (),
norm_layer: Optional[ModuleType] = None,
activation: Optional[ModuleType] = nn.ReLU,
device: Union[str, int, torch.device] = "cpu",
softmax: bool = False,
concat: bool = False,
num_atoms: int = 1,
dueling_param: Optional[Tuple[Dict[str, Any], Dict[str, Any]]] = None,
linear_layer: Type[nn.Linear] = nn.Linear,
hidden_dim: int = 128,
bidirectional: bool = True,
dropout: float = 0.,
n_layers: int = 1,
max_length: int = 512,
trans_model_name: str = 'bert-base-uncased',
) -> None:
nn.Module.__init__(self)
self.device = device
self.softmax = softmax
self.num_atoms = num_atoms
self.hidden_dim = hidden_dim
self.bidirectional = bidirectional
self.dropout = dropout
self.n_layers = n_layers
self.trans_model_name = trans_model_name
self.max_length = max_length
input_dim = int(np.prod(state_shape))
action_dim = int(np.prod(action_shape)) * num_atoms
if concat:
input_dim += action_dim
self.use_dueling = dueling_param is not None
output_dim = action_dim if not self.use_dueling and not concat else 0
self.output_dim = output_dim or hidden_dim
self.model = MyGRU(768, self.hidden_dim, self.n_layers,
self.dropout, self.bidirectional, self.output_dim)
if self.use_dueling: # dueling DQN
q_kwargs, v_kwargs = dueling_param # type: ignore
q_output_dim, v_output_dim = 0, 0
if not concat:
q_output_dim, v_output_dim = action_dim, num_atoms
q_kwargs: Dict[str, Any] = {
**q_kwargs, "input_dim": self.output_dim,
"output_dim": q_output_dim,
"device": self.device
}
v_kwargs: Dict[str, Any] = {
**v_kwargs, "input_dim": self.output_dim,
"output_dim": v_output_dim,
"device": self.device
}
self.Q, self.V = MLP(**q_kwargs), MLP(**v_kwargs)
self.output_dim = self.Q.output_dim
self.bert_model = AutoModel.from_pretrained(self.trans_model_name).to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(trans_model_name)
from transformers import logging
logging.set_verbosity_error()
def bert_embedding(self, x, max_length=512):
text = x
if isinstance(text, np.ndarray):
text = list(text)
tokens = self.tokenizer(text, max_length=max_length, padding='max_length', truncation=True, return_tensors='pt')
input_ids = tokens['input_ids'].to(self.device)
attention_mask = tokens['attention_mask'].to(self.device)
with torch.no_grad():
outputs = self.bert_model(input_ids, attention_mask=attention_mask)
embeddings = outputs.last_hidden_state
return embeddings
def forward(
self,
obs: Union[np.ndarray, torch.Tensor],
state: Any = None,
info: Dict[str, Any] = {},
) -> Tuple[torch.Tensor, Any]:
"""Mapping: obs -> flatten (inside MLP)-> logits."""
embedding = self.bert_embedding(obs, max_length=self.max_length)
logits = self.model(embedding)
bsz = logits.shape[0]
if self.use_dueling: # Dueling DQN
q, v = self.Q(logits), self.V(logits)
if self.num_atoms > 1:
q = q.view(bsz, -1, self.num_atoms)
v = v.view(bsz, -1, self.num_atoms)
logits = q - q.mean(dim=1, keepdim=True) + v
elif self.num_atoms > 1:
logits = logits.view(bsz, -1, self.num_atoms)
if self.softmax:
logits = torch.softmax(logits, dim=-1)
return logits, state
class Net_Bert_CLS_tianshou(Net):
def __init__(
self,
state_shape: Union[int, Sequence[int]],
action_shape: Union[int, Sequence[int]] = 0,
hidden_sizes: Sequence[int] = (),
norm_layer: Optional[ModuleType] = None,
activation: Optional[ModuleType] = nn.ReLU,
device: Union[str, int, torch.device] = "cpu",
softmax: bool = False,
concat: bool = False,
num_atoms: int = 1,
dueling_param: Optional[Tuple[Dict[str, Any], Dict[str, Any]]] = None,
linear_layer: Type[nn.Linear] = nn.Linear,
hidden_dim: int = 128,
bidirectional: bool = True,
dropout: float = 0.,
n_layers: int = 1,
max_length: int = 512,
trans_model_name: str = 'bert-base-uncased',
) -> None:
nn.Module.__init__(self)
self.device = device
self.softmax = softmax
self.num_atoms = num_atoms
self.hidden_dim = hidden_dim
self.bidirectional = bidirectional
self.dropout = dropout
self.n_layers = n_layers
self.trans_model_name = trans_model_name
self.max_length = max_length
input_dim = int(np.prod(state_shape))
action_dim = int(np.prod(action_shape)) * num_atoms
if concat:
input_dim += action_dim
self.use_dueling = dueling_param is not None
output_dim = action_dim if not self.use_dueling and not concat else 0
self.output_dim = output_dim or hidden_dim
self.model = MLP(768, output_dim, hidden_sizes, norm_layer, activation, device, linear_layer)
if self.use_dueling: # dueling DQN
q_kwargs, v_kwargs = dueling_param # type: ignore
q_output_dim, v_output_dim = 0, 0
if not concat:
q_output_dim, v_output_dim = action_dim, num_atoms
q_kwargs: Dict[str, Any] = {
**q_kwargs, "input_dim": self.output_dim,
"output_dim": q_output_dim,
"device": self.device
}
v_kwargs: Dict[str, Any] = {
**v_kwargs, "input_dim": self.output_dim,
"output_dim": v_output_dim,
"device": self.device
}
self.Q, self.V = MLP(**q_kwargs), MLP(**v_kwargs)
self.output_dim = self.Q.output_dim
self.bert_model = AutoModel.from_pretrained(self.trans_model_name).to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(trans_model_name)
from transformers import logging
logging.set_verbosity_error()
def bert_CLS_embedding(self, x, max_length=512):
text = x
if isinstance(text, np.ndarray):
text = list(text)
tokens = self.tokenizer(text, max_length=max_length, padding='max_length', truncation=True, return_tensors='pt')
input_ids = tokens['input_ids'].to(self.device)
attention_mask = tokens['attention_mask'].to(self.device)
with torch.no_grad():
outputs = self.bert_model(input_ids, attention_mask=attention_mask)
embeddings = outputs[0][:, 0, :]
return embeddings
def forward(
self,
obs: Union[np.ndarray, torch.Tensor],
state: Any = None,
info: Dict[str, Any] = {},
) -> Tuple[torch.Tensor, Any]:
"""Mapping: obs -> flatten (inside MLP)-> logits."""
embedding = self.bert_CLS_embedding(obs, max_length=self.max_length)
logits = self.model(embedding)
bsz = logits.shape[0]
if self.use_dueling: # Dueling DQN
q, v = self.Q(logits), self.V(logits)
if self.num_atoms > 1:
q = q.view(bsz, -1, self.num_atoms)
v = v.view(bsz, -1, self.num_atoms)
logits = q - q.mean(dim=1, keepdim=True) + v
elif self.num_atoms > 1:
logits = logits.view(bsz, -1, self.num_atoms)
if self.softmax:
logits = torch.softmax(logits, dim=-1)
return logits, state
class Net_Bert_CNN_tianshou(Net_GRU_Bert_tianshou):
def __init__(
self,
state_shape: Union[int, Sequence[int]],
action_shape: Union[int, Sequence[int]] = 0,
hidden_sizes: Sequence[int] = (),
norm_layer: Optional[ModuleType] = None,
activation: Optional[ModuleType] = nn.ReLU,
device: Union[str, int, torch.device] = "cpu",
softmax: bool = False,
concat: bool = False,
num_atoms: int = 1,
dueling_param: Optional[Tuple[Dict[str, Any], Dict[str, Any]]] = None,
linear_layer: Type[nn.Linear] = nn.Linear,
hidden_dim: int = 128,
bidirectional: bool = True,
dropout: float = 0.,
n_layers: int = 1,
max_length: int = 512,
trans_model_name: str = 'bert-base-uncased',
) -> None:
nn.Module.__init__(self)
self.device = device
self.softmax = softmax
self.num_atoms = num_atoms
self.hidden_dim = hidden_dim
self.bidirectional = bidirectional
self.dropout = dropout
self.n_layers = n_layers
self.trans_model_name = trans_model_name
self.max_length = max_length
input_dim = int(np.prod(state_shape))
action_dim = int(np.prod(action_shape)) * num_atoms
if concat:
input_dim += action_dim
self.use_dueling = dueling_param is not None
output_dim = action_dim if not self.use_dueling and not concat else 0
self.output_dim = output_dim or hidden_dim
self.model = MyCNN(768, output_dim, hidden_sizes, norm_layer, activation, device, linear_layer, flatten_input=False)
if self.use_dueling: # dueling DQN
q_kwargs, v_kwargs = dueling_param # type: ignore
q_output_dim, v_output_dim = 0, 0
if not concat:
q_output_dim, v_output_dim = action_dim, num_atoms
q_kwargs: Dict[str, Any] = {
**q_kwargs, "input_dim": self.output_dim,
"output_dim": q_output_dim,
"device": self.device
}
v_kwargs: Dict[str, Any] = {
**v_kwargs, "input_dim": self.output_dim,
"output_dim": v_output_dim,
"device": self.device
}
self.Q, self.V = MLP(**q_kwargs), MLP(**v_kwargs)
self.output_dim = self.Q.output_dim
self.bert_model = AutoModel.from_pretrained(self.trans_model_name).to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(trans_model_name)
from transformers import logging
logging.set_verbosity_error()
class DQN_GRU(nn.Module):
"""Reference: Human-level control through deep reinforcement learning.
"""
def __init__(
self,
state_shape: Union[int, Sequence[int]],
action_shape: Sequence[int],
device: Union[str, int, torch.device] = "cpu",
features_only: bool = False,
output_dim: Optional[int] = None,
hidden_dim: int = 128,
n_layers: int = 1,
dropout: float = 0.,
bidirectional: bool = True,
trans_model_name: str = 'bert-base-uncased',
max_length: int = 512,
) -> None:
super().__init__()
self.device = device
self.max_length = max_length
action_dim = int(np.prod(action_shape))
self.net = MyGRU(768, hidden_dim, n_layers, dropout, bidirectional,
hidden_dim)
if not features_only:
self.net = MyGRU(768, hidden_dim, n_layers, dropout, bidirectional,
action_dim)
self.output_dim = action_dim
elif output_dim is not None:
self.net = MyGRU(768, hidden_dim, n_layers, dropout, bidirectional,
output_dim)
self.output_dim = output_dim
else:
self.net = MyGRU(768, hidden_dim, n_layers, dropout, bidirectional,
hidden_dim)
self.output_dim = hidden_dim
self.trans_model_name = trans_model_name
self.bert_model = AutoModel.from_pretrained(self.trans_model_name).to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(trans_model_name)
from transformers import logging
logging.set_verbosity_error()
def bert_embedding(self, x, max_length=512):
text = x
if isinstance(text, np.ndarray):
text = list(text)
tokens = self.tokenizer(text, max_length=max_length, padding='max_length', truncation=True, return_tensors='pt')
input_ids = tokens['input_ids'].to(self.device)
attention_mask = tokens['attention_mask'].to(self.device)
with torch.no_grad():
outputs = self.bert_model(input_ids, attention_mask=attention_mask)
embeddings = outputs.last_hidden_state
return embeddings
def forward(
self,
obs: Union[np.ndarray, torch.Tensor],
state: Optional[Any] = None,
info: Dict[str, Any] = {},
) -> Tuple[torch.Tensor, Any]:
r"""Mapping: s -> Q(s, \*)."""
embedding = self.bert_embedding(obs, max_length=self.max_length)
return self.net(embedding), state
class Rainbow_GRU(DQN_GRU):
"""Reference: Rainbow: Combining Improvements in Deep Reinforcement Learning.
"""
def __init__(
self,
state_shape: Union[int, Sequence[int]],
action_shape: Sequence[int],
num_atoms: int = 51,
noisy_std: float = 0.5,
device: Union[str, int, torch.device] = "cpu",
is_dueling: bool = True,
is_noisy: bool = True,
output_dim: Optional[int] = None,
hidden_dim: int = 128,
n_layers: int = 1,
dropout: float = 0.,
bidirectional: bool = True,
trans_model_name: str = 'bert-base-uncased',
max_length: int = 512,
) -> None:
super().__init__(state_shape, action_shape, device, features_only=True,
output_dim=output_dim, hidden_dim=hidden_dim, n_layers=n_layers,
dropout=dropout, bidirectional=bidirectional, trans_model_name=trans_model_name)
self.action_num = np.prod(action_shape)
self.num_atoms = num_atoms
def linear(x, y):
if is_noisy:
return NoisyLinear(x, y, noisy_std)
else:
return nn.Linear(x, y)
self.Q = nn.Sequential(
linear(self.output_dim, 512), nn.ReLU(inplace=True),
linear(512, self.action_num * self.num_atoms)
)
self._is_dueling = is_dueling
if self._is_dueling:
self.V = nn.Sequential(
linear(self.output_dim, 512), nn.ReLU(inplace=True),
linear(512, self.num_atoms)
)
self.output_dim = self.action_num * self.num_atoms
def forward(
self,
obs: Union[np.ndarray, torch.Tensor],
state: Optional[Any] = None,
info: Dict[str, Any] = {},
) -> Tuple[torch.Tensor, Any]:
r"""Mapping: x -> Z(x, \*)."""
obs, state = super().forward(obs)
q = self.Q(obs)
q = q.view(-1, self.action_num, self.num_atoms)
if self._is_dueling:
v = self.V(obs)
v = v.view(-1, 1, self.num_atoms)
logits = q - q.mean(dim=1, keepdim=True) + v
else:
logits = q
probs = logits.softmax(dim=2)
return probs, state
class Net_GRU_nn_emb_tianshou(Net):
def __init__(
self,
action_shape: Union[int, Sequence[int]] = 0,
hidden_sizes: Sequence[int] = (),
norm_layer: Optional[ModuleType] = None,
activation: Optional[ModuleType] = nn.ReLU,
device: Union[str, int, torch.device] = "cpu",
softmax: bool = False,
concat: bool = False,
num_atoms: int = 1,
dueling_param: Optional[Tuple[Dict[str, Any], Dict[str, Any]]] = None,
linear_layer: Type[nn.Linear] = nn.Linear,
hidden_dim: int = 128,
bidirectional: bool = True,
dropout: float = 0.,
n_layers: int = 1,
max_length: int = 512,
trans_model_name: str = 'bert-base-uncased',
word_emb_dim: int = 128,
) -> None:
nn.Module.__init__(self)
self.device = device
self.softmax = softmax
self.num_atoms = num_atoms
self.hidden_dim = hidden_dim
self.bidirectional = bidirectional
self.dropout = dropout
self.n_layers = n_layers
self.trans_model_name = trans_model_name
self.max_length = max_length
action_dim = int(np.prod(action_shape)) * num_atoms
self.use_dueling = dueling_param is not None
output_dim = action_dim if not self.use_dueling and not concat else 0
self.output_dim = output_dim or hidden_dim
self.tokenizer = AutoTokenizer.from_pretrained(trans_model_name)
from transformers import logging
logging.set_verbosity_error()
self.vocab_size = self.tokenizer.vocab_size
self.embedding = nn.Embedding(self.vocab_size, word_emb_dim)
self.model = MyGRU(word_emb_dim, self.hidden_dim, self.n_layers,
self.dropout, self.bidirectional, self.output_dim)
if self.use_dueling: # dueling DQN
q_kwargs, v_kwargs = dueling_param # type: ignore
q_output_dim, v_output_dim = 0, 0
if not concat:
q_output_dim, v_output_dim = action_dim, num_atoms
q_kwargs: Dict[str, Any] = {
**q_kwargs, "input_dim": self.output_dim,
"output_dim": q_output_dim,
"device": self.device
}
v_kwargs: Dict[str, Any] = {
**v_kwargs, "input_dim": self.output_dim,
"output_dim": v_output_dim,
"device": self.device
}
self.Q, self.V = MLP(**q_kwargs), MLP(**v_kwargs)
self.output_dim = self.Q.output_dim
def forward(
self,
obs: Union[np.ndarray, torch.Tensor],
state: Any = None,
info: Dict[str, Any] = {},
) -> Tuple[torch.Tensor, Any]:
"""Mapping: obs -> flatten (inside MLP)-> logits."""
if isinstance(obs, np.ndarray):
text = list(obs)
else:
text = obs
tokens = self.tokenizer(text, max_length=self.max_length, padding='max_length', truncation=True, return_tensors='pt')
input_ids = tokens['input_ids'].to(self.device)
attention_mask = tokens['attention_mask'].to(self.device)
embedding = self.embedding(input_ids)
mask = attention_mask.unsqueeze(-1).expand(embedding.size()).float()
embedding = embedding * mask
logits = self.model(embedding)
bsz = logits.shape[0]
if self.use_dueling: # Dueling DQN
q, v = self.Q(logits), self.V(logits)
if self.num_atoms > 1:
q = q.view(bsz, -1, self.num_atoms)
v = v.view(bsz, -1, self.num_atoms)
logits = q - q.mean(dim=1, keepdim=True) + v
elif self.num_atoms > 1:
logits = logits.view(bsz, -1, self.num_atoms)
if self.softmax:
logits = torch.softmax(logits, dim=-1)
return logits, state
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