from typing import Any, Dict, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from tianshou.data import Batch, to_torch
from tianshou.utils.net.common import MLP
[docs]class Actor(nn.Module):
"""Simple actor network.
Will create an actor operated in discrete action space with structure of
preprocess_net ---> action_shape.
:param preprocess_net: a self-defined preprocess_net which output a
flattened hidden state.
:param action_shape: a sequence of int for the shape of action.
:param hidden_sizes: a sequence of int for constructing the MLP after
preprocess_net. Default to empty sequence (where the MLP now contains
only a single linear layer).
:param bool softmax_output: whether to apply a softmax layer over the last
layer's output.
:param int preprocess_net_output_dim: the output dimension of
preprocess_net.
For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
.. seealso::
Please refer to :class:`~tianshou.utils.net.common.Net` as an instance
of how preprocess_net is suggested to be defined.
"""
def __init__(
self,
preprocess_net: nn.Module,
action_shape: Sequence[int],
hidden_sizes: Sequence[int] = (),
softmax_output: bool = True,
preprocess_net_output_dim: Optional[int] = None,
device: Union[str, int, torch.device] = "cpu",
) -> None:
super().__init__()
self.device = device
self.preprocess = preprocess_net
self.output_dim = int(np.prod(action_shape))
input_dim = getattr(preprocess_net, "output_dim", preprocess_net_output_dim)
self.last = MLP(
input_dim, # type: ignore
self.output_dim,
hidden_sizes,
device=self.device
)
self.softmax_output = softmax_output
[docs] def forward(
self,
obs: Union[np.ndarray, torch.Tensor],
state: Any = None,
info: Dict[str, Any] = {},
) -> Tuple[torch.Tensor, Any]:
r"""Mapping: s -> Q(s, \*)."""
logits, hidden = self.preprocess(obs, state)
logits = self.last(logits)
if self.softmax_output:
logits = F.softmax(logits, dim=-1)
return logits, hidden
[docs]class Critic(nn.Module):
"""Simple critic network. Will create an actor operated in discrete \
action space with structure of preprocess_net ---> 1(q value).
:param preprocess_net: a self-defined preprocess_net which output a
flattened hidden state.
:param hidden_sizes: a sequence of int for constructing the MLP after
preprocess_net. Default to empty sequence (where the MLP now contains
only a single linear layer).
:param int last_size: the output dimension of Critic network. Default to 1.
:param int preprocess_net_output_dim: the output dimension of
preprocess_net.
For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
.. seealso::
Please refer to :class:`~tianshou.utils.net.common.Net` as an instance
of how preprocess_net is suggested to be defined.
"""
def __init__(
self,
preprocess_net: nn.Module,
hidden_sizes: Sequence[int] = (),
last_size: int = 1,
preprocess_net_output_dim: Optional[int] = None,
device: Union[str, int, torch.device] = "cpu",
) -> None:
super().__init__()
self.device = device
self.preprocess = preprocess_net
self.output_dim = last_size
input_dim = getattr(preprocess_net, "output_dim", preprocess_net_output_dim)
self.last = MLP(
input_dim, # type: ignore
last_size,
hidden_sizes,
device=self.device
)
[docs] def forward(
self, obs: Union[np.ndarray, torch.Tensor], **kwargs: Any
) -> torch.Tensor:
"""Mapping: s -> V(s)."""
logits, _ = self.preprocess(obs, state=kwargs.get("state", None))
return self.last(logits)
[docs]class CosineEmbeddingNetwork(nn.Module):
"""Cosine embedding network for IQN. Convert a scalar in [0, 1] to a list \
of n-dim vectors.
:param num_cosines: the number of cosines used for the embedding.
:param embedding_dim: the dimension of the embedding/output.
.. note::
From https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/blob/master
/fqf_iqn_qrdqn/network.py .
"""
def __init__(self, num_cosines: int, embedding_dim: int) -> None:
super().__init__()
self.net = nn.Sequential(nn.Linear(num_cosines, embedding_dim), nn.ReLU())
self.num_cosines = num_cosines
self.embedding_dim = embedding_dim
[docs] def forward(self, taus: torch.Tensor) -> torch.Tensor:
batch_size = taus.shape[0]
N = taus.shape[1]
# Calculate i * \pi (i=1,...,N).
i_pi = np.pi * torch.arange(
start=1, end=self.num_cosines + 1, dtype=taus.dtype, device=taus.device
).view(1, 1, self.num_cosines)
# Calculate cos(i * \pi * \tau).
cosines = torch.cos(taus.view(batch_size, N, 1) * i_pi
).view(batch_size * N, self.num_cosines)
# Calculate embeddings of taus.
tau_embeddings = self.net(cosines).view(batch_size, N, self.embedding_dim)
return tau_embeddings
[docs]class ImplicitQuantileNetwork(Critic):
"""Implicit Quantile Network.
:param preprocess_net: a self-defined preprocess_net which output a
flattened hidden state.
:param int action_shape: a sequence of int for the shape of action.
:param hidden_sizes: a sequence of int for constructing the MLP after
preprocess_net. Default to empty sequence (where the MLP now contains
only a single linear layer).
:param int num_cosines: the number of cosines to use for cosine embedding.
Default to 64.
:param int preprocess_net_output_dim: the output dimension of
preprocess_net.
.. note::
Although this class inherits Critic, it is actually a quantile Q-Network
with output shape (batch_size, action_dim, sample_size).
The second item of the first return value is tau vector.
"""
def __init__(
self,
preprocess_net: nn.Module,
action_shape: Sequence[int],
hidden_sizes: Sequence[int] = (),
num_cosines: int = 64,
preprocess_net_output_dim: Optional[int] = None,
device: Union[str, int, torch.device] = "cpu"
) -> None:
last_size = int(np.prod(action_shape))
super().__init__(
preprocess_net, hidden_sizes, last_size, preprocess_net_output_dim, device
)
self.input_dim = getattr(
preprocess_net, "output_dim", preprocess_net_output_dim
)
self.embed_model = CosineEmbeddingNetwork(
num_cosines,
self.input_dim # type: ignore
).to(device)
[docs] def forward( # type: ignore
self, obs: Union[np.ndarray, torch.Tensor], sample_size: int, **kwargs: Any
) -> Tuple[Any, torch.Tensor]:
r"""Mapping: s -> Q(s, \*)."""
logits, hidden = self.preprocess(obs, state=kwargs.get("state", None))
# Sample fractions.
batch_size = logits.size(0)
taus = torch.rand(
batch_size, sample_size, dtype=logits.dtype, device=logits.device
)
embedding = (logits.unsqueeze(1) *
self.embed_model(taus)).view(batch_size * sample_size, -1)
out = self.last(embedding).view(batch_size, sample_size, -1).transpose(1, 2)
return (out, taus), hidden
[docs]class FractionProposalNetwork(nn.Module):
"""Fraction proposal network for FQF.
:param num_fractions: the number of factions to propose.
:param embedding_dim: the dimension of the embedding/input.
.. note::
Adapted from https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/blob/master
/fqf_iqn_qrdqn/network.py .
"""
def __init__(self, num_fractions: int, embedding_dim: int) -> None:
super().__init__()
self.net = nn.Linear(embedding_dim, num_fractions)
torch.nn.init.xavier_uniform_(self.net.weight, gain=0.01)
torch.nn.init.constant_(self.net.bias, 0)
self.num_fractions = num_fractions
self.embedding_dim = embedding_dim
[docs] def forward(
self, obs_embeddings: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
# Calculate (log of) probabilities q_i in the paper.
dist = torch.distributions.Categorical(logits=self.net(obs_embeddings))
taus_1_N = torch.cumsum(dist.probs, dim=1)
# Calculate \tau_i (i=0,...,N).
taus = F.pad(taus_1_N, (1, 0))
# Calculate \hat \tau_i (i=0,...,N-1).
tau_hats = (taus[:, :-1] + taus[:, 1:]).detach() / 2.0
# Calculate entropies of value distributions.
entropies = dist.entropy()
return taus, tau_hats, entropies
[docs]class FullQuantileFunction(ImplicitQuantileNetwork):
"""Full(y parameterized) Quantile Function.
:param preprocess_net: a self-defined preprocess_net which output a
flattened hidden state.
:param int action_shape: a sequence of int for the shape of action.
:param hidden_sizes: a sequence of int for constructing the MLP after
preprocess_net. Default to empty sequence (where the MLP now contains
only a single linear layer).
:param int num_cosines: the number of cosines to use for cosine embedding.
Default to 64.
:param int preprocess_net_output_dim: the output dimension of
preprocess_net.
.. note::
The first return value is a tuple of (quantiles, fractions, quantiles_tau),
where fractions is a Batch(taus, tau_hats, entropies).
"""
def __init__(
self,
preprocess_net: nn.Module,
action_shape: Sequence[int],
hidden_sizes: Sequence[int] = (),
num_cosines: int = 64,
preprocess_net_output_dim: Optional[int] = None,
device: Union[str, int, torch.device] = "cpu",
) -> None:
super().__init__(
preprocess_net, action_shape, hidden_sizes, num_cosines,
preprocess_net_output_dim, device
)
def _compute_quantiles(
self, obs: torch.Tensor, taus: torch.Tensor
) -> torch.Tensor:
batch_size, sample_size = taus.shape
embedding = (obs.unsqueeze(1) *
self.embed_model(taus)).view(batch_size * sample_size, -1)
quantiles = self.last(embedding).view(batch_size, sample_size,
-1).transpose(1, 2)
return quantiles
[docs] def forward( # type: ignore
self, obs: Union[np.ndarray, torch.Tensor],
propose_model: FractionProposalNetwork,
fractions: Optional[Batch] = None,
**kwargs: Any
) -> Tuple[Any, torch.Tensor]:
r"""Mapping: s -> Q(s, \*)."""
logits, hidden = self.preprocess(obs, state=kwargs.get("state", None))
# Propose fractions
if fractions is None:
taus, tau_hats, entropies = propose_model(logits.detach())
fractions = Batch(taus=taus, tau_hats=tau_hats, entropies=entropies)
else:
taus, tau_hats = fractions.taus, fractions.tau_hats
quantiles = self._compute_quantiles(logits, tau_hats)
# Calculate quantiles_tau for computing fraction grad
quantiles_tau = None
if self.training:
with torch.no_grad():
quantiles_tau = self._compute_quantiles(logits, taus[:, 1:-1])
return (quantiles, fractions, quantiles_tau), hidden
[docs]class NoisyLinear(nn.Module):
"""Implementation of Noisy Networks. arXiv:1706.10295.
:param int in_features: the number of input features.
:param int out_features: the number of output features.
:param float noisy_std: initial standard deviation of noisy linear layers.
.. note::
Adapted from https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/blob/master
/fqf_iqn_qrdqn/network.py .
"""
def __init__(
self, in_features: int, out_features: int, noisy_std: float = 0.5
) -> None:
super().__init__()
# Learnable parameters.
self.mu_W = nn.Parameter(torch.FloatTensor(out_features, in_features))
self.sigma_W = nn.Parameter(torch.FloatTensor(out_features, in_features))
self.mu_bias = nn.Parameter(torch.FloatTensor(out_features))
self.sigma_bias = nn.Parameter(torch.FloatTensor(out_features))
# Factorized noise parameters.
self.register_buffer('eps_p', torch.FloatTensor(in_features))
self.register_buffer('eps_q', torch.FloatTensor(out_features))
self.in_features = in_features
self.out_features = out_features
self.sigma = noisy_std
self.reset()
self.sample()
[docs] def reset(self) -> None:
bound = 1 / np.sqrt(self.in_features)
self.mu_W.data.uniform_(-bound, bound)
self.mu_bias.data.uniform_(-bound, bound)
self.sigma_W.data.fill_(self.sigma / np.sqrt(self.in_features))
self.sigma_bias.data.fill_(self.sigma / np.sqrt(self.in_features))
[docs] def f(self, x: torch.Tensor) -> torch.Tensor:
x = torch.randn(x.size(0), device=x.device)
return x.sign().mul_(x.abs().sqrt_())
[docs] def sample(self) -> None:
self.eps_p.copy_(self.f(self.eps_p)) # type: ignore
self.eps_q.copy_(self.f(self.eps_q)) # type: ignore
[docs] def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.training:
weight = self.mu_W + self.sigma_W * (
self.eps_q.ger(self.eps_p) # type: ignore
)
bias = self.mu_bias + self.sigma_bias * self.eps_q.clone() # type: ignore
else:
weight = self.mu_W
bias = self.mu_bias
return F.linear(x, weight, bias)
[docs]def sample_noise(model: nn.Module) -> bool:
"""Sample the random noises of NoisyLinear modules in the model.
:param model: a PyTorch module which may have NoisyLinear submodules.
:returns: True if model has at least one NoisyLinear submodule;
otherwise, False.
"""
done = False
for m in model.modules():
if isinstance(m, NoisyLinear):
m.sample()
done = True
return done
[docs]class IntrinsicCuriosityModule(nn.Module):
"""Implementation of Intrinsic Curiosity Module. arXiv:1705.05363.
:param torch.nn.Module feature_net: a self-defined feature_net which output a
flattened hidden state.
:param int feature_dim: input dimension of the feature net.
:param int action_dim: dimension of the action space.
:param hidden_sizes: hidden layer sizes for forward and inverse models.
:param device: device for the module.
"""
def __init__(
self,
feature_net: nn.Module,
feature_dim: int,
action_dim: int,
hidden_sizes: Sequence[int] = (),
device: Union[str, torch.device] = "cpu"
) -> None:
super().__init__()
self.feature_net = feature_net
self.forward_model = MLP(
feature_dim + action_dim,
output_dim=feature_dim,
hidden_sizes=hidden_sizes,
device=device
)
self.inverse_model = MLP(
feature_dim * 2,
output_dim=action_dim,
hidden_sizes=hidden_sizes,
device=device
)
self.feature_dim = feature_dim
self.action_dim = action_dim
self.device = device
[docs] def forward(
self, s1: Union[np.ndarray, torch.Tensor],
act: Union[np.ndarray, torch.Tensor], s2: Union[np.ndarray,
torch.Tensor], **kwargs: Any
) -> Tuple[torch.Tensor, torch.Tensor]:
r"""Mapping: s1, act, s2 -> mse_loss, act_hat."""
s1 = to_torch(s1, dtype=torch.float32, device=self.device)
s2 = to_torch(s2, dtype=torch.float32, device=self.device)
phi1, phi2 = self.feature_net(s1), self.feature_net(s2)
act = to_torch(act, dtype=torch.long, device=self.device)
phi2_hat = self.forward_model(
torch.cat([phi1, F.one_hot(act, num_classes=self.action_dim)], dim=1)
)
mse_loss = 0.5 * F.mse_loss(phi2_hat, phi2, reduction="none").sum(1)
act_hat = self.inverse_model(torch.cat([phi1, phi2], dim=1))
return mse_loss, act_hat