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import gym
import torch
import torch.nn as nn
from abc import ABC, abstractmethod
from gym.spaces import Box, Discrete
from torch.distributions import Categorical, Distribution, Normal
from typing import NamedTuple, Optional, Sequence, Type, TypeVar, Union
from shared.module import FeatureExtractor, mlp
class PiForward(NamedTuple):
pi: Distribution
logp_a: Optional[torch.Tensor]
entropy: Optional[torch.Tensor]
class Actor(nn.Module, ABC):
@abstractmethod
def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward:
...
class CategoricalActorHead(Actor):
def __init__(
self,
act_dim: int,
hidden_sizes: Sequence[int] = (32,),
activation: Type[nn.Module] = nn.Tanh,
init_layers_orthogonal: bool = True,
) -> None:
super().__init__()
layer_sizes = tuple(hidden_sizes) + (act_dim,)
self._fc = mlp(
layer_sizes,
activation,
init_layers_orthogonal=init_layers_orthogonal,
final_layer_gain=0.01,
)
def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward:
logits = self._fc(obs)
pi = Categorical(logits=logits)
logp_a = None
entropy = None
if a is not None:
logp_a = pi.log_prob(a)
entropy = pi.entropy()
return PiForward(pi, logp_a, entropy)
class GaussianDistribution(Normal):
def log_prob(self, a: torch.Tensor) -> torch.Tensor:
return super().log_prob(a).sum(axis=-1)
def sample(self) -> torch.Tensor:
return self.rsample()
class GaussianActorHead(Actor):
def __init__(
self,
act_dim: int,
hidden_sizes: Sequence[int] = (32,),
activation: Type[nn.Module] = nn.Tanh,
init_layers_orthogonal: bool = True,
log_std_init: float = -0.5,
) -> None:
super().__init__()
layer_sizes = tuple(hidden_sizes) + (act_dim,)
self.mu_net = mlp(
layer_sizes,
activation,
init_layers_orthogonal=init_layers_orthogonal,
final_layer_gain=0.01,
)
self.log_std = nn.Parameter(
torch.ones(act_dim, dtype=torch.float32) * log_std_init
)
def _distribution(self, obs: torch.Tensor) -> Distribution:
mu = self.mu_net(obs)
std = torch.exp(self.log_std)
return GaussianDistribution(mu, std)
def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward:
pi = self._distribution(obs)
logp_a = None
entropy = None
if a is not None:
logp_a = pi.log_prob(a)
entropy = pi.entropy()
return PiForward(pi, logp_a, entropy)
class TanhBijector:
def __init__(self, epsilon: float = 1e-6) -> None:
self.epsilon = epsilon
@staticmethod
def forward(x: torch.Tensor) -> torch.Tensor:
return torch.tanh(x)
@staticmethod
def inverse(y: torch.Tensor) -> torch.Tensor:
eps = torch.finfo(y.dtype).eps
clamped_y = y.clamp(min=-1.0 + eps, max=1.0 - eps)
return torch.atanh(clamped_y)
def log_prob_correction(self, x: torch.Tensor) -> torch.Tensor:
return torch.log(1.0 - torch.tanh(x) ** 2 + self.epsilon)
class StateDependentNoiseDistribution(Normal):
def __init__(
self,
loc,
scale,
latent_sde: torch.Tensor,
exploration_mat: torch.Tensor,
exploration_matrices: torch.Tensor,
bijector: Optional[TanhBijector] = None,
validate_args=None,
):
super().__init__(loc, scale, validate_args)
self.latent_sde = latent_sde
self.exploration_mat = exploration_mat
self.exploration_matrices = exploration_matrices
self.bijector = bijector
def log_prob(self, a: torch.Tensor) -> torch.Tensor:
gaussian_a = self.bijector.inverse(a) if self.bijector else a
log_prob = super().log_prob(gaussian_a).sum(axis=-1)
if self.bijector:
log_prob -= torch.sum(self.bijector.log_prob_correction(gaussian_a), dim=1)
return log_prob
def sample(self) -> torch.Tensor:
noise = self._get_noise()
actions = self.mean + noise
return self.bijector.forward(actions) if self.bijector else actions
def _get_noise(self) -> torch.Tensor:
if len(self.latent_sde) == 1 or len(self.latent_sde) != len(
self.exploration_matrices
):
return torch.mm(self.latent_sde, self.exploration_mat)
# (batch_size, n_features) -> (batch_size, 1, n_features)
latent_sde = self.latent_sde.unsqueeze(dim=1)
# (batch_size, 1, n_actions)
noise = torch.bmm(latent_sde, self.exploration_matrices)
return noise.squeeze(dim=1)
@property
def mode(self) -> torch.Tensor:
mean = super().mode
return self.bijector.forward(mean) if self.bijector else mean
StateDependentNoiseActorHeadSelf = TypeVar(
"StateDependentNoiseActorHeadSelf", bound="StateDependentNoiseActorHead"
)
class StateDependentNoiseActorHead(Actor):
def __init__(
self,
act_dim: int,
hidden_sizes: Sequence[int] = (32,),
activation: Type[nn.Module] = nn.Tanh,
init_layers_orthogonal: bool = True,
log_std_init: float = -0.5,
full_std: bool = True,
squash_output: bool = False,
learn_std: bool = False,
) -> None:
super().__init__()
self.act_dim = act_dim
layer_sizes = tuple(hidden_sizes) + (self.act_dim,)
if len(layer_sizes) == 2:
self.latent_net = nn.Identity()
elif len(layer_sizes) > 2:
self.latent_net = mlp(
layer_sizes[:-1],
activation,
output_activation=activation,
init_layers_orthogonal=init_layers_orthogonal,
)
else:
raise ValueError("hidden_sizes must be of at least length 1")
self.mu_net = mlp(
layer_sizes[-2:],
activation,
init_layers_orthogonal=init_layers_orthogonal,
final_layer_gain=0.01,
)
self.full_std = full_std
std_dim = (hidden_sizes[-1], act_dim if self.full_std else 1)
self.log_std = nn.Parameter(
torch.ones(std_dim, dtype=torch.float32) * log_std_init
)
self.bijector = TanhBijector() if squash_output else None
self.learn_std = learn_std
self.device = None
self.exploration_mat = None
self.exploration_matrices = None
self.sample_weights()
def to(
self: StateDependentNoiseActorHeadSelf,
device: Optional[torch.device] = None,
dtype: Optional[Union[torch.dtype, str]] = None,
non_blocking: bool = False,
) -> StateDependentNoiseActorHeadSelf:
super().to(device, dtype, non_blocking)
self.device = device
return self
def _distribution(self, obs: torch.Tensor) -> Distribution:
latent = self.latent_net(obs)
mu = self.mu_net(latent)
latent_sde = latent if self.learn_std else latent.detach()
variance = torch.mm(latent_sde**2, self._get_std() ** 2)
assert self.exploration_mat is not None
assert self.exploration_matrices is not None
return StateDependentNoiseDistribution(
mu,
torch.sqrt(variance + 1e-6),
latent_sde,
self.exploration_mat,
self.exploration_matrices,
self.bijector,
)
def _get_std(self) -> torch.Tensor:
std = torch.exp(self.log_std)
if self.full_std:
return std
ones = torch.ones(self.log_std.shape[0], self.act_dim)
if self.device:
ones = ones.to(self.device)
return ones * std
def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward:
pi = self._distribution(obs)
logp_a = None
entropy = None
if a is not None:
logp_a = pi.log_prob(a)
entropy = -logp_a
return PiForward(pi, logp_a, entropy)
def sample_weights(self, batch_size: int = 1) -> None:
std = self._get_std()
weights_dist = Normal(torch.zeros_like(std), std)
# Reparametrization trick to pass gradients
self.exploration_mat = weights_dist.rsample()
self.exploration_matrices = weights_dist.rsample(torch.Size((batch_size,)))
def actor_head(
action_space: gym.Space,
hidden_sizes: Sequence[int],
init_layers_orthogonal: bool,
activation: Type[nn.Module],
log_std_init: float = -0.5,
use_sde: bool = False,
full_std: bool = True,
squash_output: bool = False,
) -> Actor:
assert not use_sde or isinstance(
action_space, Box
), "use_sde only valid if Box action_space"
assert not squash_output or use_sde, "squash_output only valid if use_sde"
if isinstance(action_space, Discrete):
return CategoricalActorHead(
action_space.n,
hidden_sizes=hidden_sizes,
activation=activation,
init_layers_orthogonal=init_layers_orthogonal,
)
elif isinstance(action_space, Box):
if use_sde:
return StateDependentNoiseActorHead(
action_space.shape[0],
hidden_sizes=hidden_sizes,
activation=activation,
init_layers_orthogonal=init_layers_orthogonal,
log_std_init=log_std_init,
full_std=full_std,
squash_output=squash_output,
)
else:
return GaussianActorHead(
action_space.shape[0],
hidden_sizes=hidden_sizes,
activation=activation,
init_layers_orthogonal=init_layers_orthogonal,
log_std_init=log_std_init,
)
else:
raise ValueError(f"Unsupported action space: {action_space}")
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