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from typing import Optional, Tuple, Type, TypeVar, Union
import torch
import torch.nn as nn
from torch.distributions import Distribution, Normal
from rl_algo_impls.shared.actor.actor import Actor, PiForward
from rl_algo_impls.shared.module.utils import mlp
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)
def sum_independent_dims(tensor: torch.Tensor) -> torch.Tensor:
if len(tensor.shape) > 1:
return tensor.sum(dim=1)
return tensor.sum()
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 = sum_independent_dims(super().log_prob(gaussian_a))
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,
in_dim: int,
hidden_sizes: Tuple[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 = (in_dim,) + hidden_sizes + (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,
)
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 = (layer_sizes[-2], 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,
actions: Optional[torch.Tensor] = None,
action_masks: Optional[torch.Tensor] = None,
) -> PiForward:
assert (
not action_masks
), f"{self.__class__.__name__} does not support action_masks"
pi = self._distribution(obs)
return pi_forward(pi, actions, self.bijector)
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,)))
@property
def action_shape(self) -> Tuple[int, ...]:
return (self.act_dim,)
def pi_forward(
distribution: Distribution,
actions: Optional[torch.Tensor] = None,
bijector: Optional[TanhBijector] = None,
) -> PiForward:
logp_a = None
entropy = None
if actions is not None:
logp_a = distribution.log_prob(actions)
entropy = -logp_a if bijector else sum_independent_dims(distribution.entropy())
return PiForward(distribution, logp_a, entropy)
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