File size: 6,731 Bytes
971403f 6f3bdf9 971403f 6f3bdf9 971403f f050c92 6f3bdf9 971403f 6f3bdf9 971403f 6f3bdf9 971403f 6f3bdf9 971403f 6f3bdf9 f050c92 6f3bdf9 971403f 6f3bdf9 f050c92 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 |
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)
|