File size: 10,162 Bytes
9dc837c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 |
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.feature_extractor import FeatureExtractor
from shared.module.module import 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}")
|