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modules.py
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modules.py
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from typing import Optional, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.functional import logsigmoid
from torch.distributions import Normal, Distribution
from math import sqrt, log
class TanhNormal(Distribution):
def __init__(self,
mu: torch.Tensor,
sigma: torch.Tensor):
super().__init__()
self.mu = mu
self.sigma = sigma
self.standard_normal = Normal(torch.zeros_like(mu), torch.ones_like(sigma))
self.normal = Normal(mu, sigma)
def log_prob(self, value: torch.Tensor):
log_det = 2 * log(2) + logsigmoid(2 * value) + logsigmoid(-2 * value)
res = self.normal.log_prob(value) - log_det
return res
def rsample(self, sample_shape=torch.Size()) -> Tuple[torch.Tensor, torch.Tensor]:
value = self.mu + self.sigma * self.standard_normal.sample(sample_shape)
return torch.tanh(value), value
class Actor(nn.Module):
def __init__(self,
state_dim: int,
action_dim: int,
hidden_dim: int = 256,
edac_init: bool = True,
max_action: float = 1.0) -> None:
super().__init__()
self.action_dim = action_dim
self.max_action = max_action
self.trunk = nn.Sequential(
nn.Linear(state_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU()
)
self.mu = nn.Linear(hidden_dim, action_dim)
self.log_std = nn.Linear(hidden_dim, action_dim)
if edac_init:
# init as in the EDAC paper
for layer in self.trunk[::2]:
nn.init.constant_(layer.bias, 0.1)
nn.init.uniform_(self.mu.weight, -1e-3, 1e-3)
nn.init.uniform_(self.mu.bias, -1e-3, 1e-3)
nn.init.uniform_(self.log_std.weight, -1e-3, 1e-3)
nn.init.uniform_(self.log_std.bias, -1e-3, 1e-3)
def forward(self,
state: torch.Tensor,
deterministic: bool = False,
need_log_prob: bool = False) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
hidden = self.trunk(state)
mu, log_std = self.mu(hidden), self.log_std(hidden)
log_std = torch.clip(log_std, -20, 2)
policy_distribution = Normal(mu, torch.exp(log_std))
if deterministic:
action = mu
else:
action = policy_distribution.rsample()
tanh_action, log_prob = torch.tanh(action), None
if need_log_prob:
log_prob = policy_distribution.log_prob(action).sum(-1)
log_prob = log_prob - torch.log(1 - tanh_action.pow(2) + 1e-6).sum(-1)
# shape [batch_size,]
return tanh_action * self.max_action, log_prob
@torch.no_grad()
def act(self, state: np.ndarray, device: str) -> np.ndarray:
deterministic = not self.training
state = torch.tensor(state, device=device, dtype=torch.float32)
action = self(state, deterministic=deterministic)[0].cpu().numpy()
return action
class EnsembledLinear(nn.Module):
def __init__(self,
in_features: int,
out_features: int,
ensemble_size: int) -> None:
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.ensemble_size = ensemble_size
self.weight = nn.Parameter(torch.empty(ensemble_size, in_features, out_features))
self.bias = nn.Parameter(torch.empty(ensemble_size, 1, out_features))
self.reset_parameters()
def reset_parameters(self):
for layer in range(self.ensemble_size):
nn.init.kaiming_uniform_(self.weight[layer], a=sqrt(5))
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight[0])
bound = 0
if fan_in > 0:
bound = 1 / sqrt(fan_in)
nn.init.uniform_(self.bias, -bound, bound)
def forward(self, x: torch.Tensor) -> torch.Tensor:
out = x @ self.weight + self.bias
return out
class TruncatedQuantileEnsembledCritic(nn.Module):
def __init__(self,
state_dim: int,
action_dim: int,
num_quantiles: int,
hidden_dim: int = 256,
num_critics: int = 2,
layer_norm: bool = True,
edac_init: bool = True) -> None:
super().__init__()
#block = nn.LayerNorm(hidden_dim) if layer_norm else nn.Identity()
self.num_critics = num_critics
self.num_quantiles = num_quantiles
self.critic = nn.Sequential(
EnsembledLinear(state_dim + action_dim, hidden_dim, num_critics),
nn.LayerNorm(hidden_dim) if layer_norm else nn.Identity(),
nn.ReLU(),
EnsembledLinear(hidden_dim, hidden_dim, num_critics),
nn.LayerNorm(hidden_dim) if layer_norm else nn.Identity(),
nn.ReLU(),
EnsembledLinear(hidden_dim, hidden_dim, num_critics),
nn.LayerNorm(hidden_dim) if layer_norm else nn.Identity(),
nn.ReLU(),
EnsembledLinear(hidden_dim, num_quantiles, num_critics)
)
if edac_init:
# init as in the EDAC paper
for layer in self.critic[::3]:
nn.init.constant_(layer.bias, 0.1)
nn.init.uniform_(self.critic[-1].weight, -3e-3, 3e-3)
nn.init.uniform_(self.critic[-1].bias, -3e-3, 3e-3)
def forward(self, state: torch.Tensor, action: torch.Tensor) -> torch.Tensor:
concat = torch.cat([state, action], dim=-1)
concat = concat.unsqueeze(0)
concat = concat.repeat_interleave(self.num_critics, dim=0)
quantiles = self.critic(concat)
# [batch_size, num_critics, num_quantiles]
return quantiles.permute(1, 0, 2)