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modules.py
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modules.py
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from math import sqrt
from typing import Tuple, Optional
import numpy as np
import torch
from torch import nn
from torch.distributions import Normal, Distribution
class DeterministicActor(nn.Module):
def __init__(self,
state_dim: int,
action_dim: int,
hidden_dim: int = 256,
edac_init: bool = False,
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(),
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)
def forward(self, state: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
out = self.trunk(state)
out = torch.tanh(out)
return self.max_action * out, None
@torch.no_grad()
def act(self, state: np.ndarray, device: str) -> np.ndarray:
state = torch.tensor(state, device=device, dtype=torch.float32)
action = self(state).cpu().numpy()
return action
class StochasticActor(nn.Module):
def __init__(self,
state_dim: int,
action_dim: int,
hidden_dim: int = 256,
min_log_std: float = -20.0,
max_log_std: float = 2.0,
min_action: float = -1.0,
max_action: float = 1.0) -> None:
super().__init__()
self.net = 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)
self.min_log_std = min_log_std
self.max_log_std = max_log_std
self.min_action = min_action
self.max_action = max_action
def get_policy(self, state: torch.Tensor) -> Distribution:
hidden = self.net(state)
mean, log_std = self.mu(hidden), self.log_std(hidden)
log_std = self.log_std.clamp(self.min_log_std, self.max_log_std)
policy = Normal(mean, log_std.exp())
return policy
def log_prob(self, state: torch.Tensor, action: torch.Tensor) -> torch.Tensor:
policy = self.get_policy(state)
log_prob = policy.log_prob(action).sum(-1, keepdim=True)
return log_prob
def act(self, state: np.ndarray, device: str) -> np.ndarray:
state = torch.tensor(state[None], dtype=torch.float32, device=device)
policy = self.get_policy(state)
#action = policy.mean
if self.net.training:
action = policy.sample()
else:
action = policy.mean
return action[0].cpu().numpy()
def forward(self, state: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
policy = self.get_policy(state)
action = policy.rsample().clamp(self.min_action, self.max_action)
log_prob = policy.log_prob(action).sum(-1, keepdim=True)
return action, log_prob
class ValueFunction(nn.Module):
def __init__(self,
state_dim: int,
hidden_dim: int = 256,
layer_norm: bool = True) -> None:
super().__init__()
self.state_dim = state_dim
self.hidden_dim = hidden_dim
self.net = nn.Sequential(
nn.Linear(state_dim, hidden_dim),
nn.LayerNorm(hidden_dim) if layer_norm else nn.Identity(),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.LayerNorm(hidden_dim) if layer_norm else nn.Identity(),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.LayerNorm(hidden_dim) if layer_norm else nn.Identity(),
nn.ReLU(),
nn.Linear(hidden_dim, 1)
)
def forward(self, state: torch.Tensor) -> torch.Tensor:
return self.net(state)
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) -> None:
scale_factor = sqrt(5)
# default pytorch init
for layer in range(self.ensemble_size):
nn.init.kaiming_normal_(self.weight[layer], a=scale_factor)
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight[0])
bound = 1 / sqrt(fan_in) if fan_in > 0 else 0
nn.init.uniform_(self.bias, -bound, bound)
def forward(self, x: torch.Tensor) -> torch.Tensor:
'''
x: [ensemble_size, batch_size, input_size]
weight: [ensemble_size, input_size, out_size]
bias: [ensemble_size, batch_size, out_size]
'''
return x @ self.weight + self.bias
class EnsembledCritic(nn.Module):
def __init__(self,
state_dim: int,
action_dim: 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.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, 1, 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)
q_values = self.critic(concat).squeeze(-1)
return q_values