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3425207 | 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 | from __future__ import annotations
import torch
from schema import ACTIONS
from torch import nn
class NeuralModelMachine(nn.Module):
def __init__(self) -> None:
super().__init__()
self.action_embedding = nn.Embedding(len(ACTIONS), 16)
self.network = nn.Sequential(
nn.Linear(8 + 4 + 16, 64),
nn.LayerNorm(64),
nn.SiLU(),
nn.Linear(64, 64),
nn.SiLU(),
nn.Linear(64, 9),
)
def forward(
self,
state: torch.Tensor,
capabilities: torch.Tensor,
action: torch.Tensor,
) -> torch.Tensor:
action_features = self.action_embedding(action)
return self.network(torch.cat([state, capabilities, action_features], dim=1))
@torch.inference_mode()
def transition(
self,
state: torch.Tensor,
capabilities: torch.Tensor,
action: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
probabilities = torch.sigmoid(self(state, capabilities, action))
return (probabilities[:, :8] >= 0.5).float(), probabilities[:, 8]
def parameter_count(model: nn.Module) -> int:
return sum(parameter.numel() for parameter in model.parameters())
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