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Action-conditioned world model with reconstruction.
The model learns:
observation_t -> latent_t
latent_t + action_t -> latent_t+1
latent_t+1 -> predicted observation_t+1
"""
import torch
from torch import nn
class ActionConditionedWorldModel(nn.Module):
def __init__(
self,
obs_dim: int,
action_dim: int,
latent_dim: int = 32,
hidden_dim: int = 128,
):
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(obs_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, latent_dim),
)
self.action_encoder = nn.Sequential(
nn.Linear(action_dim, 32),
nn.ReLU(),
)
self.dynamics = nn.Sequential(
nn.Linear(latent_dim + 32, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, latent_dim),
)
self.decoder = nn.Sequential(
nn.Linear(latent_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, obs_dim),
)
def step(
self,
latent: torch.Tensor,
action: torch.Tensor,
) -> torch.Tensor:
action_features = self.action_encoder(action)
x = torch.cat([latent, action_features], dim=-1)
delta = self.dynamics(x)
return latent + delta
def forward(
self,
obs: torch.Tensor,
action: torch.Tensor,
):
latent = self.encoder(obs)
next_latent = self.step(latent, action)
next_obs_pred = self.decoder(next_latent)
return next_obs_pred, next_latent
if __name__ == "__main__":
torch.manual_seed(0)
model = ActionConditionedWorldModel(
obs_dim=12,
action_dim=3,
latent_dim=24,
)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
obs_t = torch.randn(32, 12)
action_t = torch.randn(32, 3)
obs_t1 = torch.randn(32, 12)
pred_obs_t1, _ = model(obs_t, action_t)
loss = nn.functional.mse_loss(pred_obs_t1, obs_t1)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print("training loss:", float(loss))
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