This is a lightweight PyTorch implementation of GateL0RD, our RNN presented in "Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains".
We provide two variants of GateL0RD: GateL0RD can be used like a regular PyTorch RNN, whereas GateL0RDCell can be used like a PyTorch RNNCell. To install put gatel0rd.py into your working directory.
Generic example using GateL0RD:
from gatel0rd import GateL0RD
#...
model = GateL0RD(input_size=input_dim, hidden_size=args.latent_dim, reg_lambda=args.lambda, output_size=output_dim)
optimizer = optim.Adam(model.parameters(), lr=args.lr) # optimizer of your choice
# ...
for X,Y in training_data:
Y_hat, H, Theta = model.forward(X)
optimizer.zero_grad()
loss_task = F.mse_loss(Y_hat, Y) # loss of your choice
loss = model.loss(loss_task, Theta)
loss.backward()
optimizer.step()
#...A repository containing all experiments of the paper, including examples on how to use a GateL0RDCell, can be found here.