Code for our NeurIPS 2021 paper: Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains

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Deep LearningGateL0RD
Overview

GateL0RD

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, output_size=output_dim, hidden_size=args.latent_dim, reg_lambda=args.lambda)
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.

Owner
Autonomous Learning Group
Autonomous Learning Group
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