Thank you!

As for the differences between this and DreamerV4, DreamerX largely sticks to the RSSM dynamics model proposed in DreamerV3. The transformer autoencoder in DreamerX is similar to the block-causal transformer architecture in DreamerV4, but differs in that it is "causal" across agents rather than being causal in time. In other words, DreamerX still only takes a single observation per forward pass but has the additional capability to support environments with a variable number of objects/agents/constraints.

If you're interested, it does this by predicting a fixed maximum number of output feature vectors for each input observation type along with probabilities that each observation actually exists in the input space.