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2024-08-27 22:59:40
gamengen.github.io
Data Collection via Agent Play:
Since we cannot collect human gameplay at scale, as a first stage we train an automatic RL-agent to play
the game,
persisting it’s training episodes of actions and observations, which become the training data for our
generative model.
Training the Generative Diffusion Model: We re-purpose a small diffusion model, Stable Diffusion v1.4,
and condition it on a sequence of previous actions and observations (frames). To mitigate
auto-regressive drift
during inference, we corrupt context frames by adding Gaussian noise to encoded frames during training.
This allows the network to correct information sampled in previous frames, and we found it to be
critical
for preserving visual stability over long time periods.
Latent Decoder Fine-Tuning: The pre-trained auto-encoder of Stable Diffusion v1.4, which compresses
8×8
pixel patches into 4 latent channels, results in meaningful artifacts when predicting game frames, which
affect
small details and particularly the bottom bar HUD. To leverage the pre-trained knowledge while improving
image quality, we train just the decoder of the latent auto-encoder using an MSE loss computed
against the target frame pixels.
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