tags:
- trajectory-forecasting
- spatiotemporal-transformer
- pytorch
Spatiotemporal Decoder for Fortnite Duo Replays
This is an experimental 60-second team-centroid movement forecast model trained on supported Fortnite duo server replays. It is a custom PyTorch architecture, not a Transformers from_pretrained model. The matching parser, tensorizer, encoder, decoder, training, and evaluation source are distributed in the companion public source release under github-spatiotemporal-decoder.
Architecture and data
Five-second replay snapshots become 256-dimensional team states. Two spatial attention layers model living teams at each tick; four causal temporal layers use observed ticks. A four-layer parallel decoder predicts 12 five-second horizons and five Gaussian route modes. Targets are current-centroid-relative coordinates on a fixed 32 x 32 engineered model grid.
Training used 1,150 accepted pilot sessions: 920 train, 115 validation, and 115 reserved test. Every accepted session has data warnings and incomplete tournament provenance. The reserved decoder-test split was not evaluated.
Files
| File | Purpose | SHA-256 |
|---|---|---|
encoder_state.pt |
Frozen encoder tensor dictionary, 124 tensors | b4c66614d08dbf1c4e4ad6b2cafb78a939d87e17a270dc9801bcac78329d80ee |
decoder_state.pt |
Decoder and congestion tensor dictionary, 135 tensors | 7c89d654d6c356ffcb5696452b8ede3afd20f9c7d327002fe36e929930eb4126 |
best.pt |
Original epoch-20 decoder training checkpoint, including optimizer state | 7e210adfb5453511dd4cda35c1db968245921f8e08848fa51dfb035eaa07adc6 |
The tensor-only exports have been reloaded with PyTorch's weights_only=True mode and match the tensor-state digests embedded in the original encoder and decoder checkpoints. model_weights.json records the source checkpoint and state hashes.
import torch
from huggingface_hub import hf_hub_download
repo = "BiLSTM/SpatioTemporalDecoder"
encoder_state = torch.load(
hf_hub_download(repo, "encoder_state.pt"),
map_location="cpu",
weights_only=True,
)
decoder_state = torch.load(
hf_hub_download(repo, "decoder_state.pt"),
map_location="cpu",
weights_only=True,
)
The dictionaries require the matching source code and model configuration to run inference. Load the encoder dictionary into SpatiotemporalEncoder with strict state loading; load the decoder dictionary through load_downstream_state after constructing the matching ParallelTrajectoryModel. The original best.pt is a Python pickle based training checkpoint and should be loaded only from a trusted source.
Measured validation result
The epoch-20 checkpoint was selected by lowest validation route-mixture negative log-likelihood, 7.0043 across 3,680 queries and 42,265 valid future points. Its highest-probability route mean had 75.128 m average displacement error and 127.725 m final displacement error at 60 seconds on the 115-session validation split. Static-position baselines measured 80.124 m and 143.711 m respectively. The loss was still decreasing at the configured 20-epoch endpoint.
These are development-validation measurements. They do not establish new-tournament generalization, optimal rotations, or production readiness. The raw replays and private decoded corpus are not included.