| ---
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| tags:
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| - trajectory-forecasting
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| - spatiotemporal-transformer
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| - pytorch
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| ---
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|
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| # Spatiotemporal Decoder for Fortnite Duo Replays
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| 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`.
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| ## Architecture and data
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| 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.
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| 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.
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| ## Files
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| | File | Purpose | SHA-256 |
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| | --- | --- | --- |
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| | [`encoder_state.pt`](encoder_state.pt) | Frozen encoder tensor dictionary, 124 tensors | `b4c66614d08dbf1c4e4ad6b2cafb78a939d87e17a270dc9801bcac78329d80ee` |
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| | [`decoder_state.pt`](decoder_state.pt) | Decoder and congestion tensor dictionary, 135 tensors | `7c89d654d6c356ffcb5696452b8ede3afd20f9c7d327002fe36e929930eb4126` |
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| | [`best.pt`](best.pt) | Original epoch-20 decoder training checkpoint, including optimizer state | `7e210adfb5453511dd4cda35c1db968245921f8e08848fa51dfb035eaa07adc6` |
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| 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`](model_weights.json) records the source checkpoint and state hashes.
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| ```python
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| import torch
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| from huggingface_hub import hf_hub_download
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| repo = "BiLSTM/SpatioTemporalDecoder"
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| encoder_state = torch.load(
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| hf_hub_download(repo, "encoder_state.pt"),
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| map_location="cpu",
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| weights_only=True,
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| )
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| decoder_state = torch.load(
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| hf_hub_download(repo, "decoder_state.pt"),
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| map_location="cpu",
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| weights_only=True,
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| )
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| ```
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| 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.
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| ## Measured validation result
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| 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.
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| 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.
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