README.md

metadata

license: mit
tags:
  - counter-strike
  - behavioural-cloning
  - imitation-learning
  - pytorch
  - onnx
library_name: pytorch

CSBC — PyTorch port of the Counter-Strike Behavioural Cloning model

A faithful PyTorch/ONNX conversion of Tim Pearce's Counter-Strike Behavioural Cloning model (ak47_sub_55k_drop_d4_dmexpert_28, stateful variant), produced for the Kairos GUI-agent project.

The original model is Keras / TensorFlow 2.3 (CUDA 10.1, won't use a 40-series GPU). This port runs on any modern CUDA via PyTorch: EfficientNet-B0 trunk (ONNX → onnx2torch) + a hand-written stateful ConvLSTM head. Verified numerically equal to Keras (zero-state max diff ~5e-7, 3-frame stateful sequence ~2e-6), ~3 ms/forward on a 4090.

Files

file	what
csbc_backbone.onnx	feedforward EfficientNet-B0 trunk (loaded via onnx2torch)
csbc_head_weights.npz	ConvLSTM2D(256) + 5 dense-head weights (applied in torch)
csbc_ref.npz	Keras reference outputs on fixed inputs (for the torch self-test)

Usage (Kairos)

python examples/csbc_agent/scripts/download_torch_weights.py --repo-id JamesK2W/csbc-pytorch
python examples/csbc_agent/run.py --backend torch --model-dir examples/csbc_agent/models

I/O contract

• Input: one RGB frame → BGR → cv2.resize to 280×150 → float32 (no /255; EfficientNet rescales internally). Tensor shape (1, 150, 280, 3).
• Output: 52-vector — [0:11] keys (w a s d space ctrl shift 1 2 3 r), [11:13] mouse L/R, [13:36] mouse-x argmax (23 buckets), [36:51] mouse-y argmax (15 buckets), [51] value (ignored).
• Stateful: the ConvLSTM state persists across frames; call reset_state() per episode and feed one frame at a time.

License & provenance

Weights derive from the upstream CSBC release (MIT). Academic / offline use only.