| --- |
| license: mit |
| language: |
| - en |
| pipeline_tag: reinforcement-learning |
| tags: |
| - counter-strike-2 |
| - trajectory-prediction |
| - transformer |
| - esports |
| --- |
| |
| # CS-NET v4 — CS2 Path Prediction & Spatial-Only Models |
|
|
| Trained checkpoints for **CS-NET v4** — a Transformer-based framework for |
| Counter-Strike 2 game-state forecasting. Full open-source code (training, |
| inference, 3D visualizer) is available at |
| **[github.com/Gary2005/cs-net](https://github.com/Gary2005/cs-net)**. |
|
|
| All checkpoints use the **Pro architecture** (d_model=768, ~138.7M params), |
| see `config/pretrain-a100-pro.yaml` in the source repo. |
| |
| ## Files |
| |
| | File | Task | Description | |
| |---|---|---| |
| | `cs-net-v4-pro.pt` | Path prediction (pre-training) | 600k steps. Full model: `{model, global_step}`. Auto-regressively predicts each player's future movement path (16 ticks = 4 s) from a window of the full game state (10 players + bomb + projectiles + raycast depth maps). | |
| | `pretrain-v4-pro-win_rate.pt` | Spatial-only `winrate` | Per-player team win probability from a **single tick** of the game state. `{task, model_state, head_state, config}` — embedder + spatial transformer + linear head. | |
| | `pretrain-v4-pro-alive_end.pt` | Spatial-only `alive_end` | Per-player probability of being alive at round end. Same format. | |
| | `pretrain-v4-pro-future_kill.pt` | Spatial-only `future_kill` | Per-player probability of getting a kill after the current tick. Same format. | |
|
|
| ## Quick start |
|
|
| ```bash |
| git clone https://github.com/Gary2005/cs-net.git |
| cd cs-net |
| conda create -n cs2demo python=3.10 && conda activate cs2demo |
| pip install -r requirements.txt |
| pip install huggingface_hub |
| |
| # download all 4 checkpoints |
| python scripts/download_checkpoints.py --repo-id gary2oos/cs-net-v4 |
| |
| # verify they load correctly (architecture match, finite weights, forward pass) |
| python scripts/test_checkpoints.py --models-dir checkpoints |
| ``` |
|
|
| ### Inference |
|
|
| ```python |
| # Path prediction |
| from scripts.prediction_engine import PredictionEngine |
| engine = PredictionEngine("config/pretrain-a100-pro.yaml", |
| "checkpoints/cs-net-v4-pro.pt", |
| device="cpu", maps_dir="maps/optimized_obj_files") |
| result = engine.predict_at_tick(sample, query_tick=120) # sample = round-level dict |
| |
| # Spatial-only (winrate / alive_end / future_kill) |
| from scripts.spatial_only_predictor import SpatialOnlyPredictor |
| predictor = SpatialOnlyPredictor("checkpoints", device="cpu") |
| out = predictor.predict_round_full(sample) # per-tick per-player probabilities |
| ``` |
|
|
| ### 3D visualizer |
|
|
| ```bash |
| python visualizer/server.py --port 5000 \ |
| --checkpoint checkpoints/cs-net-v4-pro.pt \ |
| --spatial-model-dir checkpoints \ |
| --device cpu |
| # open http://127.0.0.1:5000/ |
| ``` |
|
|
| ## License |
|
|
| MIT (see source repo). |
|
|
