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freefire-aim-ai
amer224/freefire-aim-ai
freefire-aim-ai is a reinforcement learning model from amer224. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch.
Pixels (84x84) → aim deltas + fire decision. NatureCNN actor-critic (~1.7M params) trained with BC warm-start + PPO + curriculum in a custom Free-Fire-like aim simulator. Beats nothing yet in real games — this is rese…
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.pt745 MB · 89%
At a glance
- Task
- Reinforcement Learning
- Library
- pytorch
- Access
- Public
- Created
- Sep 5, 2026
- Updated
- Sep 8, 2026
- SHA
- 7af72dd1
Model card
From the Hugging Face model README
FreeFire aim agent (Phase 1) — amer224/freefire-aim-ai
Pixels (84x84) → aim deltas + fire decision. NatureCNN actor-critic (~1.7M params) trained with BC warm-start + PPO + curriculum in a custom Free-Fire-like aim simulator. Beats nothing yet in real games — this is research scaffolding, and the numbers below are from the controlled simulator on unseen seeds, not live play.
Latest training tail
- upd5020 g5140480 L4 HS/hits=nan% acc=nan% kill=nan%
- upd5021 g5141504 L4 HS/hits=nan% acc=nan% kill=nan%
- upd5022 g5142528 L4 HS/hits=nan% acc=nan% kill=nan%
- upd5023 g5143552 L4 HS/hits=nan% acc=nan% kill=nan%
- upd5024 g5144576 L4 HS/hits=nan% acc=nan% kill=nan%
Files
best.pt— best-by-unseen-HS/hits checkpoint (modelstate_dict + meta)latest.pt— most recent checkpoint (resume:model,optimizer,scaler,global_step,level)config.yaml,metrics.csv— exact config + full log
Resume / eval
pip install -r requirements.txt
PYTHONPATH=. python3 -m src.train --config configs/base.yaml # auto-resumes
PYTHONPATH=. python3 -m src.evaluate --ckpt best.pt --level 1 --episodes 100
- Task
- Reinforcement Learning
- Library
- pytorch
- Created
- Sep 5, 2026
- Updated
- Sep 8, 2026