metadata
license: apache-2.0
pipeline_tag: reinforcement-learning
tags:
- vizdoom
- doom
- reinforcement-learning
- ppo
- behavior-cloning
- modernbert
datasets:
- anakin87/doom-defend-the-center-100k-oracle
model-index:
- name: tiny-doom-defender
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_defend_the_center
type: doom_defend_the_center
metrics:
- type: mean_kills
value: 23.12 +/- 2.81
name: mean_kills
verified: false
tiny-doom-defender
A 1.1M-parameter ModernBERT encoder that plays VizDoom's Defend the Center in real time on a CPU from pixels alone.
📝 Read the full story/see the model play live · 💻 GitHub repo
Inspired by VAGOsolutions/SauerkrautLM-Doom-MultiVec-1.3M.
The model underwent SFT from a programmatic oracle and has been refined with PPO.
Checkpoints in this repo
| Checkpoint | Path | Training | Mean kills | σ | Size |
|---|---|---|---|---|---|
| tiny-doom-defender (default) | root | SFT + PPO | 23.12 | 2.81 | 4.4 MB |
| tiny-doom-defender-sft | sft/ |
SFT only | 22.19 | 3.76 | 4.4 MB |
| tiny-doom-defender-int8 | int8/ |
SFT + PPO, int8 | 23.13 | 2.78 | 1.1 MB |
SauerkrautLM-Doom-MultiVec-1.3M (the inspiration) kills 20.38 enemies, with a standard deviation of 5.35.
Evaluation on 1000 episodes, seeds 10000–10999.
Usage
Install the package, download the repo, and let it play:
pip install git+https://github.com/anakin87/tiny-doom-defender
hf download anakin87/tiny-doom-defender --local-dir tiny-doom-defender
# watch it play in a live DOOM window
play-doom --ckpt tiny-doom-defender
# score it on the held-out test seeds
eval-model --ckpt tiny-doom-defender --episodes 100
Point --ckpt at a subfolder to use the other checkpoints:
eval-model --ckpt tiny-doom-defender/sft --episodes 100
eval-model --ckpt tiny-doom-defender/int8 --episodes 100
Architecture
| Parameters | 1,096,134 |
| Observation | 3 stacked 160×100 RGB frames (t, t−1, t−2) + the 2 previous actions |
| Stem | two stride-2 3×3 convs, 32 channels → a 40×25 = 1000-token grid |
| Encoder | 4-layer ModernBERT, hidden size 128, 4 heads, fed via inputs_embeds |
| Head | learned attention pooling → turn (3) / shoot (2) / value heads |
| Action space | MultiDiscrete([3, 2]) — turn left/none/right × shoot/no-shoot |
