| --- |
| license: mit |
| library_name: sample-factory |
| tags: |
| - sample-factory |
| - reinforcement-learning |
| - doom_health_gathering_supreme |
| model-index: |
| - name: APPO-doom_health_gathering_supreme |
| results: |
| - task: |
| type: reinforcement-learning |
| name: reinforcement-learning |
| dataset: |
| name: doom_health_gathering_supreme |
| type: doom_health_gathering_supreme |
| metrics: |
| - type: mean_reward |
| value: 11.787799999999942 +/- 5.578349501420757 |
| name: mean_reward |
| --- |
| # APPO Doom Health Gathering Supreme |
|
|
| Trained from random initialization by Kay Zheng (bestdive), with AI coding and execution assistance, for the Hugging Face Deep RL course Unit 8 Part II. No pretrained policy was copied and no evaluation metric was fabricated. Training used a free Colab T4 GPU, seed 42 and 4,005,888 environment frames. |
|
|
| ## Independent evaluation |
|
|
| 100 complete episodes in a separately seeded environment (100001), deterministic actions, latest final checkpoint. Mean reward 11.787799999999942, population standard deviation 5.578349501420757. The course score is 6.209450498579185 (required minimum 5). |
|
|
| These are the **true-objective episode returns** collected in Sample Factory's official enjoyment/evaluation function and used by its model-card generator. We do not substitute the higher shaped training reward. Every return is in evaluation.json. The exact checkpoint loaded is independently recorded in doom-evaluation.log. |
|
|
| ## Reproduction |
|
|
| Python 3.10.12, sample-factory==2.1.1, vizdoom==1.2.4, torch==2.2.2, numpy<2, setuptools<81. Registration and training code comes from the official Hugging Face Unit 8 Part II notebook, with explicit experiment arguments. train_doom.py and evaluate_doom.py contain the executed code. |
|
|
| For inference, place this repository under /content/doom-results/Kay-Doom, copy train_doom.py and evaluate_doom.py into /content, then run evaluate_doom.py. The checkpoint must be in checkpoint_p0/. Training from scratch runs train_doom.py. Change the explicit paths in both scripts for another workspace. |
| |
| This model is limited to this simulated environment and is not evidence of general AI competence. |
| |
