mario-dqn-workshop

Educational Super Mario Bros. World 1-1 Deep Q-Network (DQN) checkpoints for the SLM-RL Colab workshop.

These weights exist so a 15–20 minute live-training cell can start from a warm-start policy and so a final evaluation cell can show a stronger public policy. They are not a product, a Nintendo-approved model, or a replacement for the workshop’s Atari RAM-vector teacher.

Files

File	Role
warm-start.chkpt	Early World 1-1 play. Default starting point for live training.
final.chkpt	Later World 1-1 play. Default public evaluation policy.
config.json	Action map, frame stack, and preprocessing.
checksums.json	SHA-256 pins for the checkpoint files.
metrics.json	Recorded evaluation distances and rewards.

A random / untrained baseline is generated locally. It is not stored here.

Environment

• Title: Super Mario Bros. World 1-1 (SuperMarioBros-1-1-v0)
• Observation: 4 stacked 84×84 grayscale frames
• Actions: RIGHT, RIGHT+A
• Encoder: Nature-style CNN
• Frame skip: 4

How the notebook uses this repo

The Colab cell MARIO_MODEL_REPO defaults to BLANK/mario-dqn-workshop and pins MARIO_MODEL_REVISION. Downloads are anonymous. A checksum mismatch or network failure falls back to the committed workshop clips.

Live training writes local-trained.chkpt on the Colab disk. Participant uploads stay off unless the participant supplies their own token.

Limitations

• A short workshop run does not equal full training.
• Super Mario Bros is owned by Nintendo. These weights are educational evidence only.
• The workshop’s production teacher remains the Atari RAM-vector MLP in slm_rl.teachers.dqn.