Opening your Vision workspace.
Loading your game…
- 1 · Set up
- 2 · Train
- 3 · Check
- 4 · Run
Know the grounds. Set the goal. Then train.
- Environment & assumptions
- Measurable goal
- Reference strategy
- Training phases
- Checks & limits
Objectives & optimization
Example: Success at least 90%; score at least 5; steps at most 500; prioritize efficiency. Separate clauses with semicolons. Use supported metrics shown in Use values. Choose game rules and the task in World; text does not create new mechanics.
Vision
One environment. Repeatable decisions. Visible evidence.
What you teach
Inspect one image tile at a time. Mark each microchip, or compare two pixel images and mark every changed tile. The ledger numbers each mark and groups counts into tens.
Synthetic 16×16 greyscale pixel tiles, scanned in a known 6×4 grid. A linear action neuron learns authored scores from pixel shape coverage or paired pixel differences. It sees pixels, not object IDs or target counts. This is an inspection teaching fixture, not a trained real-photo detector or a medical test.
- World: try the reference, then control a round yourself.
- Train: fit a small action-scoring network away from the animation.
- Results: compare paired seeds, inspect losses and activate checked weights.
- World: test a new seed. Save project to keep your model.
Vision
Watch decisions change the outcome.
The gold frame scans one tile at a time. Mark each target, then inspect the numbered ledger. Counts show bundles of ten and loose items. A completed round reveals missed targets and false marks.
This starter uses generated pixel images. The real hanger-counting workflow inspired the mark ledger and count checks; no hanger detector or medical model is running here.
Manual buttons work with You · manual. Step advances the selected policy. One step is one decision, including the reference opponent response where applicable. Reset after a round ends.
Rules and what the network sees
Inspect one image tile at a time. Mark each microchip, or compare two pixel images and mark every changed tile. The ledger numbers each mark and groups counts into tens.
Synthetic 16×16 greyscale pixel tiles, scanned in a known 6×4 grid. A linear action neuron learns authored scores from pixel shape coverage or paired pixel differences. It sees pixels, not object IDs or target counts. This is an inspection teaching fixture, not a trained real-photo detector or a medical test.
An Alkamy Labs experiment · Explore our labs ↗
From features to an action.
3 learned weights → one score for each legal action.
A single linear neuron scores each action. It receives these engineered features, not a teacher’s chosen action:
- Mark bias
- Dark central shape coverage · pixels
- Paired pixel difference · pixels
Ridge least squares fits the weights from synthetic reference scores. Rules supply legal actions, feature extraction and opponent behaviour. This is imitation learning. It does not discover an optimal strategy through self-play.
Synthetic 16×16 greyscale pixel tiles, scanned in a known 6×4 grid. A linear action neuron learns authored scores from pixel shape coverage or paired pixel differences. It sees pixels, not object IDs or target counts. This is an inspection teaching fixture, not a trained real-photo detector or a medical test.
Train. Freeze. Compare.
Active weights stay in place while the candidate is checked.
64 fitting seeds, 12 validation seeds and 24 final seeds. Validation selects between two ridge strengths; final tests cannot change the selected weights. Maximum 25 seconds of computation, 30-second worker timeout and two attempts per training round.
Ready · no provider or API key needed.
Activation gate: ≥80% near-best reference-score agreement, finite compatible weights and final mean score at least the untrained baseline and a checked incumbent, when present. Passing this small test does not guarantee future wins.
Uses fresh teacher examples and the current two-attempt training round. Review each candidate before activation. Run keeps active weights fixed.
Off. Train manually, or start another training round when both attempts are used.
What changed?
Compare the same seeds. Keep the losses.
Train or check saved weights for a measured comparison.
Complete a round or training run to see observations and a controlled next test.