[All work](/work?type=work)

# Sentient Environment Explorer

A small grid world where an agent has to find food and avoid hazards, and a neural learner works out which moves to repeat.

Independent experiment development. Python, deep Q-learning and simulation UI.

![A grid world with a red path stepping through it.](https://cdn.sanity.io/images/qnuj1c4o/production/5e1d2a1ffe6216ee395a888f72ee615d582b00af-1600x900.png?w=1280\&q=80\&auto=format)

Food, hazards and hunger give movement a consequence, and the environment, the learner and the orchestrator are separate, so a run can be paused and watched while it's still learning.

Saved checkpoints from earlier runs are in the repo. They're not good agents, they're just what came out.

## State

A visual reinforcement-learning prototype with controls and checkpoints.

[All work](/work?type=work)