Chapter 01 of 08 · teaching experiments; the confirmatory evidence lives in chapter 08
The world: a maze, a mission, a window
Before anything can be learned, there must be a world to act in. Ours is deliberately austere, because every later claim about learning, failure, and recovery can be checked by looking at it directly.
My goal was to understand this topic deeply. Building the visualizations is what made the ideas concrete for me, and explaining each one in my own words pushed that understanding further still. That is why The Journey exists: it is the path I took, written out, one chapter at a time.
A fresh maze every time
The environment is from the
BabyAI/MiniGrid family: each reset seed generates a new maze of
connected rooms on a -cell grid,
scatters a few colored objects, and issues a one-line instruction
like “go to the grey box”. The episode succeeds when the
agent stands next to the requested object facing it, and ends
unsuccessfully after steps.
Across generated worlds we
counted distinct missions
across six colors and three object kinds, one grammar.
What the agent actually sees
Not the maze. The observation is a symbolic 7×7×3 integer tensor: an egocentric, occlusion-aware crop of the cells in front of the agent, plus a view direction (0–3) and the mission string. The three channels are lookup indices (object kind, color, door state), not pixels; walls block sight, so much of a typical view is the “unseen” symbol.
Step through it yourself
The scrubber below replays the expert episode from above, drawn directly from the symbolic data the models consume. The bright cone on the maze is the field of view; the right pane is the actual network input at that step.
What the agent can do
Three actions, , out of
MiniGrid's seven. Turning rotates the view in place;
forward advances one cell when nothing blocks it. Why
the other four are excluded, and why this set must stay
frozen, becomes clear when corruptions arrive in
chapter 06.
And a first taste of difficulty
The same world as the expert video, driven by uniformly random actions. Random wandering succeeds surprisingly often across many worlds (, measured honestly in chapter 03) but takes on average, against the expert's .
Summary
The world is a procedurally generated maze; the agent sees a 7×7 symbolic crop plus a mission string, and acts with three frozen movement actions. The gap between the world's state and that little window is the subject of the next chapter.