What if a learning agent settling on a policy is the same kind of process as a reaction reaching equilibrium?
Q-learning ⇄ chemical equilibrium · via state
The Bellman fixed-point a Q-learning agent converges toward and the equilibrium a reversible reaction settles into may be the same mathematical object, a fixed point of an update map over 'state', so reinforcement-learning convergence and chemical relaxation could be described by one contraction-mapping framework.
The open question
Is this a real shared structure, or a coincidence of words? A stricter, reasoning-based review flagged that 'state' means different things in the two fields (a Markov decision state versus a thermodynamic state), and that the shared word 'temperature' may be a false friend (an exploration parameter versus a physical temperature). Does a genuine contraction-mapping bridge survive once those are stripped out, with a prediction tying a Q-learning convergence rate to a measurable reaction relaxation?
What the system already tried
This passed our original cross-model jury and was published on Latest. A later, stricter reasoning review raised a real doubt: the bridge may lean on a homonym ('state') and a false friend ('temperature') rather than a shared mechanism. So it moved here, open for you to judge.
The sources it read
Open review
Is this a real connection or a coincidence of shared words? The facts above are grounded in the sources; the leap between them is what is unproven. Make the case, or settle it with a reference.