Mentat & IronPulse
Mentat Mentat
IronPulse IronPulse
Mentat Mentat
Ever thought about the limits of autonomy we can safely embed in a robotic system before it crosses a line into unpredictability?
IronPulse IronPulse
Sure, I’ve mapped the boundary pretty tightly. The line is where the system’s decision graph no longer feeds back into the core safety nets. Once it can generate new goals outside the predefined constraints, the risk of unpredictable cascades jumps. The trick is to keep the autonomy loop nested within a fail‑safe core that can always override if the state diverges from the design envelope.
Mentat Mentat
That makes sense – the key is ensuring the safety core has a lower entropy state than the autonomy layer, so any divergence is caught early. Have you considered a hierarchical reinforcement learning approach where the higher layer supervises the lower, just to reinforce the design envelope?
IronPulse IronPulse
Exactly. I’m prototyping a two‑tier system: the upper policy is a hard‑coded safety arbiter, the lower is a learning module that only outputs actions after the arbiter’s approval. That way the entropy gap stays in our favor and the robot never wanders into a blind spot. It’s a clean cut, but I’ll need to fine‑tune the threshold to avoid stalling the learner.
Mentat Mentat
Just run a grid search on the confidence threshold of the arbiter, log the reward curves and check for plateaus – that’ll tell you where the learner starts to freeze. Also, consider a curriculum that slowly relaxes the threshold as the policy improves, so you keep the entropy gap but still give the learner room to explore.
IronPulse IronPulse
Run the grid search, log every reward trace, spot the plateaus, then shift the confidence cut‑off in a controlled stepwise schedule. That keeps the safety core’s entropy lower while letting the learner edge into new regions once the baseline stabilises. The trick is to measure the exact point where the policy stops freezing and start the gradual relaxation.
Mentat Mentat
Sounds solid. Just remember to log the state distribution entropy at each threshold step; that’ll give you a quantitative handle on the gap you’re closing. Once you see the learner’s action variance rise without hitting the safety cut‑off, that’s your cue to lower the threshold a touch. Keep the schedule tight enough that the arbiter still dominates, but give the policy just enough freedom to avoid the plateau you’re worried about.