Life RL is an advisory multi-domain reinforcement-learning surface built on

MathNN and RLAgentNet. It suggests micro-actions for everyday living domains

(sleep, focus, chores, meals, movement, light budget reflection, inbox triage,

social energy, learning streaks, digital hygiene, mood check-ins, errands).

It does not auto-send messages, spend money, prescribe treatment, or control

devices. Sensitive domains emit explicit disclaimers.

See also: ReinforcementLearning.md (ReinforcementLearning.md) ·

MathNN.md (MathNN.md) · AugmentedIntelligenceSystems.md (AugmentedIntelligenceSystems.md)

Code: LifeRL.hpp / LifeRL.cpp. Agent ids: life, life_<domain>.

Safety

[text]
life rl safety

Hard rules:

1. Suggestions only — no external side effects from the policy.
2. Health / finance / mood / relationships are decision-support, not authority.
3. Human good / bad feedback trains a preference model; it does not grant autonomy.
4. Synthetic smoke envs are the default training path.
5. Crisis content should be redirected to professionals; do not “optimize” crisis.

Commands

[text]
life rl status
life rl domains
life rl domain focus_blocks
life rl safety
life rl ideas

life rl smoke all 12
life rl smoke sleep_routine 20
life rl train focus_blocks 50
life rl suggest meal_rhythm low energy after lunch
life rl observe chore_load kitchen is a mess
life rl reward good
life rl reward bad

life rl net on
life rl net status
life rl net save checkpoints/life_mathnn.txt
life rl net load checkpoints/life_mathnn.txt

Aliases: rl life … routes to the same handler.

Domains (initial set)

ID	Category	Sensitive
sleep_routine	health_wellness	no
focus_blocks	work	no
chore_load	home	no
meal_rhythm	food	no
movement_nudge	fitness	no
budget_check	finance	yes
inbox_triage	work	no
social_energy	relationships	yes
learning_streak	education	no
digital_hygiene	security	no
mood_checkin	health_wellness	yes
errand_routing	home	no

Each domain uses a 16-D synthetic state and 8 discrete actions (named

micro-nudges). MathNN agent ids are life and life_<domain>.

MathNN wiring

Concern	Implementation
Policy	RLAgentNet mode dueling-dqn (default) or bandit on sensitive domains
Train	life rl train enables world-model curiosity + reward-model blend
Feedback	`life rl reward good\	bad → _RLAgentNet::Feedback`
Checkpoints	`life rl net save\	load + .meta / .rm / .wm` sidecars

Related:

[text]
rl net mode life_focus_blocks hierarchical
rl net q life_sleep_routine
nn agent list
nn modes

Product brainstorm (ideas)

See also life rl ideas in-app. Highlights:

• Morning brief — fuse sleep + focus + meal into one card of three nudges.
• Energy-aware chores — condition chore policy on stress/energy features.
• Preference co-pilot — weeks of good/bad labels → personal reward model.
• Hierarchical day planner — manager picks domain, worker picks micro-action.
• World-model “what if” — predict stress/focus deltas if sleep is skipped.
• MLApp bridge — life-domain templates as encoders into the 16-D state.
• Calendar masks — meetings as illegal actions for focus/errand policies.
• Household multi-agent — shared chore bandit with fairness regularizer.
• Seasonal curriculum — winter movement harder; tax season budget_check.
• Privacy mode — on-device MathNN only; opt-in metrics export.
• Coaching chat — LLM explains a suggestion without forcing it.
• Eval gates — promote checkpoints only if self-report proxies improve.

Relationship to other systems

System	Relationship
MLAppFeatures	37 life templates (classifiers/regressors); LifeRL is sequential policy
ChildPsychologyParentingRL	Separate, heavily constrained parenting coaching — not merged
Gaming / Minecraft RL	Same MathNN/RLAgentNet substrate; different actuation + safety
Human / RSI agents	Can call ai_nn_policy_* Lua or life rl commands

Quick start

[text]
life rl domains
life rl smoke all 8
life rl net on
life rl suggest focus_blocks
life rl reward good
life rl train sleep_routine 30
life rl net save checkpoints/life_sleep.txt

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Filed under: Uncategorized - @ July 18, 2026 6:39 am