Implementation of the learning-functions brainstorm (L0\u2013L5).<\/p>\n\n\n\n
[text] [text] <\/p>\n","protected":false},"excerpt":{"rendered":" Implementation of the learning-functions brainstorm (L0\u2013L5). Architecture [text]nn learn <verb> \u2192 _MathNNLearning \u251c\u2500 _MathNNExperience (transitions, demos, prefer pairs, jobs) \u251c\u2500 _RLAgentNet (Q\/RM\/WM, Select\/Observe, SupervisedQStep) \u251c\u2500 _MathNNDevice (train\/infer placement) \u2514\u2500 _RLCheckpointMySQL (meta, transfer, quant, federate, promote) Experience store Buffer Filled by Transitions Every RLAgentNet::Observe Last state\/action Select + Observe Demos imitate start + Select, or load […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2012","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts\/2012","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/comments?post=2012"}],"version-history":[{"count":1,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts\/2012\/revisions"}],"predecessor-version":[{"id":2013,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts\/2012\/revisions\/2013"}],"wp:attachment":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/media?parent=2012"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/categories?post=2012"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/tags?post=2012"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/em>nn learn <verb>
\u2192 _MathNNLearning
\u251c\u2500 _MathNNExperience (transitions, demos, prefer pairs, jobs)
\u251c\u2500 _RLAgentNet (Q\/RM\/WM, Select\/Observe, SupervisedQStep)
\u251c\u2500 _MathNNDevice (train\/infer placement)
\u2514\u2500 _RLCheckpointMySQL (meta, transfer, quant, federate, promote)<\/p>\n\n\n\nExperience store<\/h2>\n\n\n\n
Buffer<\/strong><\/td> Filled by<\/strong><\/td><\/tr> Transitions<\/td> Every RLAgentNet::Observe<\/td><\/tr> Last state\/action<\/td> Select + Observe<\/td><\/tr> Demos<\/td> imitate start + Select, or load file<\/td><\/tr> Prefer pairs<\/td> prefer good\/bad and prefer pair<\/td><\/tr> Jobs<\/td> nn learn jobs enqueue \u2026<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n Verbs (implemented)<\/h2>\n\n\n\n
Verb<\/strong><\/td> Real behavior<\/strong><\/td><\/tr> prefer<\/strong><\/td> RM feedback on last state<\/strong>; auto pair buffer<\/td><\/tr> prefer pair \/ train<\/strong><\/td> Explicit pairs + batch RM train<\/td><\/tr> curious<\/strong><\/td> WM on + scale; warm on stored transitions<\/td><\/tr> imitate start\/stop\/train<\/strong><\/td> Demo record + BC<\/strong> via SupervisedQStep<\/td><\/tr> distill<\/strong><\/td> Weight copy + KD<\/strong> matching teacher Q<\/td><\/tr> transfer<\/strong><\/td> Full load; optional encoder fine-tune<\/strong><\/td><\/tr> adapt<\/strong><\/td> RM plasticity + replay mix<\/strong><\/td><\/tr> quantize<\/strong><\/td> Save + cal pass + NPU placement + JSON sidecar<\/td><\/tr> shadow<\/strong><\/td> Peer agent + dual-Q metrics on Select<\/td><\/tr> curriculum<\/strong><\/td> Phases + success-gated<\/strong> next<\/td><\/tr> hierarchy<\/strong><\/td> Options + HierarchyAct<\/strong> worker steps<\/td><\/tr> whatif<\/strong><\/td> Q on last state<\/strong> + WM-style horizon<\/td><\/tr> align<\/strong><\/td> RM + low curiosity + batch caution labels<\/td><\/tr> federate<\/strong><\/td> push\/pull MySQL + avg \u2248 KD toward global<\/strong><\/td><\/tr> promote<\/strong><\/td> multi-metric<\/strong> gate (loss, pref, shadow)<\/td><\/tr> scale<\/strong><\/td> tiny\/default\/large\/huge + device pref<\/td><\/tr> pack<\/strong><\/td> TutorOS, EmbodiedCraft, LifeCoach, Operator, MetaMind, WorldForge<\/td><\/tr> jobs<\/strong><\/td> enqueue \/ drain simple train jobs<\/td><\/tr> experience<\/strong><\/td> Store stats<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n Examples<\/h2>\n\n\n\n
<\/em>nn learn imitate start life
# \u2026 agent acts \u2026
nn learn imitate stop life
nn learn imitate train life 128
nn learn prefer good
nn learn prefer train life 32
nn learn distill life from life_focus_blocks 64
nn learn transfer minecraft space_engineers encoder
nn learn shadow minecraft on
nn learn promote minecraft multi
nn learn pack EmbodiedCraft
nn learn pack LifeCoach
nn learn whatif 0 3
nn learn federate life push
nn learn federate life avg<\/p>\n\n\n\nFiles<\/h2>\n\n\n\n
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Still future (honest)<\/h2>\n\n\n\n
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