feat: additive Q-learning + outcome-based rewards#4
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- Q-update: EMA → additive (Q = clamp(Q + α*r, floor, ceiling)) - q_init: 0.5 → 0.0 (memories earn value from zero) - q_ceiling: 1.0 added - Outcome resolver: CRM CSV transitions → memory rewards - client_id tagging on memories - resolve CLI command - session-end hook with retrieval reward loop - 73/73 tests pass Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
anthroos
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For prompts >30 chars, inject a reminder to call search_memory before starting the task. Hooks do auto-recall, but targeted manual search catches context the auto-recall misses. Co-authored-by: Ivan Pasichnyk <ivanpasichnyk@gmail.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
anthroos
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- Q-update: EMA → additive (Q = clamp(Q + α*r, floor, ceiling)) - q_init: 0.5 → 0.0 (memories earn value from zero) - q_ceiling: 1.0 added - Outcome resolver: CRM CSV transitions → memory rewards - client_id tagging on memories - resolve CLI command - session-end hook with retrieval reward loop - 73/73 tests pass Co-authored-by: Ivan Pasichnyk <ivanpasichnyk@gmail.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
anthroos
added a commit
that referenced
this pull request
Apr 27, 2026
For prompts >30 chars, inject a reminder to call search_memory before starting the task. Hooks do auto-recall, but targeted manual search catches context the auto-recall misses. Co-authored-by: Ivan Pasichnyk <ivanpasichnyk@gmail.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
Q = clamp(Q + α*r, floor, ceiling))resolveCLI command for manual outcome resolutionTest plan
pytest tests/ -v→ 73/73 pass.envnot in git history🤖 Generated with Claude Code