3853988af7
- Fixed PSO budget calculation bug in ml/src/hyperopt/optimizer.rs - Root cause: Division by n_particles in sequential execution - Now correctly calculates max_iters = remaining_trials (no division) - Result: 50 trials complete instead of 23 (100% vs 46%) - Added comprehensive DQN hyperopt results analysis - 39/50 trials analyzed across 2 RunPod deployments - Best hyperparameters identified: LR 4.89e-5 (ultra-low) - Created DQN_HYPEROPT_RESULTS_SUMMARY.md with expert validation - GitLab CI/CD pipeline operational (48 lines fixed) - Fixed YAML syntax errors (unquoted colons) - All 7 jobs validated and working - Warning cleanup complete (136 → 0 warnings) - Removed 143 lines dead code - Fixed visibility, unused imports, Debug traits - Archived Wave D reports to docs/archive/ - 8 early stopping reports moved - Root directory cleaned up 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
59 lines
1.4 KiB
Plaintext
59 lines
1.4 KiB
Plaintext
HYPEROPT COMPLETION DECISION - QUICK REFERENCE
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==============================================
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Date: 2025-11-02
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Status: ⚠️ INSUFFICIENT - PPO NEEDS COMPLETION
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SUMMARY
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-------
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✅ DQN: CONVERGED (16/50 trials, -5.56% improvement)
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⚠️ PPO: IMPROVING (23/50 trials, +116.59% improvement)
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RECOMMENDATION
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--------------
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⭐ DEPLOY PPO COMPLETION ONLY
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Cost: $0.056
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Duration: 13.5 minutes
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ROI: 900-1800% improvement per dollar
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Expected: 2-3x objective improvement (0.000139 → 0.0002-0.0003)
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DEPLOYMENT COMMAND
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------------------
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./deploy_ppo_hyperopt_completion.sh
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CONVERGENCE EVIDENCE
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--------------------
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DQN:
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First half best: 0.000591
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Second half best: 0.000558
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Improvement: -5.56% (PLATEAUED ✅)
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PPO:
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First half best: 0.000064
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Second half best: 0.000139
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Improvement: +116.59% (STILL IMPROVING ⚠️)
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BEST HYPERPARAMETERS (CURRENT)
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-------------------------------
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DQN (PRODUCTION READY):
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Objective: 0.000591
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Learning Rate: 0.000166
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Gamma: 0.9833
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PPO (PENDING COMPLETION):
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Objective: 0.000139
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Policy LR: 0.000784
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Value LR: 0.000391
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Clip Epsilon: 0.2284
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NEXT STEPS
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----------
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1. Deploy: ./deploy_ppo_hyperopt_completion.sh
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2. Monitor: python3 scripts/python/runpod/monitor_logs.py <POD_ID>
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3. Wait: ~13.5 minutes
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4. Analyze: See HYPEROPT_COMPLETION_ANALYSIS.md for commands
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DETAILED ANALYSIS
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-----------------
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See: HYPEROPT_COMPLETION_ANALYSIS.md
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