Daily Report — 2026-09-10

Daily Overview

• What was done: Advanced RL agent training for complex game layouts, developed a video-based level reconstruction system, and implemented git-synchronized serving for the AI Companion project.
• How it was done: Used PPO with entropy annealing and diverse layout sampling for RL, applied computer vision and frame-diffing for level reconstruction, and utilized vitest/git integration for AI Companion features.
• Impact: Increased RL clearing efficiency on hard levels and established a robust method for converting real-play videos into actionable simulator environments and managing distributed project ideas.

Successfully trained a specialized RL agent for hard-level tile-matching games and implemented a video-to-simulator level reconstruction pipeline, while simultaneously advancing the AI Companion project by building a git-synced serving engine and formalizing multi-repo idea management.

Tasks

Architecture & Strategy

• ✅ RL Model Training (Hard Layouts) — Trained PPO model with entropy annealing (0.02 -> 0.003) and integrated diverse layouts (heart shapes, random levels) to improve generalization.
• ✅ Level Reconstruction Pipeline — Developed video_record.py and rebuild_level.py to transform MP4 screen recordings into JSON level files for the simulator.
• ✅ AI Companion: Git-Sync Serving — Implemented I-137: added --git-sync to the serving engine to allow real-time idea updates via git pull/push.

Implementation & Fixes

• 🔄 AI Companion: Idea Formalization — Filling out architectural details (how/why/future) for the multi-repo portal ideas (I-009 to I-015).
• ✅ Hardware Health Check (OMEN) — Analyzed system logs to document intermittent AX211 Wi-Fi card failures for hardware warranty claims.

Problems & Solutions

Critical Issues

1. RL agent fails to clear ‘ultra-hard’ levels (e.g., level 20) even with full information.

Solution: Conducted beam search (width 1000) and oracle testing; concluded failure is due to structural bottlenecks (deep stacks) rather than policy weakness.

2. Video reconstruction errors due to fast clicking and tray animation overlapping.

Solution: Implemented ‘record-only fast path’ and added logic to handle tray jumps and identify missing tiles via stack depth analysis.

General Issues

3. Intermittent Wi-Fi card failure (Intel AX211) is difficult to demonstrate to support.

Solution: Exported an evidence package including original .evtx logs showing 177+ invalid value errors and 9 total failures.

Human vs AI Approaches

Strategic Level

RL Improvement Strategies

Role	Approach
Human	Suggested adding action-after features (v3) and mixing old/random layouts to prevent overfitting.
AI	Evaluated v3 features (showed regression) and implemented layout diversity/entropy annealing as requested.

Difference Analysis: Human focused on feature engineering and data augmentation; AI handled the technical execution and performance validation.

AI Limitations

Critical Limitations

• AI struggled to understand that certain ‘unwinnable’ levels were structurally impossible rather than policy-driven until rigorous beam-search validation was performed.

Learnings

Key Learnings

• In high-depth tile-matching games, information transparency (knowing hidden tiles) is secondary to structural management (tray capacity vs. stack depth).

Practical Learnings

• Intermittent hardware failures are best combated with system event logs rather than trying to reproduce them live.

Conversation Summaries

Sheep RL Project

✅ RL Training and Hard Level Analysis Optimized PPO training using entropy annealing and diverse layouts. Conducted deep analysis on why advanced models fail on high-depth levels, proving it is a structural limitation (tray capacity) rather than a training issue.

✅ Video-to-Level Reconstruction Built a pipeline to record gameplay from video and reconstruct the exact board state, including hidden tiles and stack layers, for simulator testing.

AI Companion

✅ Implementing Git-Synced Idea Management Developed the ‘serve’ command with git integration (I-137) to sync ideas across repositories, validated with automated tests, and moved to the next phase of planning.

OMEN Hardware Support

✅ Wi-Fi Card Error Documentation Analyzed system logs to gather evidence for HP hardware support regarding intermittent AX211 failures.

Token Usage