# 2026-03-10

## Session: Neural Lab → Isaac Sim Pivot

### Completed
- Fixed world selection UX: unified map picker, new game button, delete worlds, PM1 loads
- Researched OpenAI hide-and-seek paper (1909.07528) — got exact setup details
- Built `hide_and_seek_openai` environment: 8 walls, 6 boxes, 2 ramps, prep phase, joint zero-sum
- Fixed critical bug: sim.max_ticks not synced to RL env → entire episode was prep phase → 0 rewards
- Added visual training mode: RL env state piped to viewport via /api/sim/state
- Ran 27,347 episodes (6.5M/25M steps) of PPO self-play before pivot
- Vision upgraded to 800 range, 180° arc for OpenAI env

### Isaac Sim Pivot (Pat's direction, ~9:20 AM)
- Pat sent voice message: wants to use NVIDIA Isaac Sim + Isaac Lab instead of homebrew
- Key requirement: the viewport IS the training environment, not separate
- Isaac Sim 5.1.0 already installed at `/isaac-sim/isaac-sim-standalone-5.1.0-linux-aarch64/`
- Isaac Lab already at `/home/pmello/IsaacLab/`
- Next session: verify Isaac Sim runs, set up hide-and-seek env in Isaac Lab, launch proper training

### Anthropic API Issue
- Morning briefing cron failed: "credit balance too low" on Anthropic API
- Pat needs to check billing/credits

### Key Lessons
- Always sync sim.max_ticks with RL env max_steps when using prep_fraction
- Don't show a fake viewport sim separate from training — misleading
- Pat wants unified: what you see = what's training

## Session Summary [10:00:12 ] (auto-generated)
**Agent Profile:**
*   **Role:** DirectMARLEnv Agent
*   **Model:** Anthropic/claude-opus-4-6
*   **Session:** New (Session Startup)
*   **Session ID:** 5774
*   **Target:** Isaac Sim hide-and-seek environment (IPPO/MAPPO)

**Session Status:**
*   **Completed:**
    *   Built 2v2 DirectMARLEnv (2 hiders, 2 seekers).
    *   Configured 4 movable boxes + 2 ramps.
    *   Implemented Line-of-Sight vision (10m range, 135° FOV) with raycast occlusion.
    *   Set up Zero-sum reward system (seekers rewarded, hiders penalized).
    *   Configured IPPO training script.
*   **Still in Progress:**
    *   Verifying `sim_utils.RigidObjectCfg` import path resolution.
    *   Launching full training run (25M steps, 256 parallel envs).
    *   Monitoring GPU throughput and iteration rates.

**Next Step:**
*   Launch the full training run with 25M steps and 256 parallel environments to maximize GPU throughput.
*   Monitor real-time metrics (it/s, iteration count) and update the RESUME.md file with current status.