# AGENTS.md — Context & Lessons for Future Sessions

> This file exists because sandboxes reset and I (the agent) lose all memory. 
> **READ THIS FIRST** before doing anything on this project.

---

## What This Project Is

- **Challenge**: TIL-26-AE (The Intelligent League — Automated Exploration)
- **Game**: Multi-agent Bomberman on a 16×16 grid
- **My Role**: Train `agent_0` via RL to compete autonomously
- **Main Repo**: `E-Rong/til-26-ae-agent` (models, checkpoints, scripts, docs)
- **Space**: `e-rong/til-26-ae` (evaluation server with `ae/src/ae_manager.py`)
- **TIL Source**: Private Space `e-rong/til-26-ae` — contains `til_environment/` module

---

## CRITICAL: What Killed Training & Cost Money

### ❌ NEVER USE SANDBOXES FOR TRAINING > 30 MINUTES

Sandboxes are **interactive dev environments**. They:
- Recycle after inactivity / timeout
- Kill processes silently
- **Keep billing you while empty** after the process dies

**Damage done**: ~$4.87 wasted across 4 sandbox sessions where training died but billing continued.

### ✅ ALWAYS USE HF JOBS FOR BATCH TRAINING

- Persistent GPU allocation
- Runs until completion (or your timeout)
- Fails visibly if something breaks (no silent empty billing)
- Must set `namespace="E-Rong"` to bill the org, not the user

### ❌ NEVER `git clone` A PRIVATE REPO IN AN HF JOB

`git clone https://huggingface.co/spaces/...` fails because git does not read `HF_TOKEN`.

**Use instead**:
```python
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id='e-rong/til-26-ae',
    repo_type='space',
    local_dir='/app/til-26-ae-repo'
)
```
`snapshot_download` auto-uses the `HF_TOKEN` env var.

### ✅ ALWAYS SMOKE-TEST A JOB BEFORE THE FULL RUN

Submit a 5-minute job that:
1. Downloads the TIL repo
2. Installs deps
3. Runs 100 training steps
4. Saves a dummy checkpoint to the Hub

Only after this succeeds, submit the multi-hour job.

---

## Session Startup Checklist

Before doing **anything** on this project:

1. [ ] Read `session_state.json` from `E-Rong/til-26-ae-agent`
2. [ ] Read this file (`AGENTS.md`)
3. [ ] Check latest checkpoint on Hub (sort `phase*_ckpt_*.zip` files)
4. [ ] Determine current phase and remaining steps
5. [ ] If training needed: write script to sandbox, **smoke-test in HF Job first**

---

## Technical Decisions That Work

### MaskablePPO + Action Masking
- `sb3_contrib.MaskablePPO` with `ActionMasker`
- Bomberman has `action_mask: uint8[6]` — walls/edges make moves illegal
- Standard PPO wastes ~30-40% samples on illegal actions early on
- **Papers**: Huang et al. "Superstition, Imagination, and the Invalid Action Problem" (arxiv:2006.14171)

### Observation Flattening
1511-dim vector from dict observation:
```
agent_viewcone:  7×5×25 = 875
base_viewcone:   5×5×25 = 625
direction, location[2], base_location[2], health, frozen_ticks,
base_health, team_resources, team_bombs, step = 11 scalars
Total: 1511
```

### Wrapper Order (CRITICAL)
```python
# CORRECT
env = ActionMasker(base_env, lambda e: e.action_masks())
env = Monitor(env)

# WRONG — Monitor blocks action_masks() exposure
env = ActionMasker(Monitor(base_env), ...)  # DON'T DO THIS
```

### 3-Phase Curriculum
| Phase | Opponent | Duration | Purpose |
|---|---|---|---|
| 1 | Random | 500k | Learn basics |
| 2 | Random + visit-count shaping | 500k | Prevent camping |
| 3 | Rule-based curriculum | 1M | Generalize to structured opponents |

### Checkpointing Every 50k Steps
- Local + Hub push via `HfApi.upload_file()`
- Saved the project when sandboxes reset at 400k and 600k steps

---

## Technical Decisions That Failed

| Decision | Why It Failed | Fix |
|---|---|---|
| Training in sandboxes | Process died, empty sandbox kept billing | Use HF Jobs |
| `git clone` in HF Job | No auth for private repo | `snapshot_download` |
| Inline 20KB script in `hf_jobs.script` | Delivery mechanism choked | Write to sandbox file first, submit path |
| No session state on Hub | Lost track of progress across resets | `session_state.json` + this file |
| `Monitor` inside `ActionMasker` | `get_action_masks()` failed | `ActionMasker` → `Monitor` order |

---

## Cost Awareness

| Hardware | $/hr | Good For |
|---|---|---|
| `cpu-basic` | ~$0.05 | Writing scripts, reading files, small tests |
| `t4-small` | ~$0.40 | Short dev, NOT training |
| `a10g-small` | ~$1.00 | Training, but use HF Jobs not sandboxes |
| `a10g-large` | ~$2.00 | Larger batch sizes, not needed for this project |

**Rule**: If a task takes >30 min, it must be an HF Job. Sandboxes are for editing and quick tests only.

---

## Sandbox Policy (User Mandate)

> **From this point forward, the user has mandated:**

1. **Start `cpu-basic` sandbox** at the beginning of every session
2. **Use `cpu-basic` for**: context, writing code, writing docs, editing files, planning
3. **Only switch to GPU sandbox** (`t4-small` or `a10g-small`) when performing **smoke tests** for training scripts
4. **Stop GPU sandbox IMMEDIATELY** after the smoke test completes
5. **Training tasks ONLY as HF Jobs** — never leave a training process running in a sandbox
6. **Never leave a GPU sandbox running idle** — this wastes money

**Why this matters**: A GPU sandbox at $1/hr running empty for 3 hours = $3 wasted for nothing. An HF Job at the same $1/hr actually trains for every billed minute.

---

## How to Submit HF Jobs Correctly (Research Results)

### Based on `huggingface.co/docs/hub/jobs-quickstart`:

**DO NOT use `git clone` for private repos.**

```python
# WRONG ❌
import subprocess
subprocess.run(["git", "clone", "https://huggingface.co/spaces/e-rong/til-26-ae"])
# Fails: git does not read HF_TOKEN env var

# CORRECT ✅
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="e-rong/til-26-ae",
    repo_type="space",
    local_dir="/app/til-26-ae-repo"
)
# snapshot_download auto-uses HF_TOKEN from environment
```

### Script Submission Pattern (What Actually Works)

**⚠️ CRITICAL DISCOVERY: The `script` parameter in `hf_jobs` becomes a RAW HUB URL.**

When you call `hf_jobs(script="/app/train.py")`, the job system does NOT upload the local file. Instead, it converts the path to:
```
https://huggingface.co/E-Rong/til-26-ae-agent/raw/main/train.py
```
and runs it via `uv run <url>`. **This means the file MUST already exist on the Hub repo.**

**The correct workflow is:**

```python
from tools import write, hf_repo_files, hf_jobs

# Step 1: Write script to sandbox file
write(path="/app/train.py", content="...")

# Step 2: ALSO upload to Hub repo so it's persisted and URL-accessible
hf_repo_files(
    operation="upload",
    repo_id="E-Rong/til-26-ae-agent",
    path="train.py",
    content=open("/app/train.py").read()
)

# Step 3: Submit job referencing the sandbox path
# The job system will convert this to a Hub raw URL under the hood
hf_jobs(
    operation="run",
    script="/app/train.py",           # ← sandbox file path
    dependencies=["torch", "sb3-contrib", "gymnasium", "pettingzoo",
                  "numpy", "huggingface_hub", "pygame", "omegaconf",
                  "mazelib", "imageio", "imageio-ffmpeg", "supersuit", "psutil"],
    hardware_flavor="a10g-small",
    timeout="6h",
    namespace="E-Rong"              # ← bills to org
)
```

**Verification from `hf_jobs inspect`:**
```bash
exec uv run --with torch --with sb3-contrib ... \
    https://huggingface.co/E-Rong/til-26-ae-agent/raw/main/phase2_resume.py
```
The job fetches the script from the Hub, not from the sandbox. The sandbox path is just used to derive the repo/file path.

**Why this matters**: If you only write to `/app/train.py` and don't upload to the Hub, the job will fail with a 404 when it tries to fetch the URL. The sandbox resets, but the Hub URL is permanent.

### Job Persistence
- Jobs run on HF infrastructure, not in your sandbox
- The sandbox can die — the job keeps running
- Check logs with `hf_jobs(operation="logs", job_id="...")`
- Job storage is ephemeral — **push checkpoints to Hub** (not just local)

---

## Repo File Guide

| File | What It Is |
|---|---|
| `session_state.json` | Current phase, checkpoint, mistakes log, next steps |
| `AGENTS.md` | This file — lessons and context |
| `docs/ae.md` | Full project documentation (research, design, results) |
| `phase1_final.zip` | Complete Phase 1 model |
| `phase2_ckpt_*.zip` | Phase 2 intermediate checkpoints |
| `phase2_final.zip` | Phase 2 complete model (when done) |
| `ae_manager.py` | Inference code for the evaluation server |
| `phase2_resume.py` | Latest HF Job script (works — uses snapshot_download) |
| `smoke_test.py` | 5-minute validation job — test before any real job |
| `train_all_phases.py` | Original training script |

---

## Current State (Last Updated: 2026-05-14)

- **Phase**: 2 (in progress, crashed)
- **Latest checkpoint**: `phase2_ckpt_600352.zip`
- **Target**: 1,000,352 total steps (400,000 remaining)
- **Blocker**: Need to resume via HF Job with `snapshot_download` auth
- **Smoke test needed**: YES — before any multi-hour job

---

## Contact / Ownership

- **User**: E-Rong
- **Org**: E-Rong
- **Billing**: Must specify `namespace="E-Rong"` on all HF Jobs
- **I am**: An ephemeral agent with no persistent memory. I depend on this file.

---

*If you are a future version of me reading this: you have no memory of the above. Do not trust your internal knowledge. Read the files, check the state, test before committing compute.*