CosmicClash/TRAINING.md

# Training the AI bot

Cosmic Clash bots are trained with reinforcement learning (self-play PPO): two
ships in a headless arena share one policy that learns by playing against
itself. Training runs in Python ([Godot RL Agents](https://github.com/edbeeching/godot_rl_agents)
bridge + Stable-Baselines3); the trained policy is exported to a small JSON
file and runs **inside the game** in pure GDScript — shipped bots need no
Python, no .NET, no network.

## How it fits together

- `Game/scenes/training.tscn` + `scripts/training_mode.gd` — headless self-play
  environment: two RL ships, randomized episode starts, goal rewards. Contains
  the vendored godot_rl_agents `Sync` node that talks TCP to the trainer.
- `scripts/ship_ai_controller.gd` — training-side bridge (observations,
  rewards, action mapping). `scripts/ship_observations.gd` is the *shared*
  observation builder — training and in-game inference must stay identical,
  so never fork it.
- `training/train.py` — PPO trainer; launches N parallel headless Godot
  instances (2 agents each).
- `training/export_policy.py` — SB3 checkpoint → JSON policy for the game.
- `scripts/ai_ship_controller.gd` + `scripts/policy_network.gd` — in-game
  inference (GDScript MLP forward pass).
- `training/evaluate.py` — pits two exported policies against each other and
  appends to `training/eval_history.json`.

## Hardware

The environment is our own headless Godot sim — fully cross-platform:

- **Any machine (e.g. the M4 Mac mini)**: fine for pipeline development,
  smoke runs, and short experiments. Env stepping is CPU-bound; the policy is
  a small MLP, so even CPU-only PPO updates are cheap.
- **Linux + NVIDIA GPU (e.g. the RTX 3090 box)**: recommended for real
  multi-hour/overnight runs. PyTorch CUDA works out of the box; more CPU
  cores also mean more parallel Godot instances (`--n-parallel`).

There is no hard GPU requirement (unlike Rocket League tooling) — a GPU
mainly speeds up learning updates on long runs.

For the Linux/3090 remote-training workflow (setup, throughput tuning,
auto-copying results back to the Mac, dashboard over the network), see
[TRAINING_LINUX.md](TRAINING_LINUX.md).

## Setup

Needs Python 3.10+ and a Godot 4.7 binary.

```bash
cd training
python3.12 -m venv .venv          # macOS: brew install python@3.12
.venv/bin/pip install -r requirements.txt
```

On Linux, download the Godot 4.7 Linux binary and point at it:

```bash
export GODOT_BIN=~/godot/Godot_v4.7.1-stable_linux.x86_64
```

(macOS default is `/Applications/Godot.app/Contents/MacOS/Godot`; override
with `GODOT_BIN` or `--godot_bin` if yours lives elsewhere.)

## Run a training session

```bash
cd training
.venv/bin/python train.py --experiment run01 --timesteps 20000000 --n-parallel 6 --speedup 16
```

- Checkpoints land in `training/checkpoints/run01/` every `--checkpoint-every`
  steps (default 100k), plus `final.zip` on exit (also written on Ctrl-C).
- Resume with `--resume checkpoints/run01/final.zip`.
- `--n-parallel` = Godot instances (2 agents each). Scale with CPU cores.
- `--speedup` = in-engine physics speedup. Raise until CPU saturates.
- `--wandb` mirrors logs to Weights & Biases (`pip install wandb` first).

Expect the smoke-run scale (~100k steps) to only learn crude ball-chasing;
real behaviour needs tens of millions of steps (hours on the 3090 box).

### Watch progress

```bash
.venv/bin/tensorboard --logdir training/logs
```

Key curves: `rollout/ep_rew_mean` (should trend up), `rollout/ep_len_mean`
(should trend *down* from 225 as goals end episodes early — 225 action steps
= the 30s episode timeout).

### Reward/observation tuning

Reward weights are exported vars on `ShipAIController` (goal reward on
`TrainingMode`) — tune in `training.tscn`/scripts without touching the
trainer. If you change the *observation* layout (`ship_observations.gd`),
old checkpoints/exports become incompatible: retrain, and bump a note in
your experiment name.

## Export a checkpoint into the game

```bash
cd training
.venv/bin/python export_policy.py checkpoints/run01/final.zip ../Game/bots/hard.json
```

The exporter runs a parity check (JSON forward pass vs SB3 prediction) before
writing. Models live in `Game/bots/`.

## Evaluate progress between checkpoints

TensorBoard shows learning, but "is the new checkpoint actually *better*?"
needs head-to-head play:

```bash
.venv/bin/python export_policy.py checkpoints/run01/ppo_5000000_steps.zip /tmp/candidate.json
.venv/bin/python evaluate.py ../Game/bots/hard.json /tmp/candidate.json --episodes 40
```

Golden-goal episodes (first goal wins, timeout = draw), sides swapped halfway
for fairness, using the exact inference path that ships in-game. Every run
appends to `training/eval_history.json` — the long-term progress record.
Evaluate each new candidate against the previous promoted bot and a fixed
early reference to see absolute progress over time.

## Difficulty tiers

A bot is `(model, reaction_ticks, action_noise)` — configured on the Match
mode (`bot_model_path`, `bot_reaction_ticks`, `bot_action_noise` in
`match.tscn`) or any `AIShipController`:

- **Model**: the main lever. An early checkpoint *is* an easy bot — promote
  e.g. `easy.json` / `medium.json` / `hard.json` from different stages of one
  training run (verify the gaps with `evaluate.py`).
- **reaction_ticks** (default 8 = training cadence): higher = slower
  reactions, easier.
- **action_noise**: adds execution error, easier.

## Curriculum training

Training from scratch with self-play alone hands the network every skill
at once — finishing, defending, positioning, not stalling to a draw — off a
sparse ±40 goal reward. `train.py` has a `curriculum` flag group that stages
this the way you'd coach a human: score first, then also defend, then learn
that a draw is still a failure, and only then spend compute polishing general
movement. Each stage is a normal chained run — a new `--experiment` resumed
via `--resume checkpoints/<previous>/final.zip`, same as any other run —
just with different curriculum flags.

| Stage | Flags | What it teaches |
|---|---|---|
| 1 — score | `--opponent-mode inert --attack-goal-bias 1.0 --no-allow-vertical --no-allow-pitch-roll` | Team 1 is a do-nothing placeholder ship parked at its spawn (an effectively empty net); near-goal resets always target the goal the trainee attacks; the ship can't fly or pitch/roll, only drive and yaw. Isolated finishing practice. |
| 2 — defend too | `--opponent-mode self_play --no-allow-vertical --no-allow-pitch-roll` | Reintroduces a live opponent (self-play) and the default episode-start mix — the same near-goal state is now simultaneously a finishing chance for one side and a defensive save for the other. Locomotion stays grounded. |
| 3 — no draws | `--draw-penalty 5 --reset-std 0.3` | Training episodes are golden-goal (end at the *first* goal), so there's no in-episode goal-margin to penalize — `draw_penalty` is the closest available signal: a one-time penalty when an episode times out with no goal at all, on top of the existing per-tick `time_penalty`. Also lifts the locomotion mask (full 3D controls) by omitting `--allow-vertical`/`--allow-pitch-roll`; pair that with `--reset-std` since the policy never got a reward gradient on those axes before now, so expect a brief re-exploration wobble. |
| 4 — mechanics/refinement | *(no curriculum flags — plain `next_run.sh`)* | Stock self-play, full controls, default reward/start-state mix. This is what all runs before this feature already did. |

All curriculum flags default to leaving Godot's own `@export` defaults
alone (`train.py` only forwards a flag when you pass it), so ordinary runs
are unaffected. Full flag list: `--opponent-mode {self_play,inert,frozen}`,
`--opponent-model <path>` (for `frozen`), `--draw-penalty`,
`--attack-goal-bias`, `--kickoff-chance`, `--near-goal-chance`,
`--allow-vertical`/`--no-allow-vertical`, `--allow-pitch-roll`/`--no-allow-pitch-roll`.

### Running it automatically

`training/curriculum.py` (started via `curriculum.sh`, same detached-tmux
pattern as `start_training.sh`) drives all four stages end to end: for each
stage it runs `run_training.sh` (pull, train, export, commit+push) with that
stage's flags, then evaluates the resulting checkpoint against a reference
bot — the fixed `rookie.json` baseline for stage 1, or the previous stage's
promoted checkpoint for stages 2-4 — over 100 episodes.

```bash
cd training
./curriculum.sh                                              # start/resume the curriculum
./curriculum.sh --seed-checkpoint checkpoints/run11/final.zip # seed stage 1 instead of a fresh policy
```

The gate is deliberately lenient: it blocks a stage only on a **clear
regression** (the reference beating the candidate by 15+ points of win
rate), not "must show improvement." A 40-episode eval already misled us once
in this project — run11 was the first model to deliberately score a goal,
but its head-to-head eval read as a loss on sample noise alone. A strict
gate would have retried that stage forever for the wrong reason; a loose one
still catches a genuinely broken stage. Progress and every attempt's eval
result are logged to `curriculum_state.json` (committed alongside
`eval_history.json` after each attempt).

A stage gets up to 2 retries (3 attempts total, each resuming from that
stage's own previous attempt with a fresh `--reset-std`) before the script
stops and asks for a human look — it will not retry indefinitely or advance
past a stage that keeps failing on its own. Once you've looked at why (more
timesteps? a flag needs adjusting? the eval itself was misleading?), re-run
with `--force-retry` to try again or `--skip-to-next-stage` if you judge the
result good enough despite the gate.

Running a stage by hand (e.g. to experiment with flags before trusting the
orchestrator) still works exactly as the table above describes — just call
`next_run.sh`/`run_training.sh` directly with that stage's flags.

## Self-play notes

By default both ships share the live policy (mirrored, team-relative
observations — see `ship_observations.gd`), so training is against the
current self. `--opponent-mode inert`/`frozen` (see Curriculum training
above) replace that with a placeholder or a fixed exported policy for one
side of a run; `frozen` is a single-fixed-model slice of full league play.
Fixed-opponent training against a *pool* of past checkpoints sampled per
episode (to avoid strategy collapse on long self-play runs) is still
deferred — see TODO.md.