# jaxgmg2_3phase_optim_state

32 RL agent checkpoints trained on the JaxGMG maze environment with discount_rate=0.98,
with full optimizer state saved at each checkpoint. Primary runs for the 3-phase training regime analysis.
Similar to `jaxgmg2_3phase_unique` but with optimizer state logging enabled.

- 16 runs with alpha=0.6 (run_id 15-30)
- 16 runs with alpha=1.0 (run_id 15-30)

**WandB:** https://wandb.ai/devinterp/jaxgmg2_3phase_optim_state

## Sweep

run_id sweep: 15-30 for each alpha value. Seed is derived from run_id via:
`seed = int(discount_rate*100)*10000 + int(alpha*10)*100 + run_id`
e.g. alpha=0.6, run_id=15 -> seed=980615; alpha=1.0, run_id=15 -> seed=981015.

## Shared Hyperparams

```
rl_action=train
alpha=0.6 or 1.0
discount_rate=0.98
lr=5e-05
num_total_env_steps=10000000000
num_rollout_steps=64
num_levels=9600
cheese_loc=any
env_layout=open
env_size=13
mask_type=first_episode
use_prev_action=False
grad_acc_per_chunk=5
log_optimizer_state=True
eval_schedule=0:1,250:2,500:5,2000:10
seed_formula={int(discount_rate*100):02d}{int(alpha*10):02d}{run_id:02d}
f_str_ckpt=al_{alpha}_g_{discount_rate}_id_{run_id}_seed_{seed}
ckpt_dir=jaxgmg2_3phase_optim_state
wandb_project=jaxgmg2_3phase_optim_state
use_wandb=True
use_hf=True
```

## Naming Schema

Checkpoints are named `al_{alpha}_g_0.98_id_{run_id}_seed_{seed}`.

## Reproduced with

See [`train.yaml`](./train.yaml) in this repository. Run with:

```bash
make run projects/rl/experiments/al_0.6_g_0.98/jobs/train_optim_state.yaml
```

from the [timaeus monorepo](https://github.com/timaeus-research/timaeus).