---
license: mit
tags:
- behavior-cloning
- imitation-learning
- super-smash-bros-melee
- reinforcement-learning
- gaming
library_name: pytorch
---

# MIMIC: Melee Imitation Model for Input Cloning

Behavior-cloned Super Smash Bros. Melee bots trained on human Slippi
replays. Eight character-specific ~20M-parameter transformers that take
a 180-frame window of game state and output controller inputs (main
stick, c-stick, shoulder, buttons) at 60 Hz. Each model plays over
Slippi Online Direct Connect through Dolphin + libmelee.

- **Repo**: https://github.com/erickfm/MIMIC
- **Training data**:
  [erickfm/melee-ranked-replays](https://huggingface.co/datasets/erickfm/melee-ranked-replays)
  — ranked Slippi replays (master/diamond/platinum tier) per character.
- **Base architecture**: Shaw-relative-position causal transformer
  (d_model=512, 6 layers, 8 heads, seq_len=180). Bootstrapped from
  [HAL](https://github.com/ericyuegu/hal) (Eric Gu) and since diverged.
- **Defining MIMIC changes over HAL**: 7-class button head with a
  distinct TRIG class for airdodge/wavedash (HAL's 5-class head can't
  represent airdodge and thus can't wavedash); v2 shard alignment that
  fixes a subtle post-frame-gamestate leak in the training targets
  (see `research-notes-2026-04-11c`); the digital-L-press fix in
  `decode_and_press` (research notes 2026-04-13) without which no
  7-class BC bot wavedashes.

## Current checkpoints (retrained on 2026-04-20 baseline)

Retrained on the post-schema-drop (13 numeric cols), new-transforms
(`tanh_scale` / `linear_max` / `log_max` for velocity / hitlag /
hitstun) basis. See `research-notes-2026-04-20.md` in the MIMIC repo
for methodology + results analysis.

| Character | Run | Train games | Val loss | Step |
|---|---|---|---|---|
| **Fox**          | `fox-20260420-baseline`          | 31,030 | 0.7144 | 32768 |
| **Falco**        | `falco-20260420-baseline`        | 20,882 | 0.7487 | 31392 |
| **Marth**        | `marth-20260420-baseline`        | 11,759 | 0.6664 | 31065 |
| **Sheik**        | `sheik-20260420-baseline`        | 51,751 | 0.6566 | 26160 |
| **Captain Falcon** | `cptfalcon-20260420-baseline`  | 17,557 | 0.7368 | watchdog |
| **Luigi**        | `luigi-20260420-baseline`        |  2,290 | 0.7460 | watchdog |

**Peach**, **Jigglypuff**, and **Ice Climbers** remain on pre-2026-04-20
schemas:

- `peach-20260420-baseline` (val 0.6322) was trained on the 22-col
  schema before the schema drop — loadable via its pickled config.
- `puff` and `ice_climbers` missed the 2026-04-20 retrain cycle due
  to a download-script bug; their existing HF checkpoints are on the
  old schema. **These two are incompatible with the current 13-col
  inference code path.** Will be retrained in a follow-on cycle.

## Repo layout

```
MIMIC/
├── README.md                      # this file
├── fox/
│   ├── model.pt                   # raw PyTorch checkpoint
│   ├── config.json                # ModelConfig (copied from ckpt["config"])
│   ├── metadata.json              # provenance (step, val metrics, notes)
│   ├── mimic_norm.json            # per-feature transforms + params
│   ├── controller_combos.json     # 7-class button combo spec
│   ├── cat_maps.json
│   ├── stick_clusters.json
│   └── norm_stats.json            # per-column mean/std (z-score fallback)
├── falco/       (same layout)
├── marth/       (same layout)
├── sheik/       (same layout)
├── cptfalcon/   (same layout)
├── luigi/       (same layout)
├── puff/        (same layout)
├── ice_climbers/(same layout)
└── peach/       (same layout, pre-drop schema — retrain pending)
```

Each character directory is self-contained — the JSONs are the exact
metadata used during training, copied verbatim from the data dir so
any inference script can load them without touching the MIMIC repo.

## Usage

```bash
git clone https://github.com/erickfm/MIMIC.git
cd MIMIC
bash setup.sh  # installs Dolphin, deps, ISO

# Download all characters
python3 -c "
from huggingface_hub import snapshot_download
snapshot_download('erickfm/MIMIC', local_dir='./hf_checkpoints')
"
```

Run a character against a level-9 CPU:

```bash
python3 tools/play_vs_cpu.py \
  --checkpoint hf_checkpoints/marth/model.pt \
  --dolphin-path ./emulator/squashfs-root/usr/bin/dolphin-emu \
  --iso-path ./melee.iso \
  --data-dir hf_checkpoints/marth \
  --character MARTH --cpu-character FOX --cpu-level 9 \
  --stage FINAL_DESTINATION
```

Or play a bot over Slippi Online Direct Connect:

```bash
python3 tools/play_netplay.py \
  --checkpoint hf_checkpoints/sheik/model.pt \
  --dolphin-path ./emulator/squashfs-root/usr/bin/dolphin-emu \
  --iso-path ./melee.iso \
  --data-dir hf_checkpoints/sheik \
  --character SHEIK \
  --connect-code YOUR#123
```

The MIMIC repo also includes a Discord bot frontend
(`tools/discord_bot.py`) that queues direct-connect matches per user.
See [docs/discord-bot-setup.md](https://github.com/erickfm/MIMIC/blob/main/docs/discord-bot-setup.md).

## Architecture

```
Slippi frame ──► MimicFlatEncoder (Linear 184→512) ──► 512-d per-frame vector
                                                            │
180-frame window ──► + Shaw Relative-Position attention ────┘
                             │
                      6× Pre-Norm Causal Transformer Blocks (512-d, 8 heads, d_ff=2048, GELU, LN)
                             │
                        Autoregressive Output Heads (with detach)
                             │
              ┌──────────────┼───────────────┬────────────┐
          shoulder(3)    c_stick(9)     main_stick(37)  buttons(7)
```

### 7-class button head

| Class | Meaning |
|---|---|
| 0 | A |
| 1 | B |
| 2 | Z |
| 3 | JUMP (X or Y) |
| 4 | TRIG (digital L or R) |
| 5 | A_TRIG (shield grab) |
| 6 | NONE |

HAL's original 5-class head (A / B / Jump / Z / None) has no TRIG class
and structurally can't execute airdodge, which means HAL-lineage bots
can't wavedash. MIMIC's 7-class encoding plus a fix for
`decode_and_press` (which was silently dropping the digital L press
until 2026-04-13) is what enables the wavedashing in the replays.

### Input features (per frame, per player)

Numeric (13):

    pos_x, pos_y, percent, stock, jumps_left,
    speed_air_x_self, speed_ground_x_self,
    speed_x_attack, speed_y_attack, speed_y_self,
    hitlag_left, hitstun_left,
    shield_strength

Flags (5):

    on_ground, off_stage, facing, invulnerable, moonwalkwarning

Per-feature normalization is defined in each character's
`mimic_norm.json`. The active transforms are:

| transform | formula | used for |
|---|---|---|
| `normalize` | `2(x-min)/(max-min) - 1` → [-1, +1] | percent, stock, jumps_left, facing, invulnerable, on_ground |
| `standardize` | `(x - mean) / std` | pos_x, pos_y |
| `invert_normalize` | `2(max-x)/(max-min) - 1` | shield_strength (so "shield broken" is +1) |
| `tanh_scale` | `tanh(x / scale)` | 5 velocities (scale=5 for self, scale=10 for attack) |
| `linear_max` | `x / max` | hitlag_left (max=20) |
| `log_max` | `log1p(clamp(x,0,max)) / log1p(max)` | hitstun_left (max=120) |

Plus categorical embeddings: stage(4d), 2× character(12d),
2× action(32d). Plus the previous-frame controller state as a 56-dim
one-hot (37 stick + 9 c-stick + 7 button + 3 shoulder).

Total input per frame: **184 dimensions** → projected to 512.

Earlier builds (pre-2026-04-20) used a 22-col numeric schema that
included `invuln_left` and 8 ECB corners. Those columns turned out to
be structurally zero for our .slp parse path — libmelee never
populates them — so they were dropped from the schema. See research
notes 2026-04-20 for the audit. Checkpoints trained pre-drop
(`peach-20260420-baseline`) still load via their own pickled config
but use the 202-dim projection path.

## Training

- Model preset: `mimic` (20M params)
- Optimizer: AdamW, LR 3e-4, weight decay 0.01, **no warmup**
- LR schedule: `CosineAnnealingLR` to `eta_min=1e-6`
- Gradient clip: 1.0
- Dropout: 0.2
- Sequence length: **180 frames** (~3 seconds)
- Batch size: 256 per-GPU × 2 RTX 5090s × grad-accum 1 = **eff-batch 512**
- Mixed precision: BF16 AMP with FP32 upcast for relpos attention
  (prevents BF16 overflow in the manual Q@Kᵀ + S_rel computation)
- Max samples: 16.78M (≈ 32,768 steps at eff-batch 512)
- Watchdog: patience=12 evals on val-plateau — some chars finish early
- Reaction delay: 0. v2 shards have `target[i] = buttons[i+1]`, so
  `rd=0` matches inference — do NOT use `--reaction-delay 1` or
  `--controller-offset` with v2 shards.
- `--self-inputs` is required even on v2 shards. Runs without it
  drop the controller-history input entirely and land at val loss ~2.3.

Typical wall-clock per char on 2×RTX 5090: 10-15 min download/extract
+ 20 min parallel `norm_stats` bootstrap + 45-120 min sharding
(depending on char, cptfalcon and sheik are the longest) + ~50 min
training = 2-4 hours.

## Known limitations

1. **Character-locked.** Each model only plays the character it was
   trained on. No matchup generalization. Multi-character training
   with a character embedding is a natural next step but not done.
2. **Small-dataset overfitting on Luigi / Ice Climbers.** Luigi has
   ~2K training games; IC around 5K. Their `_bestloss.pt` is
   early-stopped — either by the patience=12 watchdog during this
   cycle or by inspection in prior cycles. Play quality varies.
3. **Edge guarding and recovery weaknesses.** Bots don't consistently
   go for off-stage edge guards or execute high-skill recovery
   mixups. The training data has these in it, but BC bots under-sample
   long-tail strategic decisions.
4. **No Matchmaking / Ranked.** The Discord bot only joins explicit
   Direct Connect lobbies. Do NOT adapt it for Slippi Online Unranked
   or Ranked — libmelee's README explicitly forbids bots on those
   ladders, and Slippi has not yet opened a "bot account" opt-in
   system.

## Acknowledgments

- **Eric Gu** for [HAL](https://github.com/ericyuegu/hal), the
  reference implementation MIMIC is based on. HAL's architecture,
  tokenization, and training pipeline are the foundation.
- **Vlad Firoiu and collaborators** for
  [libmelee](https://github.com/altf4/libmelee), the Python interface
  to Dolphin + Slippi.
- **Project Slippi** for the Slippi Dolphin fork, replay format, and
  Direct Connect rollback netplay. https://slippi.gg

## License

MIT — see the MIMIC repo's `LICENSE` file.