README.md

# DynaTraj — Training and Data Collection (SB3 + dm_control)

This repo provides two entry-point scripts:
- `sb3_train.py`: Train an RL policy with Stable-Baselines3 on dm_control tasks and periodically save policy checkpoints
- `sb3_collect.py`: Load specific checkpoints and collect fixed-length trajectories into `.npz` datasets with minimal metadata

---

## Environment & Dependencies

- Python 3.10+
- Required:
  - dm_control + MuJoCo: `pip install dm-control mujoco`
  - Reinforcement learning: `pip install stable-baselines3`
  - Utilities: `pip install numpy tqdm torch`
  - Gym API: prefers `gymnasium`, falls back to `gym` (handled automatically)
- Optional (only if you need on-screen rendering during collection):
  - OpenCV UI: `pip install opencv-python`
  - or Matplotlib: `pip install matplotlib`

Note: On headless Linux/remote environments, rendering might require EGL/OSMesa configuration. If you do not render, you do not need any display backend.

---

## Output Layout (Conventions)

- Training checkpoints: `weights/<domain>/<task>/ckpt-<k>.pt`
  - `k` starts from 1; default save interval is every 10,000 timesteps
  - Examples: `ckpt-1.pt` ≈ 10k steps, `ckpt-2.pt` ≈ 20k steps
- Full SB3 model: `weights/sb3_<algo>_<domain>-<task>_seed<seed>_<timestamp>.zip`
- Collected datasets: `dataset/*.npz` with a companion `*_metadata.pkl`

Default absolute paths in this repo:
- Checkpoint root: `/home/lau/sim/DynaTraj/weights`
- Dataset output: `/home/lau/sim/DynaTraj/dataset`

---

## Training

Script: `sb3_train.py`

Supported algorithms (argument is case-sensitive and must be lowercase here):
- `sac`, `ppo`, `td3`

Common args:
- `--domain`: dm_control domain, e.g., `cheetah`, `quadruped` (default: `cheetah`)
- `--task`: task name, e.g., `run`, `walk` (default: `run`)
- `--algo`: `sac|ppo|td3` (default: `sac`)
- `--total_timesteps`: total training steps (default: `500000`)
- `--n_envs`: number of parallel envs (default: `1`; uses sub-process vectorization if >1)
- `--seed`: random seed (default: `0`)
- `--device`: `cpu|cuda|auto` (default: `auto`)
- `--out_dir`: where to save models & checkpoints (default: `/home/lau/sim/DynaTraj/weights`)

Example:
```bash
python /home/lau/sim/DynaTraj/train_sb3_dmcontrol.py \
  --domain cheetah --task run\
  --algo sac \
  --total_timesteps 500000 \
  --out_dir /home/lau/sim/DynaTraj/weights
```

Checkpointing:
- A policy checkpoint is saved every 10,000 global timesteps as `ckpt-<k>.pt` (1-based counter).
- A full SB3 model `.zip` is also saved at the end for plain SB3 loading if needed.

---

## Data Collection

Script: `sb3_collect.py`

Purpose: Run inference with specific training checkpoints and write fixed-length trajectories to `.npz` files plus metadata.

Common args:
- `--domain` / `--task`: must match training (defaults: `cheetah` / `run`)
- `--ckpt_root`: root directory of checkpoints (default: `/home/lau/sim/DynaTraj/weights`)
- `--ckpt_indices`: comma-separated checkpoint indices, e.g., `1,5,10` (note: starts from 1)
- `--trajectories_per_ckpt`: how many trajectories per checkpoint (default: `5120`)
- `--steps_per_trajectory`: steps per trajectory (default: `24`)
- `--out_dir`: dataset output directory (default: `/home/lau/sim/DynaTraj/dataset`)
- `--device`: inference device (default: `cpu`)
- `--render`: optional flag to render frames (requires OpenCV or Matplotlib)
- `--algo`: usually unnecessary. The script reads the real algo name from the checkpoint payload. Only used as a fallback (must be UPPERCASE: `SAC|PPO|TD3`).

Example:
```bash

python sb3_collect.py \
--domain cheetah --task run \
--algo SAC \
--ckpt_root ./weights \
--ckpt_indices 1,20,30,50 \
--trajectories_per_ckpt 1024 \
--steps_per_trajectory 512 \
--out_dir ./dataset \
--device cpu
```

With rendering (optional):
```bash
python /home/lau/sim/DynaTraj/sb3_collect.py \
--domain cheetah --task run \
--algo SAC \
--ckpt_root /home/lau/sim/DynaTraj/weights \
--ckpt_indices 50 \
--trajectories_per_ckpt 5120 \
--steps_per_trajectory 24 \
--out_dir /home/lau/sim/DynaTraj/dataset \
--device cpu \
  --render
```

For Bouncing ball:
python bb_collect.py --trajectories 1024 --steps_per_trajectory 8192


Notes:
- The script searches checkpoints under `ckpt_root/<domain>/<task>/` as `ckpt-<k>.pt`. If you see an error for `ckpt-0.pt`, switch to 1-based indices.
- If your environment has no display backend, simply omit `--render`.

---

## Output Format

For each `k`:
- Dataset file: `<out_dir>/sb3_<domain>_<task>_ckpt<kNNN>_<timestamp>.npz`
- Metadata file: `<out_dir>/sb3_<domain>_<task>_ckpt<kNNN>_<timestamp>_metadata.pkl`

Metadata keys include:
- `domain`, `task`, `algo`, `seed`
- `ckpt_index` (the `k` you collected)
- `trajectories_per_ckpt`, `steps_per_trajectory`
- `total_trajectories`, `total_steps`
- `render` (whether rendering was enabled)

The exact arrays inside `.npz` are defined by the internal `TrajectoryBuffer` implementation. Typically they include time-aligned observation/state tensors, actions, rewards, and done flags.

---
## Data Format
### Transition:
a_t = pi(s_t)  
s_t+1 = f(s_t, a_t)

### 1.Cheetah
- state
  - qpos_t(x,z,pitch,joint) = 9
  - qvel_t(x,z,pitch,joint) = 9
- action
  - tau_t(joint) = 6 


## Tips

- Prefer absolute paths (all examples above use absolute paths).
- Training uses `DummyVecEnv`/`SubprocVecEnv`, automatically flattens dm_control observations and clips actions to env bounds.
- Collection reconstructs an SB3 policy and loads the `policy_state_dict` from each checkpoint; it does not require the full `.zip` model.
- Algo-arg case: training expects lowercase (`sac|ppo|td3`), collection fallback uses UPPERCASE (`SAC|PPO|TD3`).