## Pre-training experiments

**Update, Nov 20 2024**: We fixed the issue of EMA update being too infrequent causing slow pre-training ([commit](https://github.com/irom-princeton/dppo/commit/e1ef4ca1cfbff85e5ae6c49f5e57debd70174616)). Now the number of epochs needed for pre-training can be much lower than those used in the configs (e.g., 3000 for robomimic state and 1000 for robomimic pixel), and we have updated the pre-training configs in v0.7. If you would like to replicate the original experimental results from the paper, please use v0.6.

### Comparing diffusion-based RL algorithms (Sec. 5.1)
Gym configs are under `cfg/gym/pretrain/<env_name>/`, and the config name is `pre_diffusion_mlp`. Robomimic configs are under `cfg/robomimic/pretrain/<env_name>/`, and the name is also `pre_diffusion_mlp`.

**Note**: In both Gym and Robomimic experiments, for the experiments in the paper we used more than enough expert demonstrations for pre-training. You can specify `+train_dataset.max_n_episodes=<number_of_episodes>` to limit the number of episodes so the pre-training is faster.

### Comparing policy parameterizations (Sec. 5.2, 5.3)

Robomimic configs are under `cfg/robomimic/pretrain/<env_name>/`, and the naming follows `pre_<diffusion/gaussian/gmm>_<mlp/unet/transformer>_<img?>`. Furniture-Bench configs are under `cfg/furniture/pretrain/<env_name>/`, and the naming follows `pre_<diffusion/gaussian>_<mlp/unet>`.

### D3IL (Sec. 6)

D3IL configs are under `cfg/d3il/pretrain/avoid_<mode>/`, and the naming follows `pre_<diffusion/gaussian/gmm>_mlp`. In the paper we manually examine the pre-trained checkpoints and pick the ones that visually match the pre-training data the best. We also tune the Gaussian and GMM policy architecture extensively for best pre-training performance. The action chunk size can be specified with `horizon_steps` and the number of denoising steps can be specified with `denoising_steps`.