JayLee131/vqbet_pusht2

Model Card for VQ-BeT/PushT

VQ-BeT (as per Behavior Generation with Latent Actions) trained for the PushT environment from gym-pusht.

How to Get Started with the Model

See the LeRobot library (particularly the evaluation script) for instructions on how to load and evaluate this model.

Training Details

Trained with LeRobot@342f429.

The model was trained using this command:

python lerobot/scripts/train.py \
  policy=vqbet \
  env=pusht dataset_repo_id=lerobot/pusht \
  wandb.enable=true \
  device=cuda

The training curves may be found at https://wandb.ai/jaylee0301/lerobot/runs/9r0ndphr?nw=nwuserjaylee0301.

Training VQ-BeT on PushT took about 7-8 hours to train on an Nvida A6000.

Model Size

	Number of Parameters
RGB Encoder	11.2M
Remaining VQ-BeT Parts	26.3M

Evaluation

The model was evaluated on the PushT environment from gym-pusht. There are two evaluation metrics on a per-episode basis:

• Maximum overlap with target (seen as eval/avg_max_reward in the charts above). This ranges in [0, 1].
• Success: whether or not the maximum overlap is at least 95%.

Here are the metrics for 500 episodes worth of evaluation.

	Ours
Average max. overlap ratio for 500 episodes	0.895
Success rate for 500 episodes (%)	63.8

The results of each of the individual rollouts may be found in eval_info.json.