Upload policy weights, train config and readme
Browse files- README.md +62 -0
- config.json +50 -0
- model.safetensors +3 -0
- train_config.json +157 -0
README.md
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---
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datasets: aractingi/pick_lift_cube_pipeline_test
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library_name: lerobot
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license: apache-2.0
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model_name: reward_classifier
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pipeline_tag: robotics
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tags:
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- reward_classifier
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- robotics
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- lerobot
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---
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# Model Card for reward_classifier
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<!-- Provide a quick summary of what the model is/does. -->
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A reward classifier is a lightweight neural network that scores observations or trajectories for task success, providing a learned reward signal or offline evaluation when explicit rewards are unavailable.
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This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
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See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
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---
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## How to Get Started with the Model
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For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
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Below is the short version on how to train and run inference/eval:
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### Train from scratch
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```bash
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lerobot-train \
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--dataset.repo_id=${HF_USER}/<dataset> \
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--policy.type=act \
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--output_dir=outputs/train/<desired_policy_repo_id> \
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--job_name=lerobot_training \
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--policy.device=cuda \
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--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
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--wandb.enable=true
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```
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_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
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### Evaluate the policy/run inference
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```bash
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lerobot-record \
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--robot.type=so100_follower \
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--dataset.repo_id=<hf_user>/eval_<dataset> \
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--policy.path=<hf_user>/<desired_policy_repo_id> \
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--episodes=10
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```
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Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
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---
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## Model Details
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- **License:** apache-2.0
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config.json
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{
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"type": "reward_classifier",
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"n_obs_steps": 1,
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"input_features": {
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"observation.images.top": {
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"type": "VISUAL",
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"shape": [
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3,
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128,
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128
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]
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},
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"observation.state": {
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"type": "STATE",
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"shape": [
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18
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]
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}
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},
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"output_features": {
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"action": {
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"type": "ACTION",
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"shape": [
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4
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]
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}
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},
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"device": "mps",
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"use_amp": false,
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"push_to_hub": true,
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"repo_id": "fracapuano/svla101_reward_classifier",
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"private": null,
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"tags": null,
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"license": null,
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"name": "reward_classifier",
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"num_classes": 2,
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"hidden_dim": 256,
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"latent_dim": 256,
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"image_embedding_pooling_dim": 8,
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"dropout_rate": 0.1,
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"model_name": "helper2424/resnet10",
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"model_type": "cnn",
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"num_cameras": 1,
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"learning_rate": 0.0001,
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"weight_decay": 0.01,
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"grad_clip_norm": 1.0,
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"normalization_mapping": {
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"VISUAL": "MEAN_STD"
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}
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:77f9dfb5ebd24d0539728bdd80519be30f22655cc5a4a0c044e370a1bed70ec1
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size 24357748
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train_config.json
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{
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"dataset": {
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"repo_id": "aractingi/pick_lift_cube_pipeline_test",
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"root": "/Users/fracapuano/.cache/huggingface/lerobot/aractingi/pick_lift_cube_pipeline_test_cropped_resized",
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"episodes": null,
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"image_transforms": {
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"enable": false,
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"max_num_transforms": 3,
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"random_order": false,
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"tfs": {
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"brightness": {
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"weight": 1.0,
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"type": "ColorJitter",
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"kwargs": {
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"brightness": [
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0.8,
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1.2
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]
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}
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},
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"contrast": {
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"weight": 1.0,
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"type": "ColorJitter",
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"kwargs": {
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"contrast": [
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0.8,
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1.2
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]
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}
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},
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"saturation": {
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"weight": 1.0,
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"type": "ColorJitter",
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"kwargs": {
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"saturation": [
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0.5,
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1.5
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]
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}
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},
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"hue": {
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"weight": 1.0,
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"type": "ColorJitter",
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"kwargs": {
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"hue": [
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-0.05,
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0.05
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]
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}
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},
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"sharpness": {
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"weight": 1.0,
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"type": "SharpnessJitter",
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"kwargs": {
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"sharpness": [
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0.5,
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]
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}
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}
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}
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},
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"revision": null,
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"use_imagenet_stats": true,
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"video_backend": "torchcodec",
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"streaming": false
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},
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"env": null,
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"policy": {
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"type": "reward_classifier",
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"n_obs_steps": 1,
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"input_features": {
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"observation.images.top": {
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"type": "VISUAL",
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"shape": [
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},
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"observation.state": {
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"type": "STATE",
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"shape": [
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]
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}
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},
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"output_features": {
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"action": {
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"type": "ACTION",
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"shape": [
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]
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}
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},
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"device": "mps",
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"use_amp": false,
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"push_to_hub": true,
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"repo_id": "fracapuano/svla101_reward_classifier",
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"private": null,
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"tags": null,
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"license": null,
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"name": "reward_classifier",
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"num_classes": 2,
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"hidden_dim": 256,
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"latent_dim": 256,
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"image_embedding_pooling_dim": 8,
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"dropout_rate": 0.1,
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"model_name": "helper2424/resnet10",
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"model_type": "cnn",
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"num_cameras": 1,
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"learning_rate": 0.0001,
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"weight_decay": 0.01,
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"grad_clip_norm": 1.0,
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"normalization_mapping": {
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"VISUAL": "MEAN_STD"
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}
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},
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"output_dir": "outputs/train/2025-09-27/11-47-40_reward-classifier",
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"job_name": "reward-classifier",
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"resume": false,
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"seed": 2,
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"num_workers": 4,
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"batch_size": 16,
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"steps": 5000,
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"eval_freq": 1000,
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"log_freq": 10,
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"save_checkpoint": true,
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"save_freq": 1000,
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"use_policy_training_preset": true,
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"optimizer": {
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"type": "adamw",
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"lr": 0.0001,
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"weight_decay": 0.01,
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"grad_clip_norm": 1.0,
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"betas": [
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0.9,
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0.999
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],
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"eps": 1e-08
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},
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"scheduler": null,
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"eval": {
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"n_episodes": 50,
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"batch_size": 50,
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"use_async_envs": false
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},
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"wandb": {
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"enable": true,
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"disable_artifact": false,
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"project": "reward-classifier",
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"entity": null,
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"notes": null,
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"run_id": "2whm4jd4",
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"mode": null
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}
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}
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