Instructions to use ciCic/decisionTransformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ciCic/decisionTransformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ciCic/decisionTransformer")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ciCic/decisionTransformer") model = AutoModel.from_pretrained("ciCic/decisionTransformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - decisionTransformer | |
| - deep reinforcement | |
| datasets: | |
| - edbeeching/decision_transformer_gym_replay | |
| license: | |
| - mit | |
| ### Running training | |
| - Num examples = 1000 | |
| - Num Epochs = 120 | |
| - Instantaneous batch size per device = 64 | |
| - Total train batch size = 64 | |
| - Gradient Accumulation steps = 1 | |
| - Total optimization steps = 1920 | |
| ### Train Output | |
| - global_step = 1920 | |
| - train_runtime = 1849.2158 | |
| - train_samples_per_second = 64.892 | |
| - train_steps_per_second = 1.038 | |
| - train_loss = 0.04717305501302083 | |
| - epoch = 120.0 | |
| ### Dataset | |
| - edbeeching/decision_transformer_gym_replay | |
| - halfcheetah-expert-v2 |