Reinforcement Learning
Transformers
PyTorch
decision_transformer
Generated from Trainer
deep-reinforcement-learning
decision-transformer
gym-continous-control
Instructions to use RamAnanth1/decision-transformers-walker2d-expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RamAnanth1/decision-transformers-walker2d-expert with Transformers:
# Load model directly from transformers import AutoTokenizer, TrainableDT tokenizer = AutoTokenizer.from_pretrained("RamAnanth1/decision-transformers-walker2d-expert") model = TrainableDT.from_pretrained("RamAnanth1/decision-transformers-walker2d-expert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- generated_from_trainer
- deep-reinforcement-learning
- reinforcement-learning
- decision-transformer
- gym-continous-control
pipeline_tag: reinforcement-learning
datasets:
- decision_transformer_gym_replay
Decision Transformer model trained on expert trajectories sampled from the Gym Walker2d environment
This is a trained Decision Transformer model trained from scratch on expert trajectories sampled from the Gym Walker2d environment based on the modified version of the example training script provided by HuggingFace
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 120
Training results
Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1