Reinforcement Learning
Transformers
Safetensors
decision_transformer
Generated from Trainer
deep-reinfocement-learning
rubiks-cube
decision-transformer
Instructions to use hishamcse/decision-transformer-rubikscube-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hishamcse/decision-transformer-rubikscube-v0 with Transformers:
# Load model directly from transformers import AutoTokenizer, TrainableDT tokenizer = AutoTokenizer.from_pretrained("hishamcse/decision-transformer-rubikscube-v0") model = TrainableDT.from_pretrained("hishamcse/decision-transformer-rubikscube-v0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model description
An offline decision transformer based agent for solving Rubiks Cube.
Codes
Github repos(Give a star if found useful):
- https://github.com/hishamcse/DRL-Renegades-Game-Bots
- https://github.com/hishamcse/Advanced-DRL-Renegades-Game-Bots
- https://github.com/hishamcse/Robo-Chess
Kaggle Notebook:
Training and evaluation data
Please see the kaggle notebook: https://www.kaggle.com/code/syedjarullahhisham/drl-advanced-decisiontransformer-rubikscube
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 256
- eval_batch_size: 16
- 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: 20000
Training results
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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