| Name | x-transformers-rl JSON |
| Version |
0.0.95
JSON |
| download |
| home_page | None |
| Summary | X-Transformer for RL |
| upload_time | 2025-07-26 15:13:55 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 Phil Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
artificial intelligence
deep learning
evolution
reinforcement learning
transformers
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
gymnasium
moviepy
x-transformers-rl
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
## x-transformers-rl (wip)
Implementation of a transformer for reinforcement learning using `x-transformers`
## Install
```bash
$ pip install x-transformers-rl
```
## Usage
```python
import numpy as np
class Sim:
def reset(self, seed = None):
return np.random.randn(5) # state
def step(self, actions):
return np.random.randn(5), np.random.randn(1), False # state, reward, done
sim = Sim()
# learning
from x_transformers_rl import Learner
learner = Learner(
state_dim = 5,
num_actions = 2,
reward_range = (-1., 1.),
max_timesteps = 10,
world_model = dict(
attn_dim_head = 16,
heads = 4,
depth = 1,
)
)
learner(sim, 100)
```
## Example
### Lunar Lander
```bash
$ pip install -r requirements.txt
```
Then
```python
$ python train_lander.py
```
## Citation
```bibtex
@inproceedings{Wang2025EvolutionaryPO,
title = {Evolutionary Policy Optimization},
author = {Jianren Wang and Yifan Su and Abhinav Gupta and Deepak Pathak},
year = {2025},
url = {https://api.semanticscholar.org/CorpusID:277313729}
}
```
```bibtex
@article{Schulman2017ProximalPO,
title = {Proximal Policy Optimization Algorithms},
author = {John Schulman and Filip Wolski and Prafulla Dhariwal and Alec Radford and Oleg Klimov},
journal = {ArXiv},
year = {2017},
volume = {abs/1707.06347},
url = {https://api.semanticscholar.org/CorpusID:28695052}
}
```
```bibtex
@article{Farebrother2024StopRT,
title = {Stop Regressing: Training Value Functions via Classification for Scalable Deep RL},
author = {Jesse Farebrother and Jordi Orbay and Quan Ho Vuong and Adrien Ali Taiga and Yevgen Chebotar and Ted Xiao and Alex Irpan and Sergey Levine and Pablo Samuel Castro and Aleksandra Faust and Aviral Kumar and Rishabh Agarwal},
journal = {ArXiv},
year = {2024},
volume = {abs/2403.03950},
url = {https://api.semanticscholar.org/CorpusID:268253088}
}
```
```bibtex
@article{Lee2025HypersphericalNF,
title = {Hyperspherical Normalization for Scalable Deep Reinforcement Learning},
author = {Hojoon Lee and Youngdo Lee and Takuma Seno and Donghu Kim and Peter Stone and Jaegul Choo},
journal = {ArXiv},
year = {2025},
volume = {abs/2502.15280},
url = {https://api.semanticscholar.org/CorpusID:276558261}
}
```
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"description": "## x-transformers-rl (wip)\n\nImplementation of a transformer for reinforcement learning using `x-transformers`\n\n## Install\n\n```bash\n$ pip install x-transformers-rl\n```\n\n## Usage\n\n```python\nimport numpy as np\n\nclass Sim:\n def reset(self, seed = None):\n return np.random.randn(5) # state\n\n def step(self, actions):\n return np.random.randn(5), np.random.randn(1), False # state, reward, done\n\nsim = Sim()\n\n# learning\n\nfrom x_transformers_rl import Learner\n\nlearner = Learner(\n state_dim = 5,\n num_actions = 2,\n reward_range = (-1., 1.),\n max_timesteps = 10,\n world_model = dict(\n attn_dim_head = 16,\n heads = 4,\n depth = 1,\n )\n)\n\nlearner(sim, 100)\n```\n\n## Example\n\n### Lunar Lander\n\n```bash\n$ pip install -r requirements.txt\n```\n\nThen\n\n```python\n$ python train_lander.py\n```\n\n## Citation\n\n```bibtex\n@inproceedings{Wang2025EvolutionaryPO,\n title = {Evolutionary Policy Optimization},\n author = {Jianren Wang and Yifan Su and Abhinav Gupta and Deepak Pathak},\n year = {2025},\n url = {https://api.semanticscholar.org/CorpusID:277313729}\n}\n```\n\n```bibtex\n@article{Schulman2017ProximalPO,\n title = {Proximal Policy Optimization Algorithms},\n author = {John Schulman and Filip Wolski and Prafulla Dhariwal and Alec Radford and Oleg Klimov},\n journal = {ArXiv},\n year = {2017},\n volume = {abs/1707.06347},\n url = {https://api.semanticscholar.org/CorpusID:28695052}\n}\n```\n\n```bibtex\n@article{Farebrother2024StopRT,\n title = {Stop Regressing: Training Value Functions via Classification for Scalable Deep RL},\n author = {Jesse Farebrother and Jordi Orbay and Quan Ho Vuong and Adrien Ali Taiga and Yevgen Chebotar and Ted Xiao and Alex Irpan and Sergey Levine and Pablo Samuel Castro and Aleksandra Faust and Aviral Kumar and Rishabh Agarwal},\n journal = {ArXiv},\n year = {2024},\n volume = {abs/2403.03950},\n url = {https://api.semanticscholar.org/CorpusID:268253088}\n}\n```\n\n```bibtex\n@article{Lee2025HypersphericalNF,\n title = {Hyperspherical Normalization for Scalable Deep Reinforcement Learning},\n author = {Hojoon Lee and Youngdo Lee and Takuma Seno and Donghu Kim and Peter Stone and Jaegul Choo},\n journal = {ArXiv},\n year = {2025},\n volume = {abs/2502.15280},\n url = {https://api.semanticscholar.org/CorpusID:276558261}\n}\n```\n",
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"license": "MIT License\n \n Copyright (c) 2025 Phil Wang\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.",
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