Metadata-Version: 2.1
Name: jaxdl
Version: 0.0.4
Summary: JAX (Flax) Deep Learning Library
Home-page: https://github.com/patrickhart/jaxdl
Author: Patrick Hart
License: MIT
Description: # JAXDL: JAX (Flax) Deep Learning Library
        
        Clean state-of-the-art JAX/Flax deep learning algorithm implementations:
        
        * Soft-Actor-Critic (SAC) ([arXiv:1812.05905](https://arxiv.org/abs/1812.05905))
        * Twin-Delayed DDPG (TD3) ([arXiv:1802.09477](https://arxiv.org/abs/1802.09477))
        * Transformer ([arXiv:1706.03762](https://arxiv.org/abs/1706.03762); planned)
        * Unified Graph Network Blocks ([arXiv:1806.01261](https://arxiv.org/abs/1806.01261); planned)
        
        If you use JAXDL in your work, please cite this repository as follows:
        
        ```misc
        @misc{jaxdl,
          author = {Hart, Patrick},
          month = {10},
          doi = {10.5281/zenodo.5596512},
          title = {{JAXDL: JAX Deep Learning Algorithm Implementations.}},
          url = {https://github.com/patrickhart/jaxdl},
          year = {2021}
        }
        ```
        
        
        ## Results / Benchmark
        
        ### Continous Control From States
        | HalfCheetah-v2 | Ant-v2 |
        | --- | --- |
        | ![HalfCheetah-v2](https://raw.githubusercontent.com/patrickhart/jaxdl/master/utils/learning_curves/HalfCheetah-v2.png) | ![Ant-v2](https://raw.githubusercontent.com/patrickhart/jaxdl/master/utils/learning_curves/Ant-v2.png) |
        | Reacher-v2 | Humanoid-v2 |
        | ![Reacher-v2](https://raw.githubusercontent.com/patrickhart/jaxdl/master/utils/learning_curves/Reacher-v2.png) | ![Humanoid-v2](https://raw.githubusercontent.com/patrickhart/jaxdl/master/utils/learning_curves/Humanoid-v2.png) |
        
        
        ## Installation
        
        Install JAXDL using PyPi `pip install jaxdl`.
        
        To use MuJoCo 2.1 you need to run `pip install git+https://github.com/nimrod-gileadi/mujoco-py` and place the binaries of MuJoCo in `~/.mujoco/mujoco210`.
        
        
        ## Examples / Getting Started
        
        To get started have a look in the [examples folder](./examples).
        
        To train a reinforcement learning agent run
        
        ```bash
        python examples/run_rl.py \
          --mode=train \
          --env_name=Ant-v2 \
          --save_dir=./tmp/ \
          --config=./examples/configs/sac_config.py
        ```
        
        To visualize the trained agent use
        
        ```bash
        python examples/run_rl.py \
          --mode=visualize \
          --env_name=Ant-v2 \
          --save_dir=./tmp/ \
          --config=./examples/configs/sac_config.py
        ```
        
        
        ## Tensorboard
        
        Monitor the training run using:
        
        ```bash
        tensorboard --logdir=/save_dir/
        ```
        
        
        ## Contributing
        
        Contributions are welcome!
        This repository is meant to provide clean and simple implementations – please consider this when contributing.
Keywords: deep,machine,learning,reinforcement,research
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
