https://github.com/danaugrs/huskarl

Deep Reinforcement Learning Framework + Algorithms
https://github.com/danaugrs/huskarl

algorithms artificial-intelligence deep-learning python reinforcement-learning tensorflow

Last synced: 2 days ago
JSON representation

Deep Reinforcement Learning Framework + Algorithms

• Host: GitHub
• URL: https://github.com/danaugrs/huskarl
• Owner: danaugrs
• License: mit
• Created: 2019-02-18T17:01:53.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2023-03-25T00:07:33.000Z (almost 2 years ago)
• Last Synced: 2025-01-03T22:13:22.656Z (9 days ago)
• Topics: algorithms, artificial-intelligence, deep-learning, python, reinforcement-learning, tensorflow
• Language: Python
• Homepage: https://medium.com/@tensorflow/introducing-huskarl-the-modular-deep-reinforcement-learning-framework-e47d4b228dd3
• Size: 42.1 MB
• Stars: 415
• Watchers: 38
• Forks: 51
• Open Issues: 14
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-Tensorflow2 - danaugrs/huskarl

README

        


# Huskarl [![PyPI version](https://badge.fury.io/py/huskarl.svg)](https://badge.fury.io/py/huskarl)

Huskarl is a framework for deep reinforcement learning focused on modularity and fast prototyping.

It's built on TensorFlow 2.0 and uses the `tf.keras` API when possible for conciseness and readability.

Huskarl makes it easy to parallelize computation of environment dynamics across multiple CPU cores.

This is useful for speeding up on-policy learning algorithms that benefit from multiple concurrent sources of experience such as A2C or PPO.

It is especially useful for computationally intensive environments such as physics-based ones.

Huskarl works seamlessly with [OpenAI Gym](https://gym.openai.com/) environments.

There are plans to support multi-agent environments and [Unity3D environments](https://unity3d.ai).

## Algorithms

Several algorithms are implemented and more are planned.

* [x] Deep Q-Learning Network (DQN)

* [x] Multi-step DQN

* [x] Double DQN

* [x] Dueling Architecture DQN

* [x] Advantage Actor-Critic (A2C)

* [x] Deep Deterministic Policy Gradient (DDPG)

* [x] Prioritized Experience Replay

* [ ] Proximal Policy Optimization (PPO)

* [ ] Curiosity-Driven Exploration

## Installation

You can install the latest version from source with:

```

git clone https://github.com/danaugrs/huskarl.git

cd huskarl

pip install -e .

```

If you prefer, you can get the packaged version from [PyPI](https://pypi.org/project/huskarl/):

```

pip install huskarl

```

## Examples

There are three examples included - one for each implemented agent type. To run the examples you will need [`matplotlib`](https://github.com/matplotlib/matplotlib) and [`gym`](https://github.com/openai/gym) installed. 

### [dqn-cartpole.py](https://github.com/danaugrs/huskarl/blob/master/examples/dqn-cartpole.py)

![dqn-cartpole.gif](examples/dqn-cartpole.gif)

### [ddpg-pendulum.py](https://github.com/danaugrs/huskarl/blob/master/examples/ddpg-pendulum.py)

![ddpg-pendulum.gif](examples/ddpg-pendulum.gif)

### [a2c-cartpole.py](https://github.com/danaugrs/huskarl/blob/master/examples/a2c-cartpole.py)

![a2c-cartpole.gif](examples/a2c-cartpole.gif)

## Citing

If you use Huskarl in your research, you can cite it as follows:

```

@misc{salvadori2019huskarl,

    author = {Daniel Salvadori},

    title = {huskarl},

    year = {2019},

    publisher = {GitHub},

    journal = {GitHub repository},

    howpublished = {\url{https://github.com/danaugrs/huskarl}},

}

```

## About

_hùskarl_ in Old Norse means a warrior who works in his/her lord's service.