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