Metadata-Version: 2.1
Name: deepair
Version: 0.2.0
Summary: PyTorch implementations of Deep reinforcement learning algorithms.
Home-page: https://github.com/sonnhfit/deepair
Author: Son Nguyen Huu
Author-email: sonnhfit@gmail.com
License: MIT
Keywords: reinforcement-learning-algorithms reinforcement-learning machine-learning deep rl
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.7.*
Description-Content-Type: text/markdown
License-File: LICENSE

# Deepair is a Deep Reinforcement Learning library

[![PyPI version](https://badge.fury.io/py/deepair.svg)](https://badge.fury.io/py/deepair)
[![Documentation Status](https://readthedocs.org/projects/deepair/badge/?version=latest)](https://deepair.readthedocs.io/en/latest/?badge=latest)

Deepair implementations of reinforcement learning algorithms. It focus on DRL algorithms and implementing the latest advancements in DRL. Highly customizable support for training processes. Suitable for the research and application of the latest technologies in reinforcement learning.



# Features

# Documentation
Documentation is available: [https://deepair.readthedocs.io/](https://deepair.readthedocs.io/)

# Installation

```
pip install deepair
```

or
```
pip install git+https://github.com/sonnhfit/deepair.git
```

# Example

```python
import gym
from deepair.dqn import Rainbow

env = gym.make('LunarLander-v2')

rain = Rainbow(env=env, memory_size=10000, batch_size=32, target_update=256)

rain.train(timesteps=200000)

# test
state = env.reset()
done = False
score = 0

while not done:
    action = rain.select_action(state, deterministic=True)
    next_state, reward, done = env.step(action)

    state = next_state
    score += reward

print("score: ", score)
```

![rainbow lunalander env](docs/source/_static/img/rainbow_lunalander.gif)

# Implemented Algorithms

# Tutorial

# How To Contribute

