Deepair is a Deep Reinforcement Learning library

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/

Installation

pip install deepair

or

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

Example

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)

Implemented Algorithms

Tutorial

How To Contribute