PyTorch implementations of Deep reinforcement learning algorithms.
Project description
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
Project details
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deepair-0.2.0.tar.gz
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