Welcome to LearnRL’s community !

PyPI - License Codacy - Quality Codacy - Coverage

LearnRL is a library to use and learn reinforcement learning. It’s also a community off supportive enthousiasts loving to share and build RL-based AI projects ! We would love to help you make projects with LearnRL, so join us on Discord !

About LearnRL

LearnRL is a framework to use and learn reinforcement learning with a wandb integration for a good visualisation ! Our motto is clean, sharable and readable Agents ! As such, you can plug and play agents on any environment, but also look how agents are built to learn !

Also, LearnRL is cross platform compatible ! That’s why no agents are built-in learnrl itself, but you can check:

You can build and run your own Agent in a clear and sharable manner !

import learnrl as rl
import gym

class MyAgent(rl.Agent):

   def act(self, observation, greedy=False):
      """ How the Agent act given an observation """
      ...
      return action

   def learn(self):
      """ How the Agent learns from his experiences """
      ...
      return logs

   def remember(self, observation, action, reward, done, next_observation=None, info={}, **param):
      """ How the Agent will remember experiences """
      ...

env = gym.make('FrozenLake-v0', is_slippery=True) # This could be any gym Environment !
agent = MyAgent(env.observation_space, env.action_space)

pg = rl.Playground(env, agent)
pg.fit(2000, verbose=1)

Note that ‘learn’ and ‘remember’ are optional, so this framework can also be used for baselines !

Of course, you can logs any custom metrics that your Agent/Env gives you and even chose how to aggregate them through episodes or cycles: See the metric codes for more details.

metrics=[
     ('reward~env-rwd', {'steps': 'sum', 'episode': 'sum'}),
     ('handled_reward~reward', {'steps': 'sum', 'episode': 'sum'}),
     'value_loss~vloss',
     'actor_loss~aloss',
     'exploration~exp'
 ]

pg.fit(2000, verbose=1, metrics=metrics)
The Playground will allow you to have clean logs adapted to your will with the verbose parameter:
  • Verbose 1episodes cycles - If your environment makes a lot of quick episodes.
    _images/logs-verbose-1.png
  • Verbose 2episode - To log each individual episode.
    _images/logs-verbose-2.png
  • Verbose 3steps cycles - If your environment makes a lot of quick steps but has long episodes.
    _images/logs-verbose-3.png
  • Verbose 4step - To log each individual step.
    _images/logs-verbose-4.png
  • Verbose 5detailled step - To debug each individual step (with observations, actions, …).
    _images/logs-verbose-5.png

The Playground also allows you to add Callbacks with ease, for example the WandbCallback to have a nice dashboard ! TODO: Show wandb logging

Features

  • Use this API to create your own agents and environments (even multiplayer!) with great compatibility and visualisation.

Installation

Install LearnRL by running:

pip install learnrl

Get started

Create:
  • TODO: Numpy DQN tutorial

  • TODO: Tensorflow tutorials

  • TODO: Pytorch tutorials

Visualize:
  • TODO: Tensorboard visualisation tutorial

  • TODO: Wandb visualisation tutorial

  • TODO: Wandb sweep tutorial

Table Of Content

Contribute

Support

If you are having issues, please contact us on Discord.

License

The project is licensed under the GNU LGPLv3 license.
See LICENCE, COPYING and COPYING.LESSER for more details.