learnrl 1.0.2

A package to learn about Reinforcement Learning

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 tool to monitor and log reinforcement learning experiments. You build/find any compatible agent (only need an act method), you build/find a gym environment, and learnrl will make them interact together ! LearnRL also contains both tensorboard and weights&biases integrations for a beautiful and sharable experiment tracking ! Also, LearnRL is cross platform compatible ! That’s why no agents are built-in learnrl itself, but you can check: - LearnRL for Tensorflow - LearnRL for Pytorch

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)

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

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

You can logs any custom metrics that your Agent/Env gives you and even chose how to aggregate them through different timescales. 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'
 ]

 playground.fit(2000, verbose=1, metrics=metrics)

The Playground also allows you to add Callbacks with ease, for example the WandbCallback to have a nice experiment tracking dashboard !

Installation

Install LearnRL by running:

pip install learnrl

Get started

Create: - TODO: Numpy tutorials - TODO: Tensorflow tutorials - TODO: Pytorch tutorials

Visualize: - TODO: Tensorboard visualisation tutorial - TODO: Wandb visualisation tutorial - TODO: Wandb sweeps tutorial

Documentation

See the latest complete documentation for more details. See the development documentation to see what’s coming !

Contribute

• Issue Tracker.

• Projects.

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.