SpyRL

SpyRL is a reinforcement learning (RL) framework that provides commonly used library, so you will write less code. For example, it automatically creates a learning graph for every learning session.

Installation

Use the package manager pip to install SpyRL.

pip install spyrl

Usage

import gym
import sys
sys.path.insert(0, "../spyrl")
from spyrl.activity.learning import Learning
from spyrl.activity.activity_config import ActivityConfig
from spyrl.listener.impl.basic_functions import BasicFunctions
from spyrl.agent_builder.impl.actor_critic_traces_agent_builder import ActorCriticTracesAgentBuilder
from example.cartpole.helper.cartpole_discretiser import CartpoleDiscretiser
from example.cartpole.helper.env_wrapper import GymEnvWrapper

if __name__ == '__main__':
    env = gym.make('CartPole-v0')
    num_actions = env.action_space.n
    config = ActivityConfig(num_episodes=1000, out_path='result/cartpole/test1/')
    agent_builder = ActorCriticTracesAgentBuilder(num_actions, discretiser=CartpoleDiscretiser())
    learning = Learning(listener=BasicFunctions(render=False))
    learning.learn(env, agent_builder, config)

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

Citation

To cite this repository in publications:

@misc{spyrl,
  author = {Kurniawan, Budi and Vamplew, Peter and Papasimeon, Michael and Dazeley, Richard and Foale, Cameron},
  title = {SpyRL Framework},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/budi-kurniawan/spyrl}},
}

License

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