pytorch-rllib 0.1.0

Reinforcement Learning Library.

rllib

Reinforcement Learning Library.

Installation

pip install pytorch-rllib

Usage

Implemented agents:

• CrossEntropy
• Value / Policy Iteration
• Q-Learning
• SARSA
• DQN
• Rainbow
• REINFORCE
• A2C

import gym
import numpy as np

from rllib.qlearning import QLearningAgent
from rllib.trainer import Trainer
from rllib.utils import set_global_seed

# make environment
env = gym.make("Taxi-v3")
set_global_seed(seed=42, env=env)

n_actions = env.action_space.n

# make agent
agent = QLearningAgent(
    alpha=0.5,
    epsilon=0.25,
    discount=0.99,
    n_actions=n_actions,
)

# train
trainer = Trainer(env=env)
rewards = trainer.train(
    agent=agent,
    n_sessions=1000,
)

print(f"Mean reward: {np.mean(rewards[-10:])}")  # Mean reward: 8.0

More examples you can find here.

Requirements

Python >= 3.7

Citation

If you use rllib in a scientific publication, we would appreciate references to the following BibTex entry:

@misc{dayyass2022rllib,
    author       = {El-Ayyass, Dani},
    title        = {Reinforcement Learning Library},
    howpublished = {\url{https://github.com/dayyass/rllib}},
    year         = {2022}
}