Search Packages

stable-baselines3

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

sb3-contrib

Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code

di-engine

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

stable-baselines

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

py-draughts

Fastest Python draughts/checkers library — bitboards, 8 variants, alpha-beta engine, web UI. ~200x faster than pydraughts.

rofunc

Rofunc: The Full Process Python Package for Robot Learning from Demonstration and Robot Manipulation

autonomous-learning-library

A PyTorch library for building deep reinforcement learning agents.

simple-rl

A simple framework for experimenting with Reinforcement Learning in Python.

helx

Interoperating between (Deep) Reiforcement Learning libraries

mabalgs

:bust_in_silhouette: Multi-Armed Bandit Algorithms Library (MAB) :cop:

pyrddlgym-rl

Wrappers for reinforcement learning algorithms (i.e. stable baselines 3, RLlib) to work with pyRDDLGym.

rllite

rl lite repository

pyrandwalk

:walking:Python Library for Random Walks

onn

Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)

yarll

Combining deep learning and reinforcement learning.

rllib

A simple and neat implementation of reinforcement learning algorithms.

rex-gym

OpenAI Gym environments for an open-source quadruped robot (SpotMicro)

rlforge

RL Forge is an open source reinforcement learning library that aims to provide the users with useful functions for the development of Reinforcement Learning Agents. The library also includes multiple popular reinforcement learning agents and environments, in addition, it is designed to be compatible with the gymnasium library (previous OpenAI Gym).

pokerrl

Framework for Multi-Agent Deep Reinforcement Learning in Poker

corl

The Core Reinforcement Learning library is intended to enable scalable deep reinforcement learning experimentation in a manner extensible to new simulations and new ways for the learning agents to interact with them. The hope is that this makes RL research easier by removing lock-in to particular simulations.The work is released under the follow APRS approval. Initial release of CoRL - Part #1 -Approved on 2022-05-2024 12:08:51 - PA Approval # [AFRL-2022-2455]" Documentation https://act3-ace.github.io/CoRL/

lazaro

Reinforcement learning framework for implementing custom models on custom environments using state of the art RL algorithms

rl-learn

A package to accompany the book "Reinforcement Learning: A Python Introduction"

lycorisq

A neat reinforcement learning framework based on LycorisNet.

gym-adserver

An OpenAI gym environment for ad serving algorithms