Search Packages

torchrl

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

pettingzoo

An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities

torchrl-nightly

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

laser-learning-environment

The Laser Learning Environment (LLE) is a cooperative MARL grid-world

esquilax

JAX Multi-Agent RL, Neuro-Evolution, and A-Life Library

dsse

The Drone Swarm Search project provides an environment for SAR missions built on PettingZoo, where agents, represented by drones, are tasked with locating targets identified as shipwrecked individuals.

vmas

VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.

rware

Multi-Robot Warehouse (RWARE): A multi-agent reinforcement learning environment

pymaro

Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.

flatland-rl

The Flatland Framework is a multi-purpose environment to tackle problems around resilient resource allocation under uncertainty. It is designed to be a flexible and method agnostic to solve a wide range of problems in the field of operations research and reinforcement learning.

accelbrainbeat

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

pydbm

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

matrax

A collection of matrix games in JAX

pysummarization

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

xuance

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

chatarena

ChatArena (or Chat Arena) is a Multi-Agent Language Game Environments for LLMs. The goal is to develop communication and collaboration capabilities of AIs.

pyqlearning

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

openrl

unified reinforcement learning framework

benchmarl

BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.

ai-economist

Foundation: An Economics Simulation Framework

mobile-env

An open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks.

deepcomp

DeepCoMP: Self-Learning Dynamic Multi-Cell Selection for Coordinated Multipoint (CoMP)

routerlurb

RouteRL is a multi-agent reinforcement learning framework for urban route choice in different city networks. This subpackage is developed to support its compatibility with URB until the full integration.

accel-brain-base

accel-brain-base is a basic library of the Deep Learning for rapid development at low cost. This library makes it possible to design and implement deep learning, which must be configured as a complex system, by combining a plurality of functionally differentiated modules such as a Deep Boltzmann Machines(DBMs), an Auto-Encoder, an Encoder/Decoder based on Long Short-Term Memory(LSTM), a Convolutional Auto-Encoder(CAE), and Transformer.