multi-agent-learning

FinRL®: Financial Reinforcement Learning. 🔥

Paper list of multi-agent reinforcement learning (MARL)

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.

Multi-Agent Reinforcement Learning (MARL) papers

A Collection of Multi-Agent Reinforcement Learning (MARL) Resources

A selection of state-of-the-art research materials on decision making and motion planning.

An SDK for multi-agent collaborative perception.

[NeurIPS2021] Learning Distilled Collaboration Graph for Multi-Agent Perception

Active visual tracking library based on PyTorch.

A curated list of multiagent learning and related area resources.

A curated list of awesome multi-agent learning papers

We reproduced DeepMind's results and implement a meta-learning (MLSH) agent which can generalize across minigames.

Computing mixed-strategy Nash Equilibria for games involving multiple players

Multi agent reinforement learning. Chaser and runner.

Multi-agent collaboration using Reinforcement Learning

Multiplayer Game, Security, DF Service Implementation, Genetic Algorithm Implementation, Multi-Agent Systems Enhanced With Q-Learning Implementation For Improved Decision-Making.

This repository contains an implementation of a deep learning architecture designed for unsupervised or self-supervised classification tasks. The architecture consists of two components: a classifier and an aligner.