A grid-like environment (multi-agent system) used by an intelligent agent (or more than one agent) in order for it/them to carry the orbs to the pits in a limited number of movements.
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings.
A collection of MARL benchmarks based on TorchRL
Multi-agent collaboration (2 UR10s) in Omniverse Isaac Gym/Sim.
A tool for aggregating and plotting MARL experiment data.
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.
🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX
A Sample Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning
Predator-Prey-Grass gridworld environment using PettingZoo, with dynamic deletion and spawning of partially observant agents.
Extended, multi-agent and multi-objective (MaMoRL) environments based on DeepMind's AI Safety Gridworlds. This is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. It is made compatible with OpenAI's Gym/Gymnasium and Farama Foundation PettingZoo.
This repository contains the code for Diversity Control (DiCo), a novel method to constrain behavioral diversity in multi-agent reinforcement learning.
Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)
无人机动态覆盖控制；1. 实现了一个无人机点覆盖环境；2. 给出了无人机连通保持规则；3. 给出了基于MARL的控制算法
Moss is a Python library for Reinforcement Learning.
A reinforcement learning cross attention channel with centralized training and execution for NMMO NeurIPS 2023
train AI agents to master Free-style Gomoku(五子棋)
stress testing black-box AVs with MARL
Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles
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