A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
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Updated
Oct 14, 2024 - Python
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX
Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)
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 collection of MARL benchmarks based on TorchRL
This is a framework for the research on multi-agent reinforcement learning and the implementation of the experiments in the paper titled by ''Shapley Q-value: A Local Reward Approach to Solve Global Reward Games''.
[NeurIPS 2021] CDS achieves remarkable success in challenging benchmarks SMAC and GRF by balancing sharing and diversity.
Implementation of Multi-Agent Reinforcement Learning algorithm(s). Currently includes: MADDPG
A solution for Dynamic Spectrum Management in Mission-Critical UAV Networks using Team Q learning as a Multi-Agent Reinforcement Learning Approach
An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations using Multi Independent Agent Reinforcement Learning
A tool for aggregating and plotting MARL experiment data.
Emergence of complex strategies through multiagent competition
This repo is the implementation of paper ''SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning''.
applying multi-agent reinforcement learning for highway-merging autonomous vehicles
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
A simple example of how to implement vector based DDPG for MARL tasks using PyTorch and a ML-Agents environment.
A toolbox with the goal of speeding up research on bargaining in MARL (cooperation problems in MARL).
Multi-agent reinforcement learning framework
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