Multi-agents distributed reinforcement learning framework built with PyTorch in the SMAC2 environment
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Updated
Jun 28, 2024 - Python
Multi-agents distributed reinforcement learning framework built with PyTorch in the SMAC2 environment
Intelligent Social Systems and Swarm Robotics Lab (IS3R)
Deep Reinforcement Learning in C#
🔋 Datasets with baselines for offline multi-agent reinforcement learning.
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
A collection of MARL benchmarks based on TorchRL
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.
XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
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.
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging (Paper: https://ala2021.vub.ac.be/papers/ALA2021_paper_35.pdf)
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
Pytorch implementations of the multi-agent reinforcement learning algorithms, including QMIX, VDN, COMA, MADDPG, MATD3, FACMAC and MASoftQ for path planning of swarm of mobile robots.
🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX
Algorithms to solve the DSSE environment, focusing on optimizing drone swarm search and navigation for critical applications.
A tool for aggregating and plotting MARL experiment data.
DI-engine docs (Chinese and English)
SustainDC is a set of Python environments for Data Center simulation and control using Heterogeneous Multi Agent Reinforcement Learning. Includes customizable environments for workload scheduling, cooling optimization, and battery management, with integration into Gymnasium.
Adaptive Learning of Centralized and Decentralized Rewards in Multi-agent Imitation Learning
The Laser Learning Environment (LLE) is a multi-agent reinforcement learning environment
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