Mava
IC3Net
Our great sponsors
| Mava | IC3Net | |
|---|---|---|
| 5 | 2 | |
| 645 | 202 | |
| 5.7% | 0.5% | |
| 9.9 | 0.0 | |
| 4 days ago | 7 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Mava
-
Starting wth Multi Agent Reinforcement Learning
If you want to play with models and algorithms around MARL, take a look at Mava.
-
Recomendations of framework/library for MARL
These are the main reasons we built Mava , a library built specifically for MARL. We are also in the process of rewriting it to be simpler to compose MARL components (communication, central training etc), and we are rewriting our codebase in JAX, so really looking forward to improved performance! (Disclaimer, I am one of the people working on Mava).
-
Training with multiple agents.
The OpenSpiel games (as well as several others) are also available from MAVA (https://github.com/instadeepai/Mava).
-
[R] Mava: a research framework for distributed multi-agent reinforcement learning
https://arxiv.org/abs/2107.01460 Mava, a scalable framework for research in multi-agent reinforcement learning, contains implementation for several multi-agent systems like multi-agent DQN (MADQN), MADDPG, MAPPO, MAD4PG, DIAL, QMIX, and VDN, and integrates well with multi-agent RL environments like PettingZoo, Flatland, OpenSpiel, RoboCup, and SMAC. On GitHub: https://github.com/instadeepai/Mava
IC3Net
What are some alternatives?
acme - A library of reinforcement learning components and agents
smac - SMAC: The StarCraft Multi-Agent Challenge
lingvo - Lingvo
Emergent-Multiagent-Strategies - Emergence of complex strategies through multiagent competition
tf2multiagentrl - Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x
malib - A parallel framework for population-based multi-agent reinforcement learning.
pymarl2 - Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
ai-economist - Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcement learning to learn optimal economic policies, as done by the AI Economist (https://www.einstein.ai/the-ai-economist).
wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
multi_agent_path_planning - Python implementation of a bunch of multi-robot path-planning algorithms.
PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities