Emergent-Multiagent-Strategies VS Mava

Compare Emergent-Multiagent-Strategies vs Mava and see what are their differences.

Mava

🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX (by instadeepai)
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Emergent-Multiagent-Strategies Mava
1 5
38 646
- 1.4%
0.0 9.9
over 1 year ago 8 days ago
Python Python
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

Emergent-Multiagent-Strategies

Posts with mentions or reviews of Emergent-Multiagent-Strategies. We have used some of these posts to build our list of alternatives and similar projects.

Mava

Posts with mentions or reviews of Mava. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-14.

What are some alternatives?

When comparing Emergent-Multiagent-Strategies and Mava you can also consider the following projects:

IC3Net - Code for ICLR 2019 paper: Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

acme - A library of reinforcement learning components and agents

Competitive-Programming

tf2multiagentrl - Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x

DI-engine - OpenDILab Decision AI Engine

lingvo - Lingvo

pumpkin - The MAS Demonic Surveillance Platform. 🎃 [Moved to: https://github.com/scandale-project/pumpkin]

pymarl2 - Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)

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).

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