multi_agent_path_planning
Mava
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| multi_agent_path_planning | Mava | |
|---|---|---|
| 1 | 5 | |
| 685 | 513 | |
| - | 3.5% | |
| 0.0 | 9.8 | |
| 2 months ago | 30 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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multi_agent_path_planning
We haven't tracked posts mentioning multi_agent_path_planning yet.
Tracking mentions began in Dec 2020.
Mava
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Recomendations of framework/library for MARL
I'm new to MARL and I'm looking for some open source implementations that I could use in a project. I have some previous experience in single agent RL, mainly with SB3 and gym, but just now started reading some MARL papers. I'm mainly looking for a good balance between performance, good documentation and ease of use. So far, I've taken look at Mava and RLlib. Mava seems like a very complete option, though I'm not at all familiar with the API and it maybe something simpler could also do the trick. As for the environment library, I was considering PettingZoo, since it has a very similar api to gym. Thought I might as well ask here first, as people can suggest other options for me to investigate or even give me some pros and cons they have learned from past experience.
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).
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Training with multiple agents.
The OpenSpiel games (as well as several others) are also available from MAVA (https://github.com/instadeepai/Mava).
What are some alternatives?
acme - A library of reinforcement learning components and agents
tf2multiagentrl - Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x
lingvo - Lingvo
PathPlanning - Common used path planning algorithms with animations.
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).
PettingZoo - A standard API for multi-agent reinforcement learning environments, with popular reference environments and related utilities
agents-aea - A framework for autonomous economic agent (AEA) development
maro - Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.