Multi-Agent RLlib (MARLlib) is a MARL benchmark based on Ray and one of its toolkits RLlib. It provides MARL research community a unified platform for developing and evaluating the new ideas in various multi-agent environments. There are four core features of MARLlib.

• it collects most of the existing MARL algorithms that are widely acknowledged by the community and unifies them under one framework.
• it gives a solution that enables different multi-agent environments using the same interface to interact with the agents.
• it guarantees great efficiency in both the training and sampling process.
• it provides trained results including learning curves and pretrained models specific to each task and each algorithm's combination, with finetuned hyper-parameters to guarantee credibility.

Project Website: https://sites.google.com/view/marllib/home

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The README is organized as follows:

• Overview
• Environment
• Algorithm
• Getting started & Examples
• Bug Shooting

Part I. Overview

We collected most of the existing multi-agent environment and multi-agent reinforcement learning algorithms and unify them under one framework based on Ray's RLlib to boost the MARL research.

The common MARL baselines include independence learning (IQL, A2C, DDPG, TRPO, PPO), centralized critic learning (COMA, MADDPG, MAPPO, HATRPO), and value decomposition (QMIX, VDN, FACMAC, VDA2C) are all implemented.

The popular MARL environments like SMAC, MaMujoco, Google Research Football are all provided with a unified interface.

The algorithm code and environment code are fully separated. Changing the environment needs no modification on the algorithm side and vice versa.

The tutorial of RLlib can be found at https://docs.ray.io/en/releases-1.8.0/rllib/index.html. Fast examples can be found at https://docs.ray.io/en/releases-1.8.0/rllib-examples.html. These will help you easily dive into RLlib.

We hope everyone interested in MARL can be benefited from MARLlib.

Part II. Environment

Supported Multi-agent Environments / Tasks

Most of the popular environment in MARL research has been incorporated in this benchmark:

Env Name	Learning Mode	Observability	Action Space	Observations
LBF	Mixed	Both	Discrete	Discrete
RWARE	Collaborative	Partial	Discrete	Discrete
MPE	Mixed	Both	Both	Continuous
SMAC	Cooperative	Partial	Discrete	Continuous
MetaDrive	Collaborative	Partial	Continuous	Continuous
MAgent	Mixed	Partial	Discrete	Discrete
Pommerman	Mixed	Both	Discrete	Discrete
MaMujoco	Cooperative	Partial	Continuous	Continuous
GRF	Collaborative	Full	Discrete	Continuous
Hanabi	Cooperative	Partial	Discrete	Discrete

Each environment has a readme file, standing as the instruction for this task, talking about env settings, installation, and some important notes.

Part III. Algorithm

We provide three types of MARL algorithms as our baselines including:

Independent Learning: IQL DDPG PG A2C TRPO PPO

Centralized Critic: COMA MADDPG MAAC MAPPO MATRPO HATRPO HAPPO

Value Decomposition: VDN QMIX FACMAC VDAC VDPPO

Here is a chart describing the characteristics of each algorithm:

Algorithm	Support Task Mode	Need Global State	Action	Learning Mode	Type
IQL	Mixed	No	Discrete	Independent Learning	Off Policy
PG	Mixed	No	Both	Independent Learning	On Policy
A2C	Mixed	No	Both	Independent Learning	On Policy
DDPG	Mixed	No	Continuous	Independent Learning	Off Policy
TRPO	Mixed	No	Both	Independent Learning	On Policy
PPO	Mixed	No	Both	Independent Learning	On Policy
COMA	Mixed	Yes	Both	Centralized Critic	On Policy
MADDPG	Mixed	Yes	Continuous	Centralized Critic	Off Policy
MAA2C	Mixed	Yes	Both	Centralized Critic	On Policy
MATRPO	Mixed	Yes	Both	Centralized Critic	On Policy
MAPPO	Mixed	Yes	Both	Centralized Critic	On Policy
HATRPO	Cooperative	Yes	Both	Centralized Critic	On Policy
HAPPO	Cooperative	Yes	Both	Centralized Critic	On Policy
VDN	Cooperative	No	Discrete	Value Decomposition	Off Policy
QMIX	Cooperative	Yes	Discrete	Value Decomposition	Off Policy
FACMAC	Cooperative	Yes	Discrete	Value Decomposition	Off Policy
VDAC	Cooperative	Yes	Both	Value Decomposition	On Policy
VDPPO	Cooperative	Yes	Both	Value Decomposition	On Policy

Current Task & Available algorithm mapping: Y for available, N for not suitable, P for partially available on some scenarios. (Note: in our code, independent algorithms may not have I as prefix. For instance, PPO = IPPO)

Env w Algorithm	IQL	PG	A2C	DDPG	TRPO	PPO	COMA	MADDPG	MAAC	MATRPO	MAPPO	HATRPO	HAPPO	VDN	QMIX	FACMAC	VDAC	VDPPO
LBF	Y	Y	Y	N	Y	Y	Y	N	Y	Y	Y	Y	Y	P	P	P	P	P
RWARE	Y	Y	Y	N	Y	Y	Y	N	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
MPE	P	Y	Y	P	Y	Y	P	P	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
SMAC	Y	Y	Y	N	Y	Y	Y	N	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
MetaDrive	N	Y	Y	Y	Y	Y	N	Y	Y	Y	Y	Y	Y	N	N	N	N	N
MAgent	Y	Y	Y	N	Y	Y	Y	N	Y	Y	Y	Y	Y	N	N	N	N	N
Pommerman	Y	Y	Y	N	Y	Y	P	N	Y	Y	Y	Y	Y	P	P	P	P	P
MaMujoco	N	Y	Y	Y	Y	Y	N	Y	Y	Y	Y	Y	Y	N	N	Y	Y	Y
GRF	Y	Y	Y	N	Y	Y	Y	N	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
Hanabi	Y	Y	Y	N	Y	Y	Y	N	Y	Y	Y	Y	Y	N	N	N	N	N

Part IV. Getting started

Install Ray

pip install ray==1.8.0 # version sensitive

Add patch of MARLlib

cd patch
python add_patch.py

Y to replace source-packages code

Attention: Above is the common installation. Each environment needs extra dependency. Please read the installation instruction in envs/base_env/install.

Examples

python marl/main.py --algo_config=MAPPO [--finetuned] --env-config=smac with env_args.map_name=3m

--finetuned is optional, force using the finetuned hyperparameter

We provide an introduction to the code directory to help you get familiar with the codebase:

• top level directory structure

This picture is in image/code-MARLlib.png

• MARL directory structure

This picture is in image/code-MARL.png.png

• ENVS directory structure

This picture is in image/code-ENVS.png.png

Part V. Bug Shooting

• observation/action out of space bug:
  • make sure the observation/action space defined in env init function
    • has same data type with env returned data (e.g., float32/64)
    • env returned data range is in the space scope (e.g., box(-2,2))
  • the returned env observation contained the required key (e.g., action_mask/state)

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License

The MIT License