MAAC

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MAAC is an open-source AI Agents & Automation tool that provides code for Actor-Attention-Critic in multi-agent reinforcement learning. It enables researchers to experiment with attention mechanisms in complex environments.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is MAAC?

MAAC (Multi-Actor-Attention-Critic) is an open-source implementation of the Actor-Attention-Critic model, specifically designed for multi-agent reinforcement learning. Released as code for an ICML 2019 paper, it offers a foundational framework for researchers and engineers to delve into attention mechanisms within multi-agent environments. The tool requires Python 3.6.1, OpenAI baselines, PyTorch, and OpenAI Gym, providing a robust setup for replicating and extending the original research. Users can run various experiments, including the "Cooperative Treasure Collection" and "Rover-Tower" environments, by configuring options via `main.py`. This makes MAAC an invaluable resource for academic and experimental work in advanced AI.

Best used for

Ideal for developers and data scientists who need to implement and experiment with advanced multi-agent reinforcement learning models. Especially valuable for academic research and developing new AI algorithms that leverage attention mechanisms in complex, cooperative, or competitive environments.

Common actions

implement AI models

research reinforcement learning

experiment with AI agents

develop multi-agent systems

github copilotface swapping"AI Agents"collaborationdeepfakeworkflowslow-code/no-codeopen-sourceautomated workflow

Capabilities

Key features

Actor-Attention-Critic implementation
Multi-agent reinforcement learning
OpenAI baselines integration
PyTorch support
Configurable training environments

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the core requirements to run MAAC?

To run MAAC, you will need Python 3.6.1 (minimum), OpenAI baselines (specific commit hash), PyTorch (version 0.3.0.post4), OpenAI Gym (version 0.9.4), and Tensorboard with Tensorboard-Pytorch for logging. While these are the versions used in development, strict adherence may not always be necessary.

Can I use MAAC to replicate the experiments from the ICML 2019 paper?

Yes, MAAC provides the code to replicate the experiments from the "Actor-Attention-Critic for Multi-Agent Reinforcement Learning" ICML 2019 paper. The repository includes environments like "Cooperative Treasure Collection" and "Rover-Tower" that were featured in the research.

How do I configure and run training with MAAC?

All training code is contained within the `main.py` file. You can view available options by running `python main.py --help`. Specific environments like "Cooperative Treasure Collection" (fullobs_collect_treasure) and "Rover-Tower" (multi_speaker_listener) can be selected and configured for episode length.

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