MADDPG

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MADDPG is an open-source AI Agents & Automation tool that provides a PyTorch implementation of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. It is designed for multi-agent reinforcement learning in mixed cooperative-competitive environments.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is MADDPG?

MADDPG offers a PyTorch implementation of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, specifically tailored for multi-agent reinforcement learning in environments with both cooperative and competitive elements. This tool corresponds to the research paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments." It includes a quick start guide for running simulations, such as testing the algorithm on a 'simple_tag' scenario with pretrained models. The implementation allows for training agents, like predators catching prey, within the Multi-Agent Particle Environment (MPE). Users can also modify environment settings, such as switching between sparse and dense reward structures, to suit their experimental needs.

Best used for

Ideal for developers and data scientists who need to implement and experiment with multi-agent reinforcement learning algorithms, train agents in simulated environments, and research complex multi-agent systems. Especially valuable for those working with mixed cooperative-competitive scenarios and requiring a PyTorch-based solution.

Common actions

implement MARL algorithms

train multi-agents

simulate environments

research reinforcement learning

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Capabilities

Key features

PyTorch implementation
Multi-Agent Actor-Critic
Mixed cooperative-competitive environments
Multi-Agent Particle Environment
Customizable reward structures

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for running MADDPG?

To run the MADDPG implementation, you will need Python 3.6.5, the Multi-Agent Particle Environment (MPE), and PyTorch 1.1.0. These dependencies ensure compatibility and proper execution of the algorithm.

Can I use MADDPG to train agents for custom scenarios?

Yes, while the repository provides a 'simple_tag' scenario example, the MADDPG implementation is designed to be adaptable. You can integrate it with your own custom multi-agent environments for training and evaluation.

How can I change the reward structure in the Multi-Agent Particle Environment (MPE)?

You can modify the reward structure by editing the 'multiagent-particle-envs/multiagent/scenarios/simple_tag.py' file. Specifically, change 'shape=False' to 'shape=True' to switch from sparse to dense rewards, allowing for different training dynamics.

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