MADDPG (Multi-Agent Deep Deterministic Policy Gradient) Algorithm

Introduction

MADDPG is a reinforcement learning algorithm designed for cooperative multi-agent environments.

Key Features

• Decentralized execution with centralized training
• Utilizes actor-critic architecture
• Supports continuous action spaces
• Uses off-policy learning with replay buffer

Usage

To use MADDPG:

1. Initialize the agent neural networks
2. Interact with the environment and collect experiences
3. Update the actor and critic networks using the collected experiences
4. Repeat steps 2-3 until convergence

References

• MADDPG Paper
• Official MADDPG GitHub Repository