Graph Exploration for Effective Multiagent Q-Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Multiagent reinforcement learning (MARL) presents challenges in coordinating agents with coupled policies but independent rewards.
• Existing exploration techniques often rely on counting mechanisms or struggle with continuous state spaces.

Purpose of the Study:

• To propose an efficient exploration technique for MARL using graph-based communication.
• To enable decentralized collaboration among agents for improved explorative behavior.

Main Methods:

• A novel framework utilizing graph-based communication for decentralized agent collaboration.
• Agents estimate state-action space uncertainty to guide exploration.
• The method avoids explicit counting mechanisms and handles continuous states directly.

Main Results:

• The proposed algorithm facilitates efficient exploration in MARL settings.
• Decentralized communication requires minimal information exchange, with only a single parameter vector needed in continuous-state scenarios.
• Theoretical guarantees are provided for discrete states, and experimental validation is shown for continuous states.

Conclusions:

• The developed technique offers a robust and efficient solution for exploration in MARL.
• It overcomes limitations of existing methods by enabling decentralized, low-communication exploration in complex environments.