Reinforcement Learning With Task Decomposition for Cooperative Multiagent Systems.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Cooperative multiagent systems (MASs) face challenges in coordinating multiple tasks using standard reinforcement learning (RL).
• Holistic reward signals in MASs complicate policy optimization for individual tasks.
• Existing algorithms struggle to derive optimal policies for each subtask in multi-task MASs.

Purpose of the Study:

• To develop efficient, learning-based algorithms for MASs to achieve joint optimal policies in multi-task cooperative settings.
• To address the complexity of holistic reward signals in multi-task MASs.
• To enable agents to learn cooperative strategies for accomplishing multiple tasks.

Main Methods:

• Decomposition of the holistic reward signal into subtask-specific components for each agent.
• Learning multiple value functions utilizing these decomposed reward signals.
• Updating agent policies based on the sum of distributed value functions.
• Theoretical analysis of the proposed multiagent reinforcement learning (MARL) approach.

Main Results:

• The proposed algorithms enable agents to learn joint optimal policies for multiple cooperative tasks.
• The reward decomposition strategy effectively simplifies the learning process in complex MASs.
• Simulation results validate the algorithms' effectiveness in both discrete and continuous control scenarios.

Conclusions:

• The developed RL-based algorithms offer an efficient solution for multi-task cooperative MASs.
• Reward decomposition is a viable strategy for improving policy optimization in complex multiagent reinforcement learning.
• The approach demonstrates significant potential for advancing cooperative multiagent systems in diverse applications.