TIMAR: Transition-informed representation for sample-efficient multi-agent reinforcement learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Multi-Agent Reinforcement Learning (MARL) faces challenges with high training costs due to massive interaction requirements.
• Partial observability in MARL hinders agents' ability to model interactions and coordination from an egocentric perspective, impeding data efficiency.

Purpose of the Study:

• To develop a world-model-driven paradigm that enhances data efficiency in MARL by enabling holistic environmental representations.
• To introduce the Transition-Informed Multi-Agent Representations (TIMAR) framework for improved agent learning and coordination.

Main Methods:

• Leveraging a joint transition model (surrogate world model) to capture multi-agent system dynamics.
• Employing a self-supervised learning objective that encourages consistency between predicted and actual future observations.
• Incorporating an auxiliary module for predicting future transitions to infer latent states and agent influences.

Main Results:

• TIMAR significantly improves performance and data efficiency in various MARL environments compared to strong baselines (MAPPO, HAPPO, QMIX, MAT, MA2CL).
• The framework enables learning semantic representations from high-dimensional observations, boosting the data efficiency of downstream MARL algorithms.
• TIMAR enhances the generalization capabilities of Transformer-based MARL algorithms like MAT.

Conclusions:

• The TIMAR framework offers a novel approach to address data efficiency limitations in MARL.
• By learning effective representations through a world model, TIMAR facilitates better agent interaction and coordination.
• This research paves the way for more efficient and capable MARL systems.