Efficient Fully Decentralized MARL Policy Evaluation Approach

Core Concepts

Local TD-update approach lowers sample and communication complexities in MARL-PE.

Abstract

The content discusses a new approach for fully decentralized multi-agent reinforcement learning (MARL) policy evaluation. It introduces the concept of local TD-update steps to reduce communication frequency and improve convergence. Theoretical and experimental results confirm the effectiveness of this approach in lowering complexities compared to consensus-based algorithms.

Abstract:

• Actor-critic framework for fully decentralized MARL.
• Challenges in lowering sample and communication complexities.
• Introducing multiple local TD-update steps between communication rounds.
• Addressing the "agent-drift" phenomenon.

Introduction:

• Background on MARL and its applications.
• Importance of MARL policy evaluation (PE) problem.
• Critical challenge of lowering sample and communication complexities.
• Proposed solution with local TD-update approach.

Technical Challenges:

• Structural differences between TD learning in MARL and DSGD method.
• Markovian noise in TD learning.
• Agent-drift phenomenon due to heterogeneous rewards across agents.

Main Results and Contribution:

• Overcoming challenges in analyzing upper bounds of complexities for local TD-update approach.
• Effectiveness of multiple local TD-update steps in reducing communication complexities.
• Theoretical convergence analysis supporting the proposed approach.

Stats

"In actor-critic framework for fully decentralized multi-agent reinforcement learning (MARL), one of the key components is the MARL policy evaluation (PE) problem, where a set of 𝑁agents work cooperatively to evaluate the value function of the global states for a given policy through communicating with their neighbors."
"To lower communication complexity for solving MARL-PE problems, a “natural” idea is to use an “infrequent communication” approach where we perform multiple local TD-update steps between each consecutive rounds of communication to reduce the communication frequency."

Quotes

"The main contribution of this paper is that we overcome the above challenges in analyzing the upper bounds of the sample and communication complexities for the local TD-update approach."
"Both theoretically and empirically, we show that allowing multiple local TD-update steps is indeed a valid approach that can significantly lower communication complexities of MARL-PE compared to vanilla consensus-based decentralized TD learning algorithms."

Key Insights Distilled From

Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach

by Fnu Hairi,Zi... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15935.pdf

Deeper Inquiries

How does the proposed local TD-update approach compare with other state-of-the-art methods

The proposed local TD-update approach in the context of MARL policy evaluation (PE) offers several advantages compared to other state-of-the-art methods. Firstly, it significantly reduces communication complexity by allowing multiple local TD-update steps between consecutive rounds of communication. This infrequent communication strategy helps lower the number of required communication rounds, leading to more efficient convergence towards an 𝜖-stationary point. Additionally, the local TD-update approach has been shown to have a sample complexity that matches the state-of-the-art single-agent RL setting, demonstrating its effectiveness in fully decentralized multi-agent systems.

What implications does the "agent-drift" phenomenon have on real-world applications

The "agent-drift" phenomenon observed in MARL can have significant implications for real-world applications, especially in scenarios where agents operate autonomously and make decisions based on their individual rewards. In such cases, heterogeneous rewards across agents may lead to divergent value functions as each agent updates its parameters independently. This could result in suboptimal or inconsistent decision-making within the system, impacting overall performance and coordination among agents. Addressing agent-drift is crucial for ensuring effective collaboration and achieving desired outcomes in decentralized multi-agent systems.

How can these findings be applied to optimize performance in other multi-agent systems beyond MARL

The findings from the research on local TD-update approaches for MARL-PE can be applied to optimize performance in various other multi-agent systems beyond MARL. By implementing infrequent communication strategies and incorporating multiple local update steps between interactions with neighboring agents, similar improvements in sample and communication complexities can be achieved. These optimized techniques can enhance efficiency and convergence rates in distributed optimization problems, networked large-scale systems like power networks or autonomous driving applications where cooperative decision-making among multiple entities is essential for success.