Multi-Agent Reinforcement Learning and Real-Time Decision-Making in Robotic Soccer for Virtual Environments

TL;DR

This paper introduces a scalable multi-agent reinforcement learning framework for robotic soccer, combining hierarchical RL, mean-field theory, and real-time decision-making to improve cooperation, strategy, and performance in dynamic environments.

Contribution

It presents a novel unified MARL framework integrating hierarchical RL and mean-field theory, optimized for real-time multi-agent decision-making in robotic soccer.

Findings

Proximal Policy Optimization outperforms previous methods with 4.32 goals and 82.9% ball control.

Hierarchical RL increases average goals to 5.26 by decomposing strategies.

Mean-field actor-critic achieves 5.93 goals, 89.1% ball control, and 92.3% passing accuracy.

Abstract

The deployment of multi-agent systems in dynamic, adversarial environments like robotic soccer necessitates real-time decision-making, sophisticated cooperation, and scalable algorithms to avoid the curse of dimensionality. While Reinforcement Learning (RL) offers a promising framework, existing methods often struggle with the multi-granularity of tasks (long-term strategy vs. instant actions) and the complexity of large-scale agent interactions. This paper presents a unified Multi-Agent Reinforcement Learning (MARL) framework that addresses these challenges. First, we establish a baseline using Proximal Policy Optimization (PPO) within a client-server architecture for real-time action scheduling, with PPO demonstrating superior performance (4.32 avg. goals, 82.9% ball control). Second, we introduce a Hierarchical RL (HRL) structure based on the options framework to decompose the problem into a high-level trajectory planning layer (modeled as a Semi-Markov Decision Process) and a low-level action execution layer, improving global strategy (avg. goals increased to 5.26). Finally, to ensure scalability, we integrate mean-field theory into the HRL framework, simplifying many-agent interactions into a single agent vs. the population average. Our mean-field actor-critic method achieves a significant performance boost (5.93 avg. goals, 89.1% ball control, 92.3% passing accuracy) and enhanced training stability. Extensive simulations of 4v4 matches in the Webots environment validate our approach, demonstrating its potential for robust, scalable, and cooperative behavior in complex multi-agent domains.