Advancements in Multi-Agent Reinforcement Learning

The field of multi-agent reinforcement learning (MARL) is rapidly advancing, with a focus on developing innovative methods to improve cooperation, scalability, and sample efficiency. Recent research has explored the use of distributed neural policy gradients, role discovery and diversity through dynamics models, and compositional learning approaches to enable effective coordination among agents. Additionally, there has been a growing interest in developing novel algorithms that can handle complex environments, such as those with partial observability or high-dimensional state and action spaces. These advancements have the potential to significantly impact various applications, including robotics, autonomous navigation, and decision-making under uncertainty.

Noteworthy papers in this area include:

  • R3DM, which introduces a novel role-based MARL framework that learns emergent roles by maximizing the mutual information between agents' roles, observed trajectories, and expected future behaviors.
  • Bregman Centroid Guided Cross-Entropy Method, which proposes a lightweight enhancement to ensemble Cross-Entropy Method that leverages Bregman centroids for principled information aggregation and diversity control.
  • Multi-agent Markov Entanglement, which uncovers the underlying mathematical structure that enables value decomposition in multi-agent Markov decision processes.
  • CORA, which evaluates coalitional advantages via marginal contributions from all possible coalitions and decomposes advantages using the core solution from cooperative game theory, ensuring coalitional rationality.

Sources

Distributed Neural Policy Gradient Algorithm for Global Convergence of Networked Multi-Agent Reinforcement Learning

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning

Bregman Centroid Guided Cross-Entropy Method

Multi-agent Markov Entanglement

Compositional Learning for Modular Multi-Agent Self-Organizing Networks

Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods

Q-ARDNS-Multi: A Multi-Agent Quantum Reinforcement Learning Framework with Meta-Cognitive Adaptation for Complex 3D Environments

Thinking Beyond Visibility: A Near-Optimal Policy Framework for Locally Interdependent Multi-Agent MDPs

CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients

A Risk-Aware Reinforcement Learning Reward for Financial Trading

Composing Agents to Minimize Worst-case Risk

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