Offline Multi Agent Reinforcement Learning

Offline multi-agent reinforcement learning (MARL) focuses on training multiple agents to cooperate or compete using only pre-collected data, eliminating the need for costly or risky real-time interaction. Current research emphasizes addressing the challenges of distributional shift (where the learned policy differs from the data-generating policy) and the high-dimensional joint action space, often employing techniques like value decomposition, stationary distribution regularization, and diffusion models to improve performance and stability. This field is crucial for deploying MARL in real-world applications where online learning is impractical, impacting diverse areas such as robotics, game playing, and resource management. Standardization of datasets and evaluation methods is a growing focus to ensure reliable progress and comparison of algorithms.