New algorithms tackle decentralized multi-player reinforcement learning

Researchers have developed new algorithms for decentralized multi-player reinforcement learning in episodic Markov Decision Processes (MDPs) with information asymmetry. The proposed methods, mQ-learning, mQ-learning-intervals, mEXC, and mEXC-Bellman, address scenarios with unobserved actions and independent or common rewards. These algorithms achieve competitive regret bounds compared to centralized learning, particularly for a small number of players or limited action sets. AI

IMPACT Introduces novel algorithms that could advance multi-agent reinforcement learning capabilities.

RANK_REASON Academic paper detailing new algorithms for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Markov decision processes
• mEXC-Bellman
• mQ-learning
• mQ-learning-intervals
• Q-learning

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →