LG AI Research· EXAONE

[AAMAS 2024] Multi-Agent Reinforcement Learning for Real-World Application (Part 2)

In Part 1, we explored examples and studies that apply multi-agent reinforcement learning (MARL) to real-world problems. While AI models have demonstrated potential in solving real-world problems, there are still many factors to consider when implementing them in real-world scenarios. Can we make agents learn more efficiently to optimize costs? What if it’s difficult to develop a simulator that can interact with agents? How can we utilize the vast amounts of data being accumulated at industrial sites? This post will take a look at three papers presented at AAMAS 2024 that address these questions.. Multi-Agent Reinforcement Learning for Real-World Application Part 1 Data Intelligence Lab, Deunsol Yoon ( Link ) Part 2 Data Intelligence Lab Sunghoon Hong (this post) 1. Agent-Oriented Centralized Critic for Asynchronous Multi-Agent Reinforcement Learning [1] In real-world problems, such as the NCC scheduling optimization problem introduced in Part 1, the duration or start time of an action may be different for each agent. …

Announced

26 Jun 2024 00:00 UTC

Detected

28 Aug 2026 22:20 UTC (+793d after)