A Hierarchical Approach for MARLÖ Challenge.

Recently reinforcement learning has been showing remarkable performance in playing games. However, the majority of conventional approaches merely solve games with a single task. It is not yet well studied whether reinforcement learning is effective in games like Minecraft, where players are required to finish multiple different tasks while cooperating with other collaborators. In such games, AIs are confronted with dual challenges - finishing multiple tasks and building a multi-agent system. We propose a hierarchical approach with reinforcement learning policies to address the challenges. Experiments show that our approach performs well when dealing with multiple tasks and multiple agents simultaneously. Our approach got the second runner-up in MARLO Challenge, demonstrating its potential in tackling the challenges.