Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines

Understand

In multi-agent reinforcement learning (MARL), it is challenging for a collection of agents to learn complex temporally extended tasks.

• The difficulties lie in computational complexity and how to learn the high-level ideas behind reward functions.
• We study the graph-based Markov Decision Process (MDP) where the dynamics of neighboring agents are coupled.
• We use a reward machine (RM) to encode each agent's task and expose reward function internal structures.