Toward Policy Explanations for Multi-Agent Reinforcement Learning

Understand

Advances in multi-agent reinforcement learning (MARL) enable sequential decision making for a range of exciting multi-agent applications such as cooperative AI and autonomous driving.

• Explaining agent decisions is crucial for improving system transparency, increasing user satisfaction, and facilitating human-agent collaboration.
• However, existing works on explainable reinforcement learning mostly focus on the single-agent setting and are not suitable for addressing challenges posed by multi-agent environments.
• We present novel methods to generate two types of policy explanations for MARL: (i) policy summarization about the agent cooperation and task sequence, and (ii) language explanations to answer queries about agent behavior.