  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. Experimental
results on three MARL domains demonstrate the scalability of our methods. A
user study shows that the generated explanations significantly improve user
performance and increase subjective ratings on metrics such as user
satisfaction.
