  Deep reinforcement learning (RL) has achieved outstanding results in recent
years, which has led a dramatic increase in the number of methods and
applications. Recent works are exploring learning beyond single-agent scenarios
and considering multi-agent scenarios. However, they are faced with lots of
challenges and are seeking for help from traditional game-theoretic algorithms,
which, in turn, show bright application promise combined with modern algorithms
and boosting computing power. In this survey, we first introduce basic concepts
and algorithms in single agent RL and multi-agent systems; then, we summarize
the related algorithms from three aspects. Solution concepts from game theory
give inspiration to algorithms which try to evaluate the agents or find better
solutions in multi-agent systems. Fictitious self-play becomes popular and has
a great impact on the algorithm of multi-agent reinforcement learning.
Counterfactual regret minimization is an important tool to solve games with
incomplete information, and has shown great strength when combined with deep
learning.
