… reinforcement learning. Next we summarize the most important aspects of evolutionary game
theory… Finally, we discuss the state-of-the-art of multi-agent reinforcement learning and the …

… 2.1 Reinforcement learning evolutionary game model We start by introducing our reinforcement
learning evolutionary game (RLEG), where each agent could be in one of \(n_{s}\) states …

… Secondly, we formulate a corresponding evolutionary game and utilize net utility as the
payoff. Related utility function and price function are discussed to quantify influential network …

… As a particularly suitable candidate, reinforcement learning (… The marriage between RL
and evolutionary game may be a … in the evolutionary games with the reinforcement learning (…

… cannot be suited for evolutionary game theory any more, this paper … reinforcement learning
into evolutionary games and constructs multiagent learning model for evolutionary games. …

… by combining the evolutionary game process with reinforcement learning, it can better guide
the direction of the evolutionary game and realize a more ideal evolutionary situation. In the …

… By contrast, we find that hypothetical reinforcement learners … learners are maintained at a
low frequency. We conclude that specific game structures can select for trial-and-error learning …

… network through combining deep reinforcement learning with evolutionary game theory. This
… evolutionary game theory into the setting of the reward function for reinforcement learning, …

… We model it as an evolutionary game where the vehicles choose … evolutionary game-based
vehicular cloud access algorithm (EG-VCA). Moreover, we propose a distributed Q-learning-…

… evolutionary game … reinforcement learning and evolutionary games can be combined. In
Figure 2, we describe the basic response and influence mechanisms, and to identify the game …