n the anti-jamming game performed better than the myopic learning and random defense strategy because it successfully accommodated the environment dynamics and strategic behavior of the cognitive attackers. To further improve system performance, the authors used the QV and the SARSA RL algorithms in [164] to replace the Minimax-Q learning in [193]. Minimax-Q learning is an off-policy and greedy algorithm, whereas the QV and SARSA are on-policy algorithms. It was shown that QV learning can achieve the best performance as the value of both Q and V is updated. In [206], the authors first investigated an anti-jamming game model where the SU could access only one channel at a time and hopped among different channels. An MDP-based channel hopping defense strategy with the assumption of perfect knowledge was derived by analyzing interactions between the SU and attackers. Based on this, they proposed two learning schemes by which SUs gained knowledge of adversaries in order to handle cases without perfect knowledge. The schemes were then extended to a scenario where SUs could access all available channels simultaneously and redefined the anti-jamming game with randomized power allocation as the defense strategy. The Nash equilibrium was derived for this Colonel Blotto game, which minimized the worst-case damage. In [69], a game model was formed to integrate anti-jamming and jamming subgames into a stochastic framework. Q-learning was applied to find an optimal channel access strategy. It was shown that Minimax-Q learning was more suitable than Nash-Q learning for an aggressive environment. For distributed mobile ad hoc networking scenarios, Friend-or-foe Q-learning provided the best solution where centralized control was nearly unavailable. By employing the Double Q-learning algorithm to defend against the jamming attacks, Multi-Objective Ant Colony Optimization (MOACO) and greedy-based optimization methods were proposed in [192]. A Q-learning-assisted cluster-based data utilization was proposed that could enhance inter-