# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 3 Artificial Intelligence
### Regression Algorithms
#### 3.1.3 Reinforcement Learning (RL)
The reward function evaluates the current settings and allocates rewards or penalties based on the result obtained from the function. There exist two distinct variations of the value function, i.e., state-value and action-value, which calculates the long-term expected reward based on the agent's future state. The environment refers to a task or simulation in which the agent utilizes the trial-and-error mechanism to maximize the cumulative reward. An agent engages in interactions with its surroundings, acquires knowledge of its own and the environment's states, and continuously acquires and assimilates data to execute specific actions within an RL algorithm. Therefore, at each instantaneous moment, it discerns the precise condition of the surroundings and implements a course of action that prompts the environment to transform into a different phase. The agent operates under a reward-punishment framework that depends on the result of the action it selects. At this juncture, the action will be rewarded if it is beneficial. If the action is improper, a penalty will be assessed. Although this feedback is less enlightening than Supervised Learning, which provides appropriate actions, it is more informative than Unsupervised Learning. Using trial and error, this technique enables the agent to ascertain accurate actions, devoid of any explicit critique of its efficacy [27]. RL refers to a specific class of ML tasks that aim to systematically discover optimal solutions using recurrent experiences, as described in Table 5. In contrast to supervised and Unsupervised Learning, RL is characterized by its collaborative and iterative nature. The method explores various approaches, monitors the environment's response, and adjusts its actions to determine the optimal solution. Hence, RL has demonstrated significant benefits in the management of offloading for systems that have been installed with 5G/6G technologies [79], including spectrum allocation, implementation of proactive radio resource control, and the optimization of handling data distribution inside the established framework of network slicing [80]. The process of selecting actions in RL is intrinsically linked to a persistent challenge called the \"trade-off between exploration and exploitation\". By this trade-off, an agent is tasked with determining whether it is more advantageous to arbitrarily examine the environment in search of an action that will lead to further action or to maintain the existing data and optimize the rewards by selecting specified actions [81]. Deep deterministic policy gradient (DDPG), Trust Region Policy Optimization (TRPO), deep Q-learning, and deep double Q-learning are widely applied RL techniques in various applications. Figure 5: The Reinforcement Learning framework. 1. Q-learning (QL) is a model-free RL technique that instructs an agent on an action strategy based on environmental perceptions and status. The approach, despite being a model-free RL, excludes the transition probability. Beginning with the current state, the approach functions within an MDS paradigm to determine the optimal policy by utilizing an expectation-maximization algorithm on the average benefit estimated over all subsequent stages [82]. 2. The double Q-learning (DQL) approach is an adaptation of Q-learning that explicitly tackles the problem of action value overestimation in noisy circumstances, resulting in a deceleration in the process of acquisition [83]. 3. The Markov Decision Process (MDP) technique is a stochastic process that occurs in discrete time. It adheres to this Markov property; it can be argued that the probability of transitioning to a particular state in the upcoming time step is exclusively influenced by the present state. It endeavors to identify a practical course of steps for the decision-maker impacted by the ambient environment [26]. 4. A deep Q-learning network (Deep QL) is used to address the issues of several states and large amounts of knowledge, and deep Q-learning [84] uses DNN instead of MDS architecture.