# Machine Learning: Models, Challenges, and Research Directions
## 3 Machine Learning Models
### Reinforcement Learning  
Reinforcement learning (RL) models are another ML category that trains an agent to learn in an interactive environment by getting feedback from its own experiences and actions. Reinforcement learning trains the models to learn the optimal actions in an environment to get maximum rewards. These optimal actions can be performed through interactions with an environment and their responses. In the absence of a supervisor, the learner has to explore the sequence of their actions independently, which maximizes the rewards [82]. Advantages of reinforcement learning include their ability to handle dynamic and complex environments, making them suitable for tasks like robotics control and game playing. They also excel in scenarios with sparse or delayed feedback. However, they often require extensive training and may struggle with high-dimensional state spaces. Additionally, defining a suitable reward function can be challenging, and poorly designed rewards may lead to suboptimal behavior. Furthermore, exploration strategies need to be carefully balanced to avoid excessive trial-and-error. In general, an agent attempts to sequentially learn the decision-making challenges through modeling the Markov Decision Process (MDP). At a timestep \(t\), the agent receives an observation of an environment \(P_{t}\in P\), where \(P\) indicates the state space, and the agent can choose a behavior \(B_{t}\in R(P_{t})\) based on the environment reaction. In this context, \(B(P_{t})\) is the set of all possible actions of state \(P_{t}\). Then, the agent has a reward \(R_{t+1}\). Then, the state \(P_{t+1}\) and reward \(R_{t+1}\) depend on the previous state of \(P_{t}\) and the behavior of \(R_{t}\). During the learning process, the agent also learns to maximize the cumulative rewards, shown as the expected reward return \(E_{t}\), using a discount rate \(Y\) using the following equation [83]:  
\[D_{t}=\sum_{k=0}^{\infty}Y^{k}R_{t+k+1}\ \ \ 0\leq Y\leq 1 \tag{2}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Category** & **Characteristics** & & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{Cluster-based} & Divides uncategorized data & \multirow{2}{*}{\(\bullet\)} & \multirow{2}{*}{Easy implementation.} & \multirow{2}{*}{\(\bullet\)} & \multirow{2}{*}{High complexity.} \\  & into similar groups; & & & \\ \hline Dimensionality & Decreases the number of & \multirow{2}{*}{\(\bullet\)} & Decrease time and & \multirow{2}{*}{\(\bullet\)} & \multirow{2}{*}{Data loss.} \\ reduction-based & features in the given dataset; & & storage. & \\ \hline \multirow{6}{*}{Neural network-based} & \multirow{6}{*}{Inspiration of human brains.} & \multirow{6}{*}{\(\bullet\)} & Can detect the & \multirow{2}{*}{\(\bullet\)} & \multirow{2}{*}{Require huge datasets;} \\  & & interactions among & & \\ \cline{1-1}  & & predictor variables; & & \\ \cline{1-1}  & & Availability of multiple & & power; \\ \cline{1-1}  & & training processes; & & \\ \cline{1-1}  & & High adaptability. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Characteristics, limitations, and strengths of unsupervised models [74; 75]. The agent also follows a rule that maps from the states to the probabilities of every action. The value of such a rule depends on the expected discounted return, calculated as follows:  
\[V_{\pi}\left(p\right)=E_{\pi}\left[D_{t}\mid P_{t}=p\right] \tag{3}\]  
Moreover, the action value function \(T_{\pi}\left(p,\ b\right)\) refers to the expected return from state \(p\) and behavior \(b\):  
\[T_{\pi}\left(p,b\right)= E_{\pi}\left[D_{t}\mid P_{t}=p,\ B_{t}=b\right] \tag{4}\]  
The optimal action value with rule \(\pi\) is defined as follows:  
\[q_{*}(p,b)= \underset{\pi}{\max}T_{\pi}\left(p,b\right) \tag{5}\]  
In reinforcement learning models, optimal equations can be used to improve the rule followed by an agent iteratively.