An integral part of modern artificial intelligence (AI) and machine learning (ML), Reinforcement Learning (RL) represents a set of algorithms that guide an agent to learn from its interactions with an environment towards maximal reward accumulation. Among various techniques employed in reinforcement learning, the Contextual Bandits problems have garnered substantial research attention due to their unique balance between exploration and exploitation.

A Contextual Bandit, often regarded as a simplified version of fully-fledged reinforcement learning, differs from traditional multi-armed bandit problems by incorporating context information or state information into the learning process. This approach proves critical when decision-making requires situational awareness or when action selection should change dynamically based on environment conditions.

• Immediate Reward: Unlike traditional reinforcement learning approaches where the rewards are delayed, contextual bandits provide immediate rewards for each action taken. This facilitates faster learning and immediate policy adaptability.

• Single-Step Problems: Typically, contextual bandits focus on problems where the decision does not affect future state transitions. This trait distinctly separates it from full-fledged reinforcement learning models.

• Personalized Outputs: Contextual bandits can deliver personalized outputs depending on the input context, making them particularly useful in recommendation systems and ad placements.

The implementation stage requires careful consideration of the problem context, appropriate bandit algorithms, and rigorous evaluation measures. Suitable models for contextual bandit problems include LinUCB, Thompson Sampling, and Factorization Bandit, among others. These models should be chosen compatible with the complexity and scale of the problem space, along with the computational resources at disposal. Evaluating the performance of a contextual bandit necessitates multi-faceted metrics such as average reward and regret minimization. Lastly, the performance comparison against a well-defined baseline or benchmark is vital for validating the model's practical utility.

Contextual bandits excel in domains requiring personalized outputs and instant reward feedback. With a well-defined problem space, appropriate algorithm, meticulous evaluation and fine-tuning, the incorporation of contextual bandits can produce robust, adaptive AI systems. However, the limitations must not be overlooked, and practitioners should carefully assess the trade-offs before embarking on using Contextual Bandits in Reinforcement Learning.

The Contextual Bandits approach offers several inherent advantages, such as:

• Scalability: Contextual bandits can handle larger state and action spaces compared to some full reinforcement learning models, as they generally require less computation and simpler representations.

• Real-world Applicability: The applicability of contextual bandits spans across several real-world situations, from ad recommendation and personalized content delivery to healthcare, where decisions alter dynamically based on specific contextual information.

• Reduced Complexity: By focusing on single-step problems, contextual bandits substantially reduce the complexity involved in credit assignment and long-term planning seen in full reinforcement learning models. This leads to simpler and computationally cheaper algorithms.

Despite its numerous advantages, certain limitations come with contextual bandits:

• Limited Scope: By design, contextual bandits are relevant only for single-step problems. This limits their usage in complex, multi-step decision-making scenarios which are a cornerstone of full reinforcement learning tasks.

• Exploration vs Exploitation Tradeoff: Balancing exploration (trying new actions) and exploitation (leveraging known information) is inherently challenging in contextual bandits. Overemphasis of one may lead to sub-optimal results.

• Overfitting Risk: There is a risk of overfitting due to a high degree of customization or personalization to specific contexts. This may cause the Bandit to perform poorly when introduced to an unseen or a slightly altered context.