Contextual Bandits

Contents of content show

What is Contextual Bandits?

Contextual bandits are a class of machine learning algorithms designed for sequential decision-making. They personalize actions by using “context”—such as user data or environmental features—to make better choices. The core purpose is to balance exploiting known-good options with exploring new ones to maximize cumulative rewards over time.

How Contextual Bandits Works

+-----------+       +-------------------+       +--------+       +---------------+       +--------+
|  Context  |----->|  Bandit Algorithm |----->| Action |----->|  Environment  |----->| Reward |
| (User x)  |       | (e.g., LinUCB)    |       |  (a)   |       | (e.g., Website) |       |  (r)   |
+-----------+       +-------------------+       +--------+       +---------------+       +--------+
      ^                     |                                                               |
      |                     |_______________________________________________________________|
      |                                           (Update model with (x, a, r))             |
      |_____________________________________________________________________________________|

Contextual bandits are a sophisticated form of reinforcement learning that optimizes decision-making by taking into account the specific situation or context. Unlike simpler multi-armed bandits that treat all decisions equally, contextual bandits use additional information to tailor choices, making them far more effective for personalization. The process operates in a continuous feedback loop, constantly learning and refining its strategy to maximize a desired outcome, such as click-through rates or conversions.

1. Contextual Input

At the start of each cycle, the system receives a “context.” This is a set of features or data points that describe the current environment. For example, in a news recommendation system, the context could include the user’s location, device type, time of day, and topics of previously read articles. This information provides the necessary clues for the algorithm to make a personalized decision.

2. Action Selection (Exploration vs. Exploitation)

Using the input context, the bandit algorithm selects an “action” from a set of available options. This is where the core challenge lies: balancing exploration and exploitation. Exploitation involves choosing the action that the model currently predicts will yield the highest reward based on past experience. Exploration involves trying out other actions, even those with lower predicted rewards, to gather more data and potentially discover new, better options for the future. Algorithms like LinUCB or Thompson Sampling use the context to estimate the potential reward of each action and manage this trade-off intelligently.

3. Reward and Model Update

After an action is taken (e.g., a specific news article is recommended), the environment provides a “reward” (e.g., the user clicks the article, resulting in a reward of 1, or ignores it, a reward of 0). This feedback—consisting of the context, the chosen action, and the resulting reward—is logged and used to update the underlying machine learning model. This update refines the model’s understanding of which actions work best in which contexts, improving the quality of future decisions.

Breakdown of the ASCII Diagram

Context (User x)

This block represents the starting point of the process. It is the set of observable features provided to the algorithm before a decision is made.

  • What it is: A feature vector describing the current state (e.g., user demographics, time of day, device).
  • Why it matters: It’s the key differentiator from non-contextual bandits, enabling personalized decisions.

Bandit Algorithm

This is the core decision-making engine. It takes the context and uses its internal model to choose an action.

  • What it is: An algorithm like LinUCB, Thompson Sampling, or Epsilon-Greedy.
  • How it interacts: It receives the context, calculates the expected reward for all possible actions, and selects one based on an exploration-exploitation strategy.

Action (a)

This block represents the output of the algorithm—the decision that was made.

  • What it is: One of several predefined options (e.g., show ad A, recommend product B, use headline C).
  • Why it matters: This is the concrete step taken by the system that will be evaluated.

Environment

The environment is the real-world system where the action is performed.

  • What it is: A website, mobile app, or any other system where users interact with the chosen actions.
  • How it interacts: It applies the action and observes the outcome (e.g., user interaction).

Reward (r)

This is the feedback signal that the algorithm learns from.

  • What it is: A numerical score indicating the success of the action (e.g., 1 for a click, 0 for no click).
  • Why it matters: It’s the “ground truth” that guides the algorithm’s learning process. The model is updated using the context, action, and this reward to improve future choices.

Core Formulas and Applications

Example 1: Epsilon-Greedy (ε-Greedy) Algorithm

This pseudocode outlines the epsilon-greedy strategy. With probability ε (epsilon), it explores by choosing a random action to gather new data. With probability 1-ε, it exploits its current knowledge by selecting the action with the highest estimated reward for the given context. It’s simple and effective for balancing exploration and exploitation.

Initialize reward estimates Q(c, a) for all context-action pairs
FOR each time step t = 1, 2, ...
  Observe context c_t
  Generate a random number p from
  IF p < ε:
    Select a random action a_t (Explore)
  ELSE:
    Select action a_t that maximizes Q(c_t, a) (Exploit)
  
  Execute action a_t and observe reward r_t
  Update Q(c_t, a_t) using the observed reward r_t
END FOR

Example 2: LinUCB (Linear Upper Confidence Bound)

LinUCB assumes a linear relationship between the context features and the expected reward. It calculates a confidence bound for each arm's predicted reward and chooses the arm with the highest bound, effectively balancing the uncertainty (exploration) and the predicted performance (exploitation). It is widely used in recommendation systems and online advertising.

FOR each time step t = 1, 2, ...
  Observe context features x_{t,a} for each arm a
  FOR each arm a:
    Calculate p_{t,a} = A_a^{-1} * x_{t,a}
    Calculate UCB_a = x_{t,a}^T * θ_a + α * sqrt(x_{t,a}^T * p_{t,a})
  
  Choose arm a_t with the highest UCB
  Observe reward r_t
  Update matrix A_{a_t} and vector b_{a_t}:
  A_{a_t} = A_{a_t} + x_{t,a_t} * x_{t,a_t}^T
  b_{a_t} = b_{a_t} + r_t * x_{t,a_t}
  Update θ_{a_t} = A_{a_t}^{-1} * b_{a_t}
END FOR

Example 3: Thompson Sampling

Thompson Sampling is a Bayesian approach where each arm is associated with a reward distribution (e.g., a Beta distribution for click/no-click rewards). At each step, it samples a reward value from each arm's posterior distribution and chooses the arm with the highest sampled value. This naturally balances exploration and exploitation based on model uncertainty.

Initialize parameters (α_a, β_a) for each arm's Beta distribution
FOR each time step t = 1, 2, ...
  Observe context c_t
  FOR each arm a:
    Sample a value θ_a from Beta(α_a, β_a)
  
  Select arm a_t with the highest sampled θ
  Observe binary reward r_t (0 or 1)
  
  Update parameters for the chosen arm a_t:
  IF r_t = 1:
    α_{a_t} = α_{a_t} + 1
  ELSE:
    β_{a_t} = β_{a_t} + 1
END FOR

Practical Use Cases for Businesses Using Contextual Bandits

  • Personalized Recommendations: E-commerce and media platforms use contextual bandits to tailor product or content suggestions based on user behavior, device, and browsing history, increasing engagement and conversion rates.
  • Dynamic Pricing: Businesses can optimize pricing strategies in real-time by treating different price points as "arms" and using context like demand, user segment, and time of day to maximize revenue.
  • Optimized Ad Placement: In online advertising, contextual bandits select the most relevant ad to display to a user by considering their demographics and browsing context, which improves click-through rates and ad effectiveness.
  • Clinical Trial Optimization: In healthcare, contextual bandits can dynamically assign patients to different treatment arms based on their specific characteristics, potentially identifying the most effective treatments for patient subgroups faster.
  • UI/UX Personalization: Websites and apps can personalize user interface elements, such as button colors or layouts, for different user segments to optimize user experience and achieve higher goal completion rates.

Example 1: Dynamic Pricing Strategy

CONTEXT:
  - user_segment: "new_visitor"
  - time_of_day: "peak_hours"
  - current_demand: "high"
ARMS (Price Points):
  - $9.99
  - $12.99
  - $14.99
LOGIC: Bandit model selects a price point based on the context to maximize the probability of a purchase.
BUSINESS USE CASE: An online ride-sharing service uses this to adjust fares based on real-time context, balancing driver supply with rider demand to maximize completed trips and revenue.

Example 2: News Article Recommendation

CONTEXT:
  - user_history: ["sports", "technology"]
  - device_type: "mobile"
  - location: "USA"
ARMS (Article Categories):
  - "Politics"
  - "Sports"
  - "Technology"
  - "Business"
LOGIC: Bandit model predicts the highest click-through rate for articles, prioritizing "Sports" and "Technology" for this user.
BUSINESS USE CASE: A media publisher personalizes its homepage for each visitor, showing articles most likely to be clicked, thereby increasing reader engagement and ad impressions.

Example 3: Personalized Marketing Offers

CONTEXT:
  - purchase_history_value: "high"
  - days_since_last_visit: 30
  - campaign_channel: "email"
ARMS (Offer Types):
  - "10% Discount"
  - "Free Shipping"
  - "Buy One, Get One Free"
LOGIC: Bandit determines that for a high-value, lapsed customer, "Free Shipping" has the highest probability of re-engagement.
BUSINESS USE CASE: An e-commerce brand sends personalized promotional emails to different customer segments to maximize conversion rates and customer lifetime value.

🐍 Python Code Examples

This example demonstrates a simple Epsilon-Greedy contextual bandit from scratch using NumPy. It defines a basic environment where rewards depend on the context and which arm is chosen. The `EpsilonGreedyBandit` class makes decisions by either exploring (choosing randomly) or exploiting (choosing the best-known arm for the current context).

import numpy as np

class EpsilonGreedyBandit:
    def __init__(self, num_arms, epsilon=0.1):
        self.num_arms = num_arms
        self.epsilon = epsilon
        # Using a dictionary to store Q-values for each context
        self.q_values = {}

    def choose_arm(self, context):
        context_key = str(context)
        if context_key not in self.q_values:
            self.q_values[context_key] = np.zeros(self.num_arms)

        if np.random.rand() < self.epsilon:
            # Exploration
            return np.random.choice(self.num_arms)
        else:
            # Exploitation
            return np.argmax(self.q_values[context_key])

    def update(self, context, arm, reward):
        context_key = str(context)
        if context_key not in self.q_values:
            self.q_values[context_key] = np.zeros(self.num_arms)
        
        # Update Q-value using a simple averaging method
        self.q_values[context_key][arm] += 0.1 * (reward - self.q_values[context_key][arm])

# Example Usage
num_arms = 3
contexts = [,,,]
bandit = EpsilonGreedyBandit(num_arms=num_arms, epsilon=0.1)

for i in range(1000):
    context = contexts[np.random.choice(len(contexts))]
    chosen_arm = bandit.choose_arm(context)
    
    # Simulate reward (e.g., arm 0 is best for context)
    reward = 1 if (chosen_arm == 0 and context ==) or 
                   (chosen_arm == 1 and context ==) else 0
    
    bandit.update(context, chosen_arm, reward)

print("Learned Q-values:", bandit.q_values)

This example illustrates how to use the `vowpalwabbit` library, a powerful tool for efficient contextual bandit implementation. The code sets up a bandit problem where the cost (the negative of the reward) is provided for the chosen action. The model learns a policy that maps contexts to actions to minimize cumulative cost.

from vowpalwabbit import pyvw

# Initialize Vowpal Wabbit in contextual bandit mode
model = pyvw.vw("--cb_explore 2 --quiet")

# Contexts: user features
user_contexts = [
    {'user': 'Tom', 'age': 25},
    {'user': 'Anna', 'age': 35}
]
# Actions: which ad to show
actions = [
    {'ad': 'sports'},
    {'ad': 'news'}
]

# Simulate learning loop
for i in range(100):
    # Get a random context
    context = user_contexts[i % 2]
    
    # Format for VW: shared_features | action_features
    # We provide context for each action
    vw_format_1 = f"shared |user {context['user']} age={context['age']}n|ad {actions['ad']}"
    vw_format_2 = f"shared |user {context['user']} age={context['age']}n|ad {actions['ad']}"
    
    # Predict which action to take
    prediction = model.predict([vw_format_1, vw_format_2])
    chosen_action_index = prediction - 1 # VW is 1-based

    # Simulate reward/cost. Let's say Tom (age 25) prefers sports
    cost = 0
    if context['user'] == 'Tom' and chosen_action_index == 0: # sports ad
        cost = -1 # reward of 1
    elif context['user'] == 'Anna' and chosen_action_index == 1: # news ad
        cost = -1 # reward of 1
    
    # Learn from the result
    # Format: action:cost:probability | features
    learn_string = f"{chosen_action_index+1}:{cost}:{prediction} |user {context['user']} age={context['age']}n|ad {actions[chosen_action_index]['ad']}"
    model.learn(learn_string)

# Make a final prediction for a user
final_prediction = model.predict([f"shared |user Tom age=25n|ad {actions['ad']}",
                                  f"shared |user Tom age=25n|ad {actions['ad']}"])
print(f"Final prediction for Tom: Action {final_prediction} with probability {final_prediction}")

🧩 Architectural Integration

Data Flow and System Integration

Contextual bandits are typically integrated into existing application architectures as a microservice or an API endpoint. The standard data flow begins when a client application (e.g., a web server or mobile app backend) sends a request containing the current context to the bandit service. This context is a feature vector containing user attributes, environmental data, and other relevant information.

The bandit service processes this context, runs it through the trained model to predict the best action, and returns the chosen action to the client. The client application then executes this action (e.g., renders a specific UI variant) and logs the outcome. Crucially, a feedback loop is established where the result of the action (the reward) is sent back to the bandit system to update the model, often through an asynchronous data pipeline.

Dependencies and Infrastructure

The implementation of a contextual bandit system relies on several key infrastructure components:

  • A feature store or data warehouse to source real-time and historical context data.
  • A model serving environment capable of low-latency predictions to ensure a fast user experience. This could be a dedicated API server or a serverless function.
  • A data ingestion pipeline (e.g., using event streaming platforms like Kafka or a message queue) to reliably collect reward feedback.
  • A model training and updating pipeline, which can be scheduled to run periodically (e.g., daily) or triggered by a certain volume of new data. This pipeline retrains the bandit model with new interaction data to adapt to changing patterns.
  • Logging and monitoring systems to track prediction performance, reward metrics, and system health.

The bandit's model itself is often stored in a model registry, allowing for versioning and controlled deployments. The overall architecture is designed to be highly available and scalable to handle real-time decision requests from the core application.

Types of Contextual Bandits

  • Linear Bandits (e.g., LinUCB): This is one of the most common types. It assumes that the expected reward of an action is a linear function of the context features. It's computationally efficient and works well when this linearity assumption holds, making it popular for recommendation systems.
  • Epsilon-Greedy (ε-Greedy) for Context: A simple yet effective strategy where the algorithm explores a random action with a small probability (epsilon) and exploits the best-known action for a given context the rest of the time. It is easy to implement and provides a baseline for performance.
  • Tree-Based Bandits: These models use decision trees or random forests to capture complex, non-linear relationships between contexts and rewards. They can partition the context space into regions and learn different policies for each, making them powerful for handling intricate interactions between features.
  • Neural Bandits: This approach uses neural networks to represent the relationship between context and rewards. It is highly flexible and can model extremely complex, non-linear patterns, making it suitable for high-dimensional contexts like images or text, although it requires more data and computational resources.
  • Thompson Sampling for Context: A Bayesian method where the algorithm models the reward distribution for each action. To make a decision, it samples from these distributions and picks the action with the highest sample. Its ability to incorporate uncertainty makes it very effective at balancing exploration and exploitation.

Algorithm Types

  • LinUCB. This algorithm models the reward as a linear function of the context. It selects actions using an "upper confidence bound" (UCB) that balances choosing known high-reward actions with exploring actions that have high uncertainty.
  • Epsilon-Greedy. A straightforward algorithm that chooses a random action with a fixed probability (epsilon) to explore, and otherwise chooses the action with the highest estimated reward for the current context (exploit). It is simple but can be effective.
  • Thompson Sampling. A Bayesian algorithm that maintains a probability distribution for the reward of each arm. It selects an arm by sampling from these distributions and choosing the one with the highest sample, naturally balancing exploration and exploitation.

Popular Tools & Services

Software Description Pros Cons
Vowpal Wabbit (VW) An open-source, fast, and scalable machine learning library with a strong focus on contextual bandits. It is designed for high-throughput, online learning scenarios and is widely used in production for personalization and ad recommendation. Extremely fast and memory-efficient; supports a wide range of bandit algorithms; mature and production-ready. Steep learning curve due to its unique command-line interface and data format; requires more manual setup than managed services.
Amazon SageMaker RL A fully managed service from AWS that provides tools to build, train, and deploy reinforcement learning models, including contextual bandits. It integrates with other AWS services for data storage, training, and deployment, simplifying the ML workflow. Managed infrastructure reduces operational overhead; integrates well with the AWS ecosystem; supports popular RL frameworks like TensorFlow and PyTorch. Can be expensive for large-scale or continuous training; may introduce vendor lock-in; complexity can be high for simple use cases.
Google Cloud AutoML Tables A service that automates the process of building machine learning models on structured data. It can be adapted to create a contextual bandit pipeline, handling feature engineering and model selection automatically, making it accessible to non-experts. Highly automated, reducing the need for ML expertise; performs automated feature engineering and hyperparameter tuning; easy to deploy models as APIs. Less control over model architecture compared to manual coding; can be a "black box"; cost can be higher than building from scratch.
Optimizely / Statsig These are experimentation platforms that have expanded from traditional A/B testing to include multi-armed and contextual bandits. They provide a user-friendly interface for marketers and product managers to run optimization experiments without deep technical knowledge. Easy-to-use graphical interface; integrates experimentation with analytics; automates traffic allocation and learning. Primarily focused on web/mobile UI optimization; may lack the flexibility for more complex, custom bandit problems; subscription-based pricing can be costly.

📉 Cost & ROI

Initial Implementation Costs

The initial cost of implementing a contextual bandit system varies significantly based on the scale and complexity of the project. Small-scale deployments, perhaps for personalizing a single feature in an app, could be developed by a single engineer over several weeks. Large-scale, enterprise-grade systems require a dedicated team and more robust infrastructure.

  • Development & Expertise: $15,000 - $75,000 for small to mid-sized projects. For large-scale custom solutions, this can exceed $150,000, factoring in data science and engineering salaries.
  • Infrastructure & Tooling: Costs can range from minimal for open-source tools on existing cloud infrastructure to $10,000–$50,000+ annually for managed services, feature stores, and high-throughput model serving environments.
  • Data Preparation: A significant, often underestimated cost involves creating data pipelines to supply clean, real-time context, which can add 20-40% to the initial development time.

Expected Savings & Efficiency Gains

Contextual bandits automate and accelerate the optimization process, leading to significant efficiency gains over manual methods or traditional A/B testing. Instead of waiting weeks for an A/B test to conclude, bandits begin optimizing traffic in near real-time. This can lead to a 10-30% faster convergence on optimal strategies. Operationally, it reduces the manual labor required from product managers and analysts to set up and interpret tests, potentially saving hundreds of hours per year. One of the key risks is integration overhead; if the bandit system is not seamlessly integrated with data sources and client applications, it can lead to underutilization and wasted investment.

ROI Outlook & Budgeting Considerations

The ROI for contextual bandits is typically measured by the lift in a key business metric, such as conversion rate, click-through rate, or revenue per user. Businesses often report a 5-15% lift in their target metric after replacing A/B tests with a well-implemented bandit system. A projected ROI can often be in the range of 75-250% within the first 12-18 months, depending on the scale and business value of the optimized decision. When budgeting, companies should account for not just the initial build but also ongoing maintenance, monitoring, and iterative improvement, which typically amounts to 15-20% of the initial implementation cost annually.

📊 KPI & Metrics

Tracking the right Key Performance Indicators (KPIs) and metrics is essential for evaluating the success of a contextual bandit implementation. It's important to monitor not only the technical performance of the algorithm itself but also its direct impact on business objectives. This ensures the system is not just algorithmically sound but is also delivering tangible value.

Metric Name Description Business Relevance
Click-Through Rate (CTR) / Conversion Rate The percentage of users who click on a recommended item or complete a desired action. Directly measures the effectiveness of the bandit in engaging users and achieving primary business goals.
Cumulative Reward The total sum of rewards accumulated by the bandit algorithm over a period of time. Indicates the overall performance and value generated by the bandit system since its deployment.
Regret The difference between the cumulative reward of the optimal (but unknown) policy and the bandit's actual cumulative reward. A theoretical metric used to measure how quickly and effectively the algorithm is learning the best policy.
Prediction Latency The time taken by the model to return an action after receiving a context. Ensures the system is fast enough for real-time applications and does not degrade the user experience.
Lift Over Random/Baseline The percentage improvement in the primary metric compared to a random choice policy or a previous static strategy. Quantifies the incremental value provided by the contextual bandit and helps justify its ROI.

In practice, these metrics are monitored through a combination of application logs, real-time analytics dashboards, and automated alerting systems. For instance, a dashboard might visualize the cumulative reward trend and the CTR for different user segments. Automated alerts can be configured to trigger if key metrics like latency exceed predefined thresholds or if the overall reward rate drops unexpectedly. This continuous feedback loop allows data science and engineering teams to identify issues, fine-tune model parameters, and optimize the system for better performance.

Comparison with Other Algorithms

Contextual Bandits vs. A/B Testing

A/B testing involves splitting traffic evenly between variations and waiting until one proves to be a statistically significant winner for the entire population. Contextual bandits are more dynamic; they learn from interactions in real-time and begin shifting traffic towards better-performing variations much faster. While A/B testing finds the single best option for everyone, contextual bandits can find different "winners" for different user segments based on their context, leading to a more personalized and optimized outcome. The primary strength of contextual bandits here is speed and personalization, whereas A/B testing is simpler to implement and interpret.

Contextual Bandits vs. Multi-Armed Bandits (MAB)

The key difference is "context." A standard multi-armed bandit learns the single best action to take over time across all situations, but it does not use any side information. A contextual bandit, however, uses features about the user or situation to make its choice. For example, a MAB might learn that "Ad A" is generally the best. A contextual bandit could learn that "Ad A" is best for mobile users in the morning, while "Ad B" is better for desktop users in the evening, leading to superior overall performance.

Contextual Bandits vs. Full Reinforcement Learning (RL)

Contextual bandits are considered a simplified form of reinforcement learning. The main distinction is that bandits operate on single-step decisions with immediate rewards. They do not consider how an action might affect future contexts or long-term rewards. Full RL algorithms, like Q-learning or policy gradients, are designed for sequential problems where actions have delayed consequences and influence future states. Contextual bandits are more efficient and require less data for problems like recommendations or ad placement, while full RL is necessary for complex tasks like game playing or robotics control.

⚠️ Limitations & Drawbacks

While powerful, contextual bandits are not a universal solution and may be inefficient or problematic in certain scenarios. Their effectiveness depends on the quality of contextual data and the nature of the decision-making problem. Understanding their limitations is key to successful implementation.

  • Requires High-Quality Context: The performance of a contextual bandit is heavily dependent on the availability of relevant and predictive features. If the context is sparse, noisy, or irrelevant, the algorithm may perform no better than a simpler multi-armed bandit.
  • Single-Step Decision Focus: Contextual bandits are designed for stateless, immediate-reward problems. They cannot handle scenarios where an action affects future states or has delayed rewards, which are better suited for full reinforcement learning.
  • The Cold Start Problem: When a new action ("arm") is introduced, the algorithm has no prior information about it and must explore it extensively to learn its effectiveness. This can lead to suboptimal performance during the initial learning phase for that arm.
  • Complexity in Implementation: Properly setting up a contextual bandit system is more complex than a simple A/B test. It requires robust data pipelines for context and rewards, model training infrastructure, and careful tuning of exploration-exploitation parameters.
  • Scalability with Many Actions: As the number of actions grows, the algorithm needs more data and time to effectively explore all options and learn their reward structures, which can be a bottleneck in systems with thousands of potential actions.
  • Risk of Overfitting: With highly detailed contexts, there's a risk of the model overfitting to specific user profiles, leading to poor generalization for new or unseen contexts.

In situations with long-term goals or where actions have cascading effects, hybrid strategies or more advanced reinforcement learning approaches might be more suitable.

❓ Frequently Asked Questions

How are contextual bandits different from multi-armed bandits?

The primary difference is the use of "context." A multi-armed bandit tries to find the single best action for all situations, while a contextual bandit uses side information (like user demographics, location, or time of day) to choose the best action for each specific situation, enabling personalization.

Can contextual bandits replace A/B testing?

In many cases, yes, especially for personalization. Contextual bandits are more efficient because they dynamically allocate traffic to better-performing variations, leading to faster optimization and reduced opportunity cost compared to the fixed allocation in A/B tests. However, A/B tests are simpler for validating changes where personalization is not the primary goal.

What kind of data is needed for a contextual bandit?

You need three key types of data: 1) a context vector (features describing the situation), 2) a set of actions that were taken, and 3) the reward that resulted from each action. For example, user features, the ad that was shown, and whether the user clicked on it.

What is the "exploration-exploitation" trade-off?

It's the central dilemma in bandit problems. Exploitation means choosing the action that currently seems best based on past data to maximize immediate rewards. Exploration means trying different, potentially suboptimal actions to gather more information that could lead to better long-term rewards.

When should I not use a contextual bandit?

You should avoid using a contextual bandit for problems where actions have long-term consequences that affect future states. For these scenarios, which involve delayed rewards and state transitions, a full reinforcement learning approach (like Q-learning) is more appropriate. Bandits are best for immediate, stateless decisions.

🧾 Summary

Contextual bandits are a powerful class of machine learning algorithms that optimize real-time decision-making by using contextual information. They excel at personalizing experiences, such as recommendations or advertisements, by balancing the need to exploit known-good options with exploring new ones. By dynamically adapting to user behavior and other situational data, they consistently outperform static A/B tests and non-contextual bandits.