# 20 Reinforcement Learning Interview Questions and Answers

Prepare for the types of questions you are likely to be asked when interviewing for a position where Reinforcement Learning will be used.

Prepare for the types of questions you are likely to be asked when interviewing for a position where Reinforcement Learning will be used.

Reinforcement Learning is a popular technique for training AI agents to optimally solve complex tasks. When interviewing for a position in AI or machine learning, it is likely that the interviewer will ask you questions about your experience with reinforcement learning. Reviewing common questions and preparing your answers ahead of time can help you feel confident and ace the interview. In this article, we review the most commonly asked reinforcement learning questions and provide tips on how to answer them.

Here are 20 commonly asked Reinforcement Learning interview questions and answers to prepare you for your interview:

Reinforcement Learning is a type of machine learning that is concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

The Markov Decision Process is a framework that is used to model decision making in situations where there is uncertainty. It is a way of representing an environment in terms of states, actions, and rewards, and can be used to find the optimal policy for an agent operating in that environment.

Bellman equations are a set of equations that define how value is propagated through a Markov decision process. In reinforcement learning, these equations are used to help the agent learn which actions will lead to the most reward.

The main difference between supervised learning and reinforcement learning is that in supervised learning, the training data is labeled and the algorithm is told what the correct output should be for each input, while in reinforcement learning, the training data is not labeled and the algorithm has to figure out what the correct output should be for each input by trial and error.

Value iteration is a method used to solve an MDP by iteratively improving the value function until it converges. The value function is a mapping from states to rewards, and it represents the expected return from a given state. The value iteration algorithm works by starting with an initial value function and then repeatedly updating it according to the Bellman equation. The algorithm converges when the value function converges to the true value function of the MDP.

Q-learning is a type of reinforcement learning that is used to find the optimal action to take in a given state. Q-learning works by creating a Q-table that contains the expected reward for taking each action in each state. The Q-table is then updated as the agent interacts with the environment and learns more about which actions lead to the highest rewards.

A policy gradient method is a reinforcement learning algorithm that uses gradient descent to update a policy. The algorithm uses feedback from the environment to adjust the policy in order to maximize reward.

On-policy evaluation is used to assess the quality of a policy by running it in an environment and measuring the resulting rewards. This is the most common form of evaluation used in reinforcement learning. Off-policy evaluation is used to assess the quality of a policy by running it in an environment and measuring the rewards that would have been received if a different policy had been used. This is less common, but can be useful in certain situations.

Some common use cases for reinforcement learning algorithms include robotics, gaming, and financial trading.

Dynamic programming methods are a class of algorithms used for solving optimization problems. They are often used for problems where the optimal solution can be found by breaking the problem down into smaller subproblems and then solving each of those subproblems recursively.

SARSA is a type of reinforcement learning algorithm that is used to learn how to take actions in an environment in order to maximize a reward. Q-Learning is a similar algorithm, but it does not take into account the future rewards when making decisions – it only looks at the immediate reward. This can sometimes lead to sub-optimal decisions being made in the long run.

Model-based reinforcement learning algorithms learn a model of the environment and use this model to make predictions about how the environment will change in the future. This allows them to plan ahead and choose actions that will lead to the most reward. Model-free reinforcement learning algorithms do not learn a model of the environment. Instead, they directly learn which actions lead to the most reward. This can be faster and easier to learn, but it can also be less efficient in the long run.

Monte Carlo Policy Gradient methods are a type of reinforcement learning that has a number of advantages. One advantage is that it can learn from very high-dimensional data, such as images. Additionally, Monte Carlo Policy Gradient methods can learn from data that is non-stationary, meaning that the data changes over time. Finally, Monte Carlo Policy Gradient methods are able to learn from data that is very sparse, meaning that there are only a few data points available.

The main limitation of using Value Iteration methods is that they can be very slow to converge on the optimal solution. This is because they have to evaluate the value of every state in the environment before they can find the optimal policy. Additionally, Value Iteration methods can sometimes get stuck in local optima, meaning that they find a sub-optimal solution because they do not explore the entire state space.

Deep Q Networks are a type of reinforcement learning algorithm that are designed to work well with large, complex action spaces. They are able to do this by approximating the Q function using a deep neural network. While Deep Q Networks are often very effective, they are not always the best choice of algorithm. Other RL algorithms may be more effective in some situations, such as when the action space is small or when the environment is very simple.

Function approximation is a technique used in RL when an agent needs to learn a value function or policy that is too complex to be represented by a simple lookup table. In this case, the agent approximates the function using a mathematical function that is easier to compute. This can be done using a variety of methods, such as linear regression or artificial neural networks.

Reward shaping is a technique used in reinforcement learning to encourage an AI agent to pursue a particular goal. It is accomplished by providing positive reinforcement (rewards) for actions that are closer to the desired goal, and negative reinforcement (punishments) for actions that are further from the desired goal.

There are two schools of thought when it comes to reward shaping. Some believe that it is an effective and ethical way to teach AI agents good behavior. Others believe that it is a form of cheating, and that AI agents should only be rewarded for actions that they would naturally pursue on their own.

Some of the challenges faced when building large scale reinforcement learning systems include the need for more data in order to train the system, the need for more computational resources, and the need to design algorithms that can learn from a variety of different tasks. Additionally, it can be difficult to evaluate the performance of a reinforcement learning system, as there is often a trade-off between exploration and exploitation.

Bootstrapping methods are a type of reinforcement learning algorithm that learn by using a value function or policy to make predictions. The predictions are then used to improve the value function or policy, and the process is repeated until the value function or policy converges.

Eligibility trace is a technique used in reinforcement learning that helps the learning process by keeping track of which states and actions are responsible for a reward. This information is then used to reinforce the states and actions that led to the reward, so that the agent is more likely to repeat them in the future.