## What are direct search methods?

Direct search is a method for solving optimization problems that does not require any information about the gradient of the objective function.

## How does Q learning work?

Q-learning is a model-free, off-policy reinforcement learning that will find the best course of action, given the current state of the agent. Depending on where the agent is in the environment, it will decide the next action to be taken.

**What is RL episode?**

In RL, episodes are considered agent-environment interactions from initial to final states. For example, in a car racing video game, you start the game (initial state) and play the game until it is over (final state). This is called an episode.

**What is a policy search?**

Policy search is a subfield in reinforcement learning which focuses on finding good parameters for a given policy parametrization. It is well suited for robotics as it can cope with high-dimensional state and action spaces, one of the main challenges in robot learning.

### What is local and global optimization?

Local optimization involves finding the optimal solution for a specific region of the search space, or the global optima for problems with no local optima. Global optimization involves finding the optimal solution on problems that contain local optima.

### What is the difference between Q-Learning and Sarsa?

Q-Learning technique is an Off Policy technique and uses the greedy approach to learn the Q-value. SARSA technique, on the other hand, is an On Policy and uses the action performed by the current policy to learn the Q-value.

**What is sarsa algorithm?**

State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. It was proposed by Rummery and Niranjan in a technical note with the name “Modified Connectionist Q-Learning” (MCQ-L).

**What is ML model training?**

Training a model simply means learning (determining) good values for all the weights and the bias from labeled examples. In supervised learning, a machine learning algorithm builds a model by examining many examples and attempting to find a model that minimizes loss; this process is called empirical risk minimization.

## What is RL advantage?

Advantage Function: Usually denoted as A(s,a), the Advantage function is a measure of how much is a certain action a good or bad decision given a certain state — or more simply, what is the advantage of selecting a certain action from a certain state.

## What is a policy in reinforcement learning?

A reinforcement learning policy is a mapping from the current environment observation to a probability distribution of the actions to be taken. During training, the agent tunes the parameters of its policy approximator to maximize the long-term reward.

**What is Generalization in reinforcement learning?**

A generalisation problem occurs when the training and testing context sets are different, and the policy then learns to rely on features of the training environments which may change at test time.

**Which methodology global optimization?**

Stochastic tunneling (STUN) is an approach to global optimization based on the Monte Carlo method-sampling of the function to be objectively minimized in which the function is nonlinearly transformed to allow for easier tunneling among regions containing function minima.

### What is simulated annealing method?

Simulated annealing is a method for solving unconstrained and bound-constrained optimization problems. The method models the physical process of heating a material and then slowly lowering the temperature to decrease defects, thus minimizing the system energy.

### What is SARSA method?

**Which is faster Q-learning and SARSA?**

It is worth mentioning that SARSA has a faster convergence rate than Q-learning and is less computationally complex than other RL algorithms [44] .

**What is difference between SARSA and Q-learning?**

QL directly learns the optimal policy while SARSA learns a “near” optimal policy. QL is a more aggressive agent, while SARSA is more conservative. An example is walking near the cliff.

## Is SARSA better than Q-learning?

If your goal is to train an optimal agent in simulation, or in a low-cost and fast-iterating environment, then Q-learning is a good choice, due to the first point (learning optimal policy directly). If your agent learns online, and you care about rewards gained whilst learning, then SARSA may be a better choice.

## What can we use for model interpretability *?

So, we are basically solving machine learning interpretability by using more machine learning! This is done by training a decision tree on the predictions of the black-box model (which is a random forest in our case). And once it provides good enough accuracy, we can use it to explain the random forest classifier.

**Which of the following are ML methods?**

Q. | Which of the following are ML methods? |
---|---|

B. | supervised Learning |

C. | semi-reinforcement Learning |

D. | All of the above |

Answer» a. based on human supervision |

**What is RL trajectory?**

So, what does it mean? According to this answer over Quora: In reinforcement learning terminology, a trajectory τ is the path of the agent through the state space up until the horizon H. The goal of an on-policy algorithm is to maximize the expected reward of the agent over trajectories.

### What is RL state?

The state is the current board position, the actions are the different places in which you can place an ‘X’ or ‘O’, and the reward is +1 or -1 depending on whether you win or lose the game. The “state space” is the total number of possible states in a particular RL setup.

### What is offline RL?

Offline RL is a paradigm that learns exclusively from static datasets of previously collected interactions, making it feasible to extract policies from large and diverse training datasets.

**What is on-policy method?**

On-policy methods attempt to evaluate or improve the policy that is used to make decisions. In contrast, off-policy methods evaluate or improve a policy different from that used to generate the data.

**What is TD error?**

The TD error indicates how far the current prediction function deviates from this condition for the current input, and the algorithm acts to reduce this error.

## What algorithms are used in reinforcement learning?

Comparison of reinforcement learning algorithms

Algorithm | Description | Action Space |
---|---|---|

SARSA – Lambda | State–action–reward–state–action with eligibility traces | Discrete |

DQN | Deep Q Network | Discrete |

DDPG | Deep Deterministic Policy Gradient | Continuous |

A3C | Asynchronous Advantage Actor-Critic Algorithm | Continuous |