Off Policy Learning is a type of reinforcement learning in which the agent learns from an external policy, different from the policy it is trying to learn, called the target policy. The agent learns by interacting with the environment using the behavior policy and uses the experience to update the value function of the target policy.

Off Policy Learning has become popular due to its ability to learn from previously collected data, which can reduce the amount of interaction needed with the environment. This is useful when the cost of collecting data is high or when the agent is operating in a dangerous or expensive environment.

The main challenge with Off Policy Learning is that the distribution of the data collected by the behavior policy can be different from the distribution of the data that the target policy would generate. This is known as the distributional shift problem. If the distributional shift is significant, the performance of the target policy can degrade.

There are various methods to address the distributional shift problem in Off Policy Learning. One such method is importance sampling, which is used to re-weight the samples collected by the behavior policy according to the ratio of the target policy and the behavior policy. Another method is doubly-robust estimators, which use a combination of importance sampling and a correction term to reduce the variance of the estimate.

Off Policy Learning can be used for various tasks such as control, prediction, and decision-making. One popular Off Policy Learning algorithm is Q-Learning, which learns the optimal action-value function of the target policy by iteratively updating the Q-value estimates using the Bellman equation.

Other Off Policy Learning algorithms include SARSA, which learns the state-action value function, and Actor-Critic methods, which combine the policy and value functions to learn the optimal policy.

Off Policy Learning has been successfully applied in various domains such as robotics, finance, and healthcare. However, it is important to carefully design the behavior policy to ensure that the collected data is representative of the target policy.