On-Policy VS Off-Policy Reinforcement Learning

A reinforcement learning system consists of four main elements: An agentA policy A reward signal, and A value function An agent’s behaviour at any point of time is defined in terms of a policy. A policy is like a blueprint of the connections between perception and action in an environment.   In the next section, we shall talk about the key differences in the two main kind of policies: / On-policy reinforcement learningOff-policy reinforcement learning On-Policy VS Off-Policy Comparing reinforcement learning models for hyperparameter optimization is an expensive affair, and often practically infeasible. So the performance of these algorithms is evaluated via on-policy interactions with the target environment. These interactions of an on-policy lea