In Reinforcement Learning (RL), on-policy and off-policy learning are different methods of learning about an environment by interacting with it. The main difference between them lies in the data used to learn and update the RL policy.
On-policy learning involves updating the policy based on the experience of the policy currently being employed. This means that the agent updates its policy using the data generated by its current policy. In on-policy learning, the agent learns and improves its policy while it is actively interacting with the environment.
Off-policy learning, on the other hand, involves updating the policy based on the experience of another policy or set of data. In other words, the policy is updated using data generated by a different (off-policy) policy. This approach allows the agent to learn from the experience of other agents, even if they are completely different from the current policy.
To better illustrate the differences, lets consider an example of a robot learning to navigate through a maze. The robot’s task is to find the exit of the maze as quickly as possible.
In on-policy learning, the robot updates its policy based on its own actions and experiences. It starts by randomly mapping out the maze and eventually learns through trial and error which actions result in moving closer to the exit. As it proceeds, the robot continues to update its policy based on its current behavior in the environment, always taking into account the latest learning.
In off-policy learning, the robot can learn from the experience of another agent or policy. For example, an off-policy algorithm may allow the robot to learn from other robots that have previously navigated through the same maze, even if these robots used different learning strategies or were controlled by different policies. This can be particularly useful as the robot can take advantage of the knowledge learned by other agents and speed up its learning process.
Overall, the main difference between on-policy and off-policy learning lies in the type of learning data used to update the policy. On-policy learning relies on the data generated by the current policy, while off-policy learning allows the agent to learn from the experience of other policies or data. As a result, the choice of the learning method depends on the specific task requirements and the available data.