What type of reinforcement learning algorithm improves a policy that differs from the one currently applied by the agent?

The correct choice refers to an off-policy learning algorithm, which is characterized by its ability to learn about one policy while following another policy. This means that the agent can gather experience based on its current policy, but it can also update or improve a separate policy that is not being followed at the moment.

In reinforcement learning, this becomes particularly important as it allows for more flexibility and efficient learning from diverse experiences. An agent can learn from past experiences and observations generated by different policies, which can lead to improved decision-making and strategies without having to follow that specific policy during the learning process.

For instance, in a typical off-policy setup, an agent might explore its environment and collect data using a behavior policy, while simultaneously refining a target policy that it aims to improve. This dynamic allows the agent to leverage a broader dataset for learning, which can enhance both speed and effectiveness in finding optimal solutions.

In contrast, on-policy learning algorithms only evaluate and improve the policy that is currently being followed by the agent, which can limit exploration and the diversity of experiences utilized in the learning process. The supervised learning algorithm is not applicable here as it involves learning from labeled data rather than reinforcement signals, and Q-learning is a specific type of off-policy algorithm, but not