Thanks for the detailed explanation. I just started learning about reinforcment learning during my studies and am still struggling to make sense of your explanation. Can you explain the difference again, but this time simplifying the language and maybe giving an example?
Sure, I'm happy to do it, here you go: 

In reinforcement learning, a value function represents the expected long-term reward of a state or a state-action pair, while a policy is a mapping from states to actions that defines the actions taken by an agent in different states.

Here is an example:

Imagine a robot trying to navigate to a goal in a maze. The value function would assign a numerical value to each state in the maze (e.g., how close the robot is to the goal), indicating how good it is for the robot to be in that state. The policy, on the other hand, would dictate what action the robot should take in each state (e.g., turn left, turn right, go straight) to achieve the goal.

So, the value function gives an estimation of how valuable each state is, while the policy determines the best action to take in each state to achieve the goal.

Let me know if this is clear otherwise I'll be happy to provide a different example.
What is the difference between learning a value function and a policy in reinforcement learning? In reinforcement learning (RL), a value function and a policy are two key concepts that are used to model an agent's behavior in an environment.

A value function is a mapping from states (or state-action pairs) to a scalar value that represents the expected return, or long-term reward, that an agent can expect to receive by following a certain policy. The value function provides a measure of how "good" a state or state-action pair is, and it is used to guide the agent's behavior by helping it choose actions that lead to states with high expected returns. There are two main types of value functions: state-value functions and action-value functions. State-value functions represent the expected return for an agent starting from a given state, while action-value functions represent the expected return for an agent starting from a given state and taking a specific action.

A policy, on the other hand, is a mapping from states to actions that defines the actions that an agent should take in each state. The goal of RL is to find an optimal policy that maximizes the expected return over time. Policies can be deterministic, meaning that they specify a unique action for each state, or they can be stochastic, meaning that they specify a probability distribution over actions for each state.

In summary, the value function provides a measure of how good a state or state-action pair is, while the policy defines the actions that an agent should take. The value function is used to evaluate the performance of a policy, and the policy is updated based on the values obtained from the value function. The process of finding an optimal policy involves learning both a value function and a policy. The value function is learned by estimating the expected return, and the policy is updated based on the estimated value function. The interaction between the value function and the policy is what makes RL a powerful tool for solving sequential decision-making problems.