What are value functions in Reinforcement Learning?

Example Answer

A value function estimates expected return under a particular policy. The state value Vπ(s) starts in state s and follows that policy. The action value Qπ(s,a) first takes action a, then follows the policy. Return includes later rewards, usually with a stated discount factor; it is not just the next reward. For a warehouse robot, two actions from the same location can have different values because they lead to different future routes and costs. I would distinguish the policy being evaluated from the best achievable value. A learned estimate also needs evaluation: a large predicted value does not prove the action will deliver that return.

Additional detail

Any personal project, employer or outcome details below are retained fictional examples. Replace them with truthful details from your own experience.

Value functions in Reinforcement Learning (RL) are foundational concepts that play a pivotal role in how agents learn and make decisions in an environment. Value functions essentially serve as a compass for an RL agent. They estimate how good it is for the agent to be in a given state, or how good it is to perform a certain action in a given state, taking into account the expected future rewards. This "goodness" or value is crucial for the agent to learn policies that guide it towards its goal more efficiently.

Two common value functions are the state-value function and the action-value function. The state-value function, denoted as V(s), estimates the expected return (or total reward) starting from state s and following a particular policy. On the other hand, the action-value function, denoted as Q(s, a), provides an estimate of the expected return starting from state s, taking an action a, and thereafter following a policy.

For instance, in a project aimed at optimizing logistics in a warehouse setting, we leveraged the action-value function to effectively guide robots in selecting the most efficient paths and actions for package sorting and delivery, significantly reducing operational costs and increasing efficiency.

Make it your own

For job seekers looking to showcase their expertise in Reinforcement Learning, especially when discussing value functions, it's imperative to emphasize not just the theoretical knowledge, but also how this knowledge can be applied to solve real-world problems. Sharing specific examples from past projects where understanding and applying value functions led to measurable improvements can be particularly compelling. Additionally, demonstrating an ongoing commitment to staying abreast of the latest research and best practices in RL will signal your value as a forward-thinking and innovative candidate.

References

Reinforcement learning lecture