What is value-based Reinforcement Learning focused on?

Value-based reinforcement learning is primarily focused on estimating the value of states and actions. In this approach, the agent learns to assess the expected return (or future reward) that can be obtained from each state or state-action pair. By evaluating these values, the agent is able to make informed decisions about which actions to take in various states to maximize its total reward over time.

Estimating the value of states allows the agent to understand how favorable or unfavorable particular states are in terms of the potential rewards they can lead to. Similarly, estimating the value of actions helps the agent determine which actions to take when in a specific state.

This approach contrasts with policy-based methods that directly learn the policy, or with techniques focused solely on minimizing computational costs or estimating actions without considering their relationship to states. By concentrating on both states and actions, value-based reinforcement learning provides a comprehensive framework that supports effective decision-making over time.