What is a common characteristic of value-based methods in RL?

Value-based methods in Reinforcement Learning (RL) are characterized primarily by their reliance on value functions, which estimate the expected return (or future reward) of different states or state-action pairs. A central component of many value-based methods is the Q-table, which stores these expected rewards for state-action pairs, allowing the agent to make decisions based on past experiences and learned values.

The accuracy of the Q-table is critical to the effectiveness of these methods. If the values in the Q-table are not accurate, the agent may make suboptimal decisions, leading to poorer performance in achieving its goals. Therefore, the correct answer emphasizes the reliance on the Q-table in value-based methods, as it directly influences how well an agent can assess the potential value of different actions in various states.

In contrast, the other options do not align with the key characteristics of value-based methods. For example, while some methods may be less computationally intensive than others, this is not a defining trait of value-based RL. Additionally, value-based methods typically focus on state optimization through their Q-values rather than action optimization alone. Lastly, direct observation without rewards is not a feature of value-based methods, which generally depend heavily on reward feedback to update their value estimates.