Advantage Function as a Form of Shaped Reward

Calculating a Shaped Reward

An agent is being trained using value-based reward shaping. In a particular transition from state s_t to s_{t+1}, the agent receives an environmental reward r of 0. The agent's current value function estimates that the value of the next state, V(s_{t+1}), is substantially higher than the value of the current state, V(s_t). Based on the formula r' = r + γV(s_{t+1}) - V(s_t), what is the most likely consequence of this shaping on the agent's learning for this specific transition?

Analyze the value-based reward shaping formula, r' = r + γV(s_{t+1}) - V(s_t), by matching each component to its specific role or definition within the general structure of potential-based reward shaping.