Before revision:  One agent learns both which states to add shaping rewards and their optimal magnitudes and the other agent  learns the optimal policy for the task using the shaped rewards.
After revision:  A reward-shaping agent (Shaper) uses switching controls to determine which states to add shaping rewards and their optimal magnitudes and the other agent learns the optimal policy for the task using the shaped rewards.