The insights from the relationship between the Bellman equation and representation rank can be leveraged to develop new RL techniques beyond just regularization by integrating these principles into the core algorithms of reinforcement learning. For example, the constraints derived from the Bellman equation on the representation rank can be used to guide the exploration-exploitation trade-off in RL algorithms. By incorporating these constraints into the action selection process or value function updates, the agent can make more informed decisions that balance the need for exploration and exploitation effectively. Furthermore, the insights can be utilized to design novel reward shaping mechanisms or state space transformations that enhance the learning process by leveraging the implicit constraints on representation rank. This approach can lead to the development of more efficient and robust RL techniques that leverage the fundamental principles of the Bellman equation and representation rank.