Solving hard exploration tasks with sparse rewards is notoriously challenging in reinforcement learning (RL), which needs to address two key issues simultaneously: exploiting past successful experiences and exploring the unknown environment. Many prior works take expert demonstrations as successful experiences and learn to imitate them directly. However, these demonstrations are often not available in practice. Recently, curiosity-driven RL methods provide intrinsic rewards, encouraging the agent to explore states with high novelty. Nonetheless, they lack a mechanism for leveraging past good experiences effectively. This work presents a Pseudo Value Network Distillation (PVND) framework to balance the RL agent's exploitative and exploratory behaviors effectively and automatically. In particular, PVND learns to set high exploitation bonuses to the critical states in rewarded trajectories from past experiences and high exploration bonuses to the novel states that agents rarely visit during exploration. We theoretically demonstrate that PVND gives larger positive intrinsic rewards to more critical states. Furthermore, PVND automatically finds meaningful and critical hierarchical sub-tasks for agents to accomplish the final goal progressively. Competitive results in several hard exploration sparse reward problems have verified its effectiveness and efficiency.