Before revision:  Constrained RL algorithms approach this problem by training agents to maximize given reward functions while respectingexplicitly defined constraints. However, in many cases, manually designing accurate constraints is a challenging task .
After revision:  Constrained RL algorithms approach this problem by training agents to maximize given reward functions while respectingexplicitly for the real world deployment of reinforcement learning (RL), it is critical that RL agents are aware of these constraints, so that they can act safely .