Condition for Policy Invariance in Reward Shaping

A reinforcement learning agent is operating in an environment where taking a specific action in a given state results in a transition to a new state. The environment's original reward for this transition is -0.5. To guide the agent more effectively, a shaping function is added, which provides an additional reward value of +2.0 for this same transition. According to the standard formulation for reward shaping, what is the total transformed reward the agent receives?

Deconstructing a Shaped Reward Function

Analyzing Reward Components in a Maze Navigation Task