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In reinforcement learning, a reward path is a path that an agent takes in order to obtain cumulative rewards. This terminology isn't really used very much on its own in machine learning, but the concept of reward is central to many machine learning algorithms and Markov decision process models.
A Markov decision process runs an agent through a sequence of states and analyzes the result. Q-learning or reinforcement learning practices run the model continually, looking for rewards and adapting the model appropriately. So you could say that the reward path is the path that generates the most reward.
Another way to explain a reward path in IT is to contrast it with a reward pathway in the human brain. In the human brain, a reward pathway is associated with a dopamine hit. In reinforcement learning and other forms of machine learning, the dopamine is not present, and the reward is based on a program to reward function instead.
One prime example is a reinforcement learning program that helps a computer learn to play a challenging video game. Programmers define the reward as surviving the game, and then the reinforcement learning model runs through the Markov decision process numerous times, building its knowledge of how to obtain reward.
Reinforcement learning and similar technologies are playing a major role in helping computers and technologies to evolve to a higher level of artificial intelligence.