How To Automate Reward Design For Reinforcement Learning Systems

Despite the success of reinforcement learning algorithms, there are few challenges which are still pervasive. Rewards, which make up for much of the RL systems, are tricky to design. A smarter reward system ensures an outcome with better accuracy.  In the context of reinforcement learning, a reward is a bridge that connects the motivations of the model with that of the objective.  Reward design decides the robustness of an RL system. Designing a reward function doesn’t come with much restrictions and developers are free to formulate their own functions. The challenge, however, is the chance of getting stuck in local minima. Reward functions are peppered with clues to make the system/model/machine to move in a certain direction. The clues in this context are a bunch of mathemat