Sparse rewards are still one of the hardest problems in reinforcement learning.
When feedback is delayed, most agents struggle to learn anything meaningful.
A recent IEEE Access article proposes a different approach:
š ieeexplore.ieee.org/document/113...
A central challenge in reinforcement learning is enabling agents to efficiently learn in environments where rewards are sparse or significantly delayed. Many reward shaping approaches rely on handcraf...
