Reinforcement Learning with Segment Reward Feedback under Linear Function Approximation
The research studies reinforcement learning using segment reward feedback to bridge sparse trajectory data and costly fine-grained rewards under linear function approximation. The authors developed algorithms for known and unknown transitions, revealing that binary feedback improves exponentially with more segments, while sum feedback is robust. Feature-based segmentation offers no advantage over equal segmentation.