Learning Dense Reward with Temporal Variant Self-Supervision

Yuning Wu, Jieliang Luo, Hui Li

Rewards play an essential role in reinforcement learning. In contrast to rule-based game environments with well-defined reward functions, complex real-world robotic applications, s…