Unified curiosity-Driven learning with smoothed intrinsic reward estimation

In reinforcement learning (RL), the intrinsic reward estimation is necessary for policy learning when the extrinsic reward is sparse or absent. To this end, Unified Curiosity-driven Learning with Smoothed intrinsic reward Estimation (UCLSE) is proposed to address the sparse extrinsic reward problem from the perspective of completeness of intrinsic reward estimation. We further propose state distribution-aware weighting method and policy-aware...