#5816. Unified curiosity-Driven learning with smoothed intrinsic reward estimation

September 2026publication date
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Abstract:
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 weighting method to dynamically unify two mainstream intrinsic reward estimation methods. In this way, the agent can explore the environment more effectively and efficiently. Under this framework, we propose to employ an attention module to extract task-relevant features for a more precise estimation of intrinsic reward. Moreover, we propose to improve the robustness of policy learning by smoothing the intrinsic reward with a batch of transitions close to the current transition.
Keywords:
Reinforcement learning; Robust intrinsic reward; Task-relevant feature; Unified curiosity-driven exploration

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