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洞見 - Reward Shaping in Reinforcement Learning
Barrier Functions Inspired Reward Shaping for Reinforcement Learning: A Novel Safety-Oriented Framework
Barrier Functions Inspired Reward Shaping enhances training efficiency and safety in RL.
Self-Adaptive Success Rate Based Reward Shaping for Reinforcement Learning in Sparse Reward Environments
This paper introduces SASR, a novel self-adaptive reward shaping method for reinforcement learning that leverages success rates derived from historical experience to enhance learning in environments with sparse rewards.
ORSO: Using Online Reward Selection and Policy Optimization to Accelerate Reward Design in Reinforcement Learning
ORSO is a novel approach that accelerates reward design in reinforcement learning by framing it as an online model selection problem, efficiently identifying effective shaping reward functions without human intervention.
