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Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantization
arXiv:2605.30928v1 Announce Type: new Abstract: Human-like agents are a long-standing goal of artificial intelligence. Despite strong performance, most reinforcement learning (RL) agents remain reward-driven and often exhibit behaviors that differ from humans, limiting interpretability and reliability. In this work, we introduce a novel human-like RL framework that predicts action sequences closely aligned with human behaviors while maximizing rewards.