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This research introduces HiMAQ, a hierarchical vector quantization framework designed to make reinforcement learning agents behave more like humans by clustering action sequences into macro actions. By aligning agent behaviors with human demonstrations while maintaining high reward optimization, the method improves interpretability and reliability across standard RL algorithms such as IQL and SAC. The work represents a significant step forward in bridging the gap between purely reward-driven systems and those that exhibit naturalistic, human-like decision-making patterns.
Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantization
arXiv:2605.30928v2 Announce Type: replace 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. Specifically, we encode human demonstrations into macro actions using a hierarchical macro action quantization approach (HiMAQ) consisting of two successive levels of vector quantization. The lower quantization level maps input actions to fine-grained subaction clusters, while the higher quantization level aggregates these subaction clusters into action clusters. Extensive evaluations on the D4RL benchmarks show that our hierarchical approach outperforms the non-hierarchical baseline (MAQ), achieving higher human-likeness scores and better success rates than previous RL agents. The improvements generalize across integrations with various RL algorithms, namely IQL, SAC, and RLPD.
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. Specifically, we encode human demonstrations into macro actions using a hierarchical macro action quantization approach (HiMAQ) consisting of two successive levels of vector quantization. The lower quantization level maps input actions to fine-grained subaction clusters, while the higher quantization level aggregates these subaction clusters into action clusters. Extensive evaluations on the D4RL benchmarks show that our hierarchical approach outperforms the non-hierarchical baseline (MAQ), achieving higher human-likeness scores and better success rates than previous RL agents. The improvements generalize across integrations with various RL algorithms, namely IQL, SAC, and RLPD.
Robotics (cs.RO)
arXiv:2605.30928 [cs.RO]
(or arXiv:2605.30928v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.30928 Focus to learn more arXiv-issued DOI via DataCite
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