Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantization

arXiv:2605.30928 · cs.RO · Submitted 2026-05-29 · Read on arXiv

cs.RO

Submitted: 2026-05-29

Updated: 2026-09-16

Code: https://github.com/rlglab/maq

Terminology

• Quantization — 40× in this paper · explained in 12 episodes
• Reinforcement Learning — 22× in this paper · explained in 4 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Action Space — 4× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 4× in this paper · explained in 2 episodes
• Actor — 3× in this paper · explained in 3 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Q-learning — 3× in this paper · explained in 3 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Wasserstein Distance — 2× in this paper · explained in 4 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Action Primitives — 1× in this paper · explained in 1 episode
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sim-to-Real Gap — 1× in this paper · explained in 6 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Temporal Difference Learning for Model Predictive Control
• End to End Learning for Self-Driving Cars
• Imitating Human Behaviour with Diffusion Models
• Flow Q-Learning
• Deep Reinforcement Learning With Macro-Actions
• Auto-Encoding Variational Bayes
• Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

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