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    This study introduces a novel method for hierarchical reinforcement learning that simultaneously explores options and actions. By maximizing reward with policy entropies, the approach enhances learning efficiency and robustness in complex tasks.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Reinforcement Learning

    Background:

    • Hierarchical tasks require learning temporal abstractions through options.
    • Simultaneously exploring options and actions in hierarchical reinforcement learning remains a challenge.

    Purpose of the Study:

    • To address the challenge of simultaneous option and action exploration in hierarchical tasks.
    • To propose a novel optimization objective and algorithm for enhanced options learning.

    Main Methods:

    • Reformulated options learning from a probabilistic inference perspective.
    • Introduced a soft options iteration method for convergence.
    • Developed an off-policy algorithm named Maximum-Entropy Options Critic (MEOC).

    Main Results:

    • MEOC outperforms baseline methods in efficiency and final performance on continuous control benchmarks.
    • The method demonstrates superior and robust performance, particularly on complex tasks.
    • Ablation studies confirm that entropy maximization promotes efficient options specialization and action multimodality.

    Conclusions:

    • Maximizing reward augmented with policy entropies is effective for hierarchical reinforcement learning.
    • The proposed MEOC algorithm offers an efficient and robust solution for learning options and actions.
    • Entropy maximization is crucial for hierarchical exploration, leading to better learning performance.