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ENTITY Hierarchical reinforcement learning and decision making

Hierarchical reinforcement learning and decision making

PulseAugur coverage of Hierarchical reinforcement learning and decision making — every cluster mentioning Hierarchical reinforcement learning and decision making across labs, papers, and developer communities, ranked by signal.

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RECENT · PAGE 1/1 · 6 TOTAL
  1. TOOL · CL_218141 ·

    New hierarchical RL framework enhances conversational agents

    Researchers have developed a novel two-level hierarchical reinforcement learning (RL) framework called ToSCA for conversational agents. This approach bridges the gap between existing token-level or utterance-level RL me…

  2. TOOL · CL_167727 ·

    New HRL Model Uses Flow-Based Generative Models for Enhanced Training

    Researchers have developed a novel Hierarchical Reinforcement Learning (HRL) model that utilizes a Flow-based Deep Generative Model (FDGM) for improved training efficiency. This new approach enables direct off-policy co…

  3. RESEARCH · CL_154008 ·

    New research explores reinforcement learning advancements across multiple domains · 10 sources tracked

    Multiple research papers published on arXiv explore advancements in reinforcement learning (RL) and its applications. One study focuses on improving the interpretability of RL policies through decision-tree pruning, dem…

  4. TOOL · CL_104695 ·

    New method enhances safety in hierarchical reinforcement learning tasks

    Researchers have developed a novel method to enhance safety in hierarchical reinforcement learning, particularly for complex, long-horizon tasks. The approach utilizes a learned world model combined with a high-level po…

  5. TOOL · CL_53649 ·

    New Algorithm CARL Enhances Skill Reusability in Hierarchical RL

    Researchers have developed a new algorithm called CARL (Contrastive Action-based Representations for Reusable Local Control) to improve the reusability of skills in Hierarchical Reinforcement Learning (HRL). CARL exploi…

  6. RESEARCH · CL_18363 ·

    Quantum circuits enhance hierarchical reinforcement learning agents, saving parameters

    Researchers have developed a hybrid hierarchical reinforcement learning agent that integrates variational quantum circuits into its architecture. This approach substitutes classical components with quantum circuits for …