arXiv Artificial Intelligence

Q-Shaped Options for Hierarchical Reinforcement Learning

1 hour ago 09.10.2026 07:00 👁 1 views
Q-Shaped Options for Hierarchical Reinforcement Learning

Quick summary

arXiv:2610.12135v1 Announce Type: new Abstract: Learning to tackle long-horizon, goal-conditioned tasks requires an agent to reason over extended timescales and act across a broad range of states. In principle, Hierarchical Reinforcement Learning (HRL) addresses both challenges through the interaction between action (temporal) and state (spatial) abstraction. First, using an action abstraction to represent temporally extended behaviour as options reduces the effective decision horizon. Second, enabling different state abstractions at each level of the decision process permits greater data aggr

Key takeaways

  • arXiv:2610.12135v1 Announce Type: new Abstract: Learning to tackle long-horizon, goal-conditioned tasks requires an agent to reason over extended timescales and act across a broad range of states.
  • In principle, Hierarchical Reinforcement Learning (HRL) addresses both challenges through the interaction between action (temporal) and state (spatial) abstraction.
  • First, using an action abstraction to represent temporally extended behaviour as options reduces the effective decision horizon.

Why it matters

The importance of “Q-Shaped Options for Hierarchical Reinforcement Learning” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗