arXiv Artificial Intelligence

ETHER: Aligning Emergent Communication for Hindsight Experience Replay

1 hour ago 01.10.2026 07:00 👁 1 views
ETHER: Aligning Emergent Communication for Hindsight Experience Replay

Quick summary

arXiv:2307.15494v3 Announce Type: replace-cross Abstract: Hindsight Experience Replay (HER) enhances sample efficiency in goal-conditioned reinforcement learning (RL) by relabelling failed trajectories with goals that were actually achieved. However, HER assumes access to a goal relabelling function and a predicate function that determines whether a goal has been satisfied. These assumptions break down in instruction-following tasks, where goals are expressed in natural language and differ from the state space. We formalize this as the Hindsight Reinforcement Learning problem, which shows the

Key takeaways

  • arXiv:2307.15494v3 Announce Type: replace-cross Abstract: Hindsight Experience Replay (HER) enhances sample efficiency in goal-conditioned reinforcement learning (RL) by relabelling failed trajectories with goals that were actually achieved.
  • However, HER assumes access to a goal relabelling function and a predicate function that determines whether a goal has been satisfied.
  • These assumptions break down in instruction-following tasks, where goals are expressed in natural language and differ from the state space.

Why it matters

“ETHER: Aligning Emergent Communication for Hindsight Experience Replay” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗