No AI summary available for this article.
Why It Matters
Meta-reinforcement learning (meta-RL) enables agents to adapt to unseen tasks with limited experience.
Provenance
Discovered via ArXiv and published by ArXiv.
Key Claims
Original description
Meta-reinforcement learning (meta-RL) enables agents to adapt to unseen tasks with limited experience. Despite its promise, the application of meta-RL in real-world tasks is hindered by safety requirements, which have been underexplored in prior work. In this paper, we propose a safe meta-RL framework that explicitly accounts for safety during adaptation. Our key insight is to reason about safety in the information space, which captures both the physical state and the agent's belief over the underlying task. Within this space, we introduce a safety value function that measures the probability...
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2609.15915v1 · Indexed about 1 hour ago