Safe Learning Under Irreversible Dynamics via Asking for Help
Quick summary
arXiv:2502.14043v3 Announce Type: replace-cross Abstract: Most learning algorithms with formal regret guarantees essentially rely on trying all possible behaviors, which is problematic when some errors cannot be recovered from. Instead, we allow the learning agent to ask for help from a mentor and to transfer knowledge between similar states. We show that this combination enables the agent to learn both safely and effectively. Under standard online learning assumptions, we provide an algorithm whose regret and number of mentor queries are both sublinear in the time horizon for Markov decision
Key takeaways
- arXiv:2502.14043v3 Announce Type: replace-cross Abstract: Most learning algorithms with formal regret guarantees essentially rely on trying all possible behaviors, which is problematic when some errors cannot be recovered from.
- Instead, we allow the learning agent to ask for help from a mentor and to transfer knowledge between similar states.
- We show that this combination enables the agent to learn both safely and effectively.
Why it matters
The importance of “Safe Learning Under Irreversible Dynamics via Asking for Help” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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