Active Reinforcement Learning: Observing Rewards at a Cost. Krueger, D, Leike, J, Evans, O, & Salvatier, J filmnips.com. abstract bibtex Abstract Active reinforcement learning (ARL) is a variant on reinforcement learning where the agent does not observe the reward unless it chooses to pay a query cost c> 0. The central question of ARL is how to quantify the long-term value of reward information. Even in multi-armed bandits, computing the value of this information is intractable and we have to rely on heuristics. We propose and evaluate several heuristic approaches for ARL in multi-.
@Article{Krueger,
author = {Krueger, D and Leike, J and Evans, O and Salvatier, J},
title = {Active Reinforcement Learning: Observing Rewards at a Cost},
journal = {filmnips.com},
volume = {},
number = {},
pages = {},
year = {},
abstract = {Abstract Active reinforcement learning (ARL) is a variant on reinforcement learning where the agent does not observe the reward unless it chooses to pay a query cost c\> 0. The central question of ARL is how to quantify the long-term value of reward information. Even in multi-armed bandits, computing the value of this information is intractable and we have to rely on heuristics. We propose and evaluate several heuristic approaches for ARL in multi-.},
location = {},
keywords = {}}
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