Active Reinforcement Learning : Observing Rewards at a Cost

@inproceedings{Krueger2016ActiveRL,
  title={Active Reinforcement Learning : Observing Rewards at a Cost},
  author={David Krueger},
  year={2016}
}
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-armed bandits and (tabular) Markov decision processes… CONTINUE READING