To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning

@inproceedings{Mahadevan1994ToDO,
  title={To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning},
  author={Sridhar Mahadevan},
  booktitle={ICML},
  year={1994}
}
Most work in reinforcement learning (RL) is based on discounted techniques, such as Q learning, where long-term rewards are geometrically attenuated based on the delay in their occurence. Schwartz recently proposed an undiscounted RL technique called R learning that optimizes average reward, and argued that it was a better metric than the discounted one optimized by Q learning. In this paper we compare R learning with Q learning on a simulated robot box-pushing task. We compare these two… CONTINUE READING
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