Reinforcement Learning with Immediate Rewards and Linear Hypotheses

@article{Abe2003ReinforcementLW,
  title={Reinforcement Learning with Immediate Rewards and Linear Hypotheses},
  author={Naoki Abe and Alan W. Biermann and Philip M. Long},
  journal={Algorithmica},
  year={2003},
  volume={37},
  pages={263-293}
}
We consider the design and analysis of algorithms that learn from the consequences of their actions with the goal of maximizing their cumulative reward, when the consequence of a given action is felt immediately, and a linear function, which is unknown a priori, (approximately) relates a feature vector for each action/state pair to the (expected) associated reward. We focus on two cases, one in which a continuous-valued reward is (approximately) given by applying the unknown linear function… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.

72 Citations

051015'07'10'13'16'19
Citations per Year
Semantic Scholar estimates that this publication has 72 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…