Robust high performance reinforcement learning through weighted k-nearest neighbors

By J. Martín H., J. de Lope, D. Maravall. 2011

33 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati
4. Guoqi Ren

In Electrical and Electronic Engineering

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “ary efforts in investigating a variety of RL methods to find optimal scheduling algorithms for radiation therapy, including the exhaustive PS [20], FQI [40], SARSA() [19] and K-Nearest NeighborsTD() [107]. The preliminary findings suggest that there may be an advantage in using non-uniform fractionati”