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William J. Curran

1 paper hereh-index 7106 citations19 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1

Across the 1 of 1 paper where every author was matched, so the position is known.

fields
  • cs.LG1

identity via Semantic Scholar / OpenAlex

most citedUsing PCA to Efficiently Represent State Spaces

14 citations · 14 across the 1 of their papers we have counts for

collaborators

1 paper

cs.LG2015★ 14 cited

Using PCA to Efficiently Represent State Spaces

William Curran, Tim Brys, Matthew Taylor +1

Reinforcement learning algorithms need to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces. This is known as the cur…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.