14 citations · 18 across the 2 of their papers we have counts for
2 papers
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…
cs.AI2015★ 4 cited
Off-Policy Reward Shaping with Ensembles
Anna Harutyunyan, Tim Brys, Peter Vrancx +1
Potential-based reward shaping (PBRS) is an effective and popular technique to speed up reinforcement learning by leveraging domain knowledge. While PBRS is proven to always preser…