223 citations · 267 across the 16 of their papers we have counts for
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cs.NE2015★ 2 cited
An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments
Denis Steckelmacher, Peter Vrancx
This paper explores the performance of fitted neural Q iteration for reinforcement learning in several partially observable environments, using three recurrent neural network archi…
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…