Denis Steckelmacher

An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments

2015 · arXiv (Cornell University)

This paper explores the performance of fitted neural Q iteration for reinforcement learning in several partially observable environments, using three recurrent neural network architectures: Long Short-Term Memory, Gated Recurrent Unit and MUT1, a recurrent neural …