Peter Vrancx

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 …

Learn to Learn on Chip: Hardware-aware Meta-learning for Quantized Few-shot Learning at the Edge

2022

Recent years have seen a growing trend of deploying deep neural network-based applications on edge devices. Many of these applications, such as biometric identification, activity tracking, user preference learning, etc., require fine-tuning of the trained …