60 citations · 60 across the 1 of their papers we have counts for
2 papers
cs.LG2019★ 60 cited
Towards Characterizing Divergence in Deep Q-Learning
Joshua Achiam, Ethan Knight, Pieter Abbeel
Deep Q-Learning (DQL), a family of temporal difference algorithms for control, employs three techniques collectively known as the `deadly triad' in reinforcement learning: bootstra…
cs.LG2018
Natural Gradient Deep Q-learning
Ethan Knight, Osher Lerner
We present a novel algorithm to train a deep Q-learning agent using natural-gradient techniques. We compare the original deep Q-network (DQN) algorithm to its natural-gradient coun…