A starting point.
Double Q-learning uses two value estimates to reduce the tendency of standard Q-learning to overestimate action values, and combines this with deep networks for learning from high-dimensional observations.
What to keep in mind
Read the original paper for the experimental setting, baselines, and limitations; a result in one setting is not a guarantee of performance elsewhere.
This work is included in a researcher’s reading path. A detailed editorial explanation is still being prepared. The complete manuscript is available in Full paper.
Source: Deep Reinforcement Learning with Double Q-learning. The original manuscript contains the methods, experiments, figures, and references. An arXiv posting date may follow an earlier conference publication. Read the linked record for version history.