Deep Q-Network
DQN approximates Q(s, a) with a deep net, using experience replay and a frozen target network so the TD target does not chase itself every step.
Definition
Deep Q-Network refers to dQN approximates Q(s, a) with a deep net, using experience replay and a frozen target network so the TD target does not chase itself every step. Keep that definition fixed when comparing series, managers, or regimes — renaming the same tape does not create a new signal.
Why it matters
It binds model output to retrieval, tools, or evaluation so answers stay grounded instead of free-floating. When dQN approximates Q(s, a) with a deep net, using experience replay and a frozen target network so the TD target does not chase itself every step shifts, related hedges, limits, and narratives usually need an explicit update rather than a quiet assumption.
Case
Suppose a desk is positioned for the opposite of what deep q-network is saying. If dQN approximates Q(s, a) with a deep net, using experience replay and a frozen target network so the TD target does not chase itself every step moves against that book, the first question is not “is the story clever?” but whether size, hedges, and stop logic still match the observation.
How to read it
Measure grounding rate, latency, and failure modes under missing context — not demo chat quality alone. Prefer a short written null hypothesis for Deep Q-Network: what would falsify the current reading in the next window?