From Approximate Q-learning to DQN | CMU 07-280 Lecture 23
Lecture 23 replaces a huge Q-table with Qθ(s,a): first derive a gradient update for linear features from squared TD error, then add replay data and a fixed target network to form DQN.
Lecture 23 replaces a huge Q-table with Qθ(s,a): first derive a gradient update for linear features from squared TD error, then add replay data and a fixed target network to form DQN.