Q-network (aka Deep Q-network) Input: State (Env+Agent), Action Output: Single neuron Q-value (current reward and future return) Characteristics: Find max action and train to that label=q(s,a) Closely linked with Q-learning concept Explore: Random Exploit: Use q-network at current moment to exploit for max action through max q-value Training: Bellman target q-value Update q-value 1 by 1, q = r + max(q[t+1]) Policy Network (applicable to fixed number of actions ...