Search type Search syntax
Tags [tag]
Exact "words here"
Author user:1234
user:me (yours)
Score score:3 (3+)
score:0 (none)
Answers answers:3 (3+)
answers:0 (none)
isaccepted:yes
hasaccepted:no
inquestion:1234
Views views:250
Code code:"if (foo != bar)"
Sections title:apples
body:"apples oranges"
URL url:"*.example.com"
Bookmarks inbookmarks:mine
inbookmarks:1234
Status closed:yes
duplicate:no
migrated:no
wiki:no
Types is:question
is:answer
Exclude -[tag]
-apples
For more details on advanced search visit our help page
Results tagged with
Search options user 37382

For questions related to the deep Q-network (DQN), which is a deep neural network (e.g. a convolutional neural network) trained with a variant of Q-learning. The expression was coined in the paper "Playing Atari with Deep Reinforcement Learning" (2013) by Google's DeepMind.

3
votes
1answer
I made a DQN that controls a traffic light. The observation states are the number of vehicles of each lane in the intersection. I trained it for 500 episodes and saved the model every 50th episode. … Does it mean that the optimized DQN model is the 450th model? If not, how do I know if the my DQN is really optimized? …
asked Jun 20 '20 by Kevin
1
vote
0answers
I made a DQN model and plot its reward curve. You can see intuitively that the curve already converged since its reward value now just oscillates. … How can I show confidence that my DQN already reached its optimal other than by just showing the curve? Are there any way to validate that it is already optimized? …
asked Jun 21 '20 by Kevin
0
votes
1answer
My DQN model outputs the best traffic light state in an intersection. I used different values of batch size and learning rate to find the best model. …
asked Jun 21 '20 by Kevin