Search Results
| 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" |
| Saves | in:saves |
| 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 dqn
Search options not deleted
user 78383
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.
2
votes
0
answers
147
views
Why does only Deep Q Learning have an overestimation bias?
This is why methods like Double DQN and TD3 were created.
But what I don't understand is, is it not true that every temporal difference estimation has an overestimation bias? …
- 131
1
vote
0
answers
36
views
Why slow-changing policy invalidates Double DQN approach in TD3 paper?
effectively address the Q-learning overestimation bias by using different networks for maximizing and estimating the next state Q value when estimating the target Q, even though the idea worked in the Double DQN …
- 131