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 convergence
Search options answers only
not deleted
user 1847
For questions related to the convergence of AI algorithms.
5
votes
Accepted
If deep Q-learning starts to choose only one action, is this a sign that the algorithm diver...
Is this a sign that the algorithm diverged?
It is a common sign of a problem with learning process. That includes divergence due to poor hyper-parameters, even just bad luck. But it can also poin …
- 33.3k
1
vote
Accepted
Do I really need to do policy evaluation until convergence in policy iteration?
I don't understand why policy evaluation needs to be done until convergence
It doesn't need to be, although the resulting algorithm if you cut short of convergence is not strictly policy iteration as … Proofs of convergence for other methods that are based on GPI are harder (e.g. for Q-learning) or still open and unproven (e.g. for Monte Carlo control). …
- 33.3k
5
votes
Accepted
If $\alpha$ decreases over time, why is Q-learning guaranteed to converge?
Why is this a convergence criterion?
It is because $R$ and $S'$ are stochastic. … For deterministic environments, it should be possible to prove convergence with large $\alpha$. …
- 33.3k
3
votes
Accepted
Is a calculus or ML approach to varying learning rate as a function of loss and epoch been i...
It is likely that such a function exists, of ideal learning rate for best expected convergence per epoch. …
- 33.3k
1
vote
Accepted
Is it possible learning convergence is lost in Reinforcement Learning as the state space grows?
The convergence time for RL methods is highly variable, and depends on many factors. … to get convergence on the larger instance. …
- 33.3k
4
votes
Accepted
Is there an advantage in decaying $\epsilon$ during Q-Learning?
Yes Q-learning benefits from decaying epsilon in at least two ways:
Early exploration. It makes little sense to follow whatever policy is implied by the initialised network closely, and more will be …
- 33.3k