freeCodeCamp/curriculum/challenges/english/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning-part-2.md
Oliver Eyton-Williams ee1e8abd87
feat(curriculum): restore seed + solution to Chinese (#40683)
* feat(tools): add seed/solution restore script

* chore(curriculum): remove empty sections' markers

* chore(curriculum): add seed + solution to Chinese

* chore: remove old formatter

* fix: update getChallenges

parse translated challenges separately, without reference to the source

* chore(curriculum): add dashedName to English

* chore(curriculum): add dashedName to Chinese

* refactor: remove unused challenge property 'name'

* fix: relax dashedName requirement

* fix: stray tag

Remove stray `pre` tag from challenge file.

Signed-off-by: nhcarrigan <nhcarrigan@gmail.com>

Co-authored-by: nhcarrigan <nhcarrigan@gmail.com>
2021-01-12 19:31:00 -07:00

588 B

id, title, challengeType, videoId, dashedName
id title challengeType videoId dashedName
5e8f2f13c4cdbe86b5c72da4 Reinforcement Learning With Q-Learning: Part 2 11 DX7hJuaUZ7o reinforcement-learning-with-q-learning-part-2

--question--

--text--

What can happen if the agent does not have a good balance of taking random actions and using learned actions?

--answers--

The agent will always try to minimize its reward for the current state/action, leading to local minima.


The agent will always try to maximize its reward for the current state/action, leading to local maxima.

--video-solution--

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