# Reinforcementlearning.jjl

**URL:** <https://discourse.julialang.org/t/reinforcementlearning-jjl/113948>\
**Category:** General Usage\
**Created:** [May 7, 2024, 2:36pm UTC](https://discourse.julialang.org/t/reinforcementlearning-jjl/113948 "2024-05-07T14:36:16Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![ttidrick](https://avatars.discourse-cdn.com/v4/letter/t/90db22/32.png) [@ttidrick](https://discourse.julialang.org/u/ttidrick)\
**Post date:** [May 7, 2024, 2:36pm UTC](https://discourse.julialang.org/t/reinforcementlearning-jjl/113948/1 "2024-05-07T14:36:16Z")

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Hi All,  
I’m curious if anyone has opinions, comments, heads-ups, etc. concerning the package ‘ReinforcementLearning.jl’.  
Thanks,  
Ty Tidrick

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**Author:** ![baggepinnen](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/baggepinnen/32/693_2.png) [@baggepinnen](https://discourse.julialang.org/u/baggepinnen)\
**Post date:** [May 7, 2024, 3:07pm UTC](https://discourse.julialang.org/t/reinforcementlearning-jjl/113948/2 "2024-05-07T15:07:47Z")

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The issue list in the repository is a kind of “heads-up”

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**Author:** ![WuSiren](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/wusiren/32/42529_2.png) [@WuSiren](https://discourse.julialang.org/u/WuSiren)\
**Post date:** [May 7, 2024, 7:02pm UTC](https://discourse.julialang.org/t/reinforcementlearning-jjl/113948/3 "2024-05-07T19:02:37Z")

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I tried this package more than one year ago and felt its then documentation was not as friendly as at least I a newbie wish. I’ve asked a question here at that time and still been waiting for the first response so far, though I’ve almost forgotten what I was doing then. Maybe the question is too trivial? 🥴

> [@How to perform an Off-Policy reinforcement learning using \`ReinforcementLearning.jl\`?](https://discourse.julialang.org/t/how-to-perform-an-off-policy-reinforcement-learning-using-reinforcementlearning-jl/99940):
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> I am a beginner of both Julia and RL. Now I’m trying to training a Q-based tabular policy with off-policy strategy using ReinforcementLearning.jl. Let behavior\_policy be a RandomPolicy (for example) to collect experiences, I want the target\_policy as a QBasedPolicy to be trained. I wrote the following code: target\_policy = QBasedPolicy( learner = MonteCarloLearner(; approximator=TabularQApproximator( ;n\_state = length(state\_space(wrapped\_env)), n\_action = len…

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**Author:** ![colintbowers](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/colintbowers/32/8033_2.png) [@colintbowers](https://discourse.julialang.org/u/colintbowers)\
**Post date:** [May 8, 2024, 6:31am UTC](https://discourse.julialang.org/t/reinforcementlearning-jjl/113948/4 "2024-05-08T06:31:52Z")

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I’m not an expert by any means, but I believe I read somewhere that they are currently doing a fairly extensive re-write of a lot of the package, and that consequently, if you find any bugs or issues in the existing package, you’re likely stuck with them until the current refactoring is done.